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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
```python
import sympy as sm
import sympy.physics.mechanics as me
sm.init_printing()
```
In the video I incorrectly typed `q1, q2, q3, q4 = sm.symbols('q1:5')`. It is corrected below:
```python
q1, q2, q3, q4, q5 = sm.symbols('q1:6')
l1, l2, l3, l4 = sm.symbols('l1:5')
```
```python
N, A, B, C = sm.symbols('N, A, ... | 71dfa8ed9297a742b2160d917ad6f0e8ba3bfb87 | 3,806 | ipynb | Jupyter Notebook | content/notebooks/vectors.ipynb | moorepants/me41055 | 5c941d28f6f61fa1a02fbda4772d88010014eec0 | [
"CC-BY-4.0"
] | null | null | null | content/notebooks/vectors.ipynb | moorepants/me41055 | 5c941d28f6f61fa1a02fbda4772d88010014eec0 | [
"CC-BY-4.0"
] | 42 | 2022-01-07T18:05:36.000Z | 2022-03-22T15:15:01.000Z | content/notebooks/vectors.ipynb | moorepants/me41055 | 5c941d28f6f61fa1a02fbda4772d88010014eec0 | [
"CC-BY-4.0"
] | 1 | 2022-01-24T17:18:40.000Z | 2022-01-24T17:18:40.000Z | 18.298077 | 107 | 0.492906 | true | 434 | Qwen/Qwen-72B | 1. YES
2. YES | 0.933431 | 0.870597 | 0.812642 | __label__eng_Latn | 0.359914 | 0.726373 |
```python
# 그래프, 수학 기능 추가
# Add graph and math features
import pylab as py
import numpy as np
import numpy.linalg as nl
# 기호 연산 기능 추가
# Add symbolic operation capability
import sympy as sy
```
```python
sy.init_printing()
```
# 2차 적분<br>Second Order Numerical Integral
다시 면적 1인 반원을 생각해 보자.<br>
Again, let's thi... | ea1fefed76200584240846bbf4f413722a724bc1 | 22,588 | ipynb | Jupyter Notebook | 20_second_order.ipynb | kangwonlee/19ECA-30-num-int | 20f45cb6cde958ebb901341a0253abfb95efe92d | [
"BSD-3-Clause"
] | null | null | null | 20_second_order.ipynb | kangwonlee/19ECA-30-num-int | 20f45cb6cde958ebb901341a0253abfb95efe92d | [
"BSD-3-Clause"
] | null | null | null | 20_second_order.ipynb | kangwonlee/19ECA-30-num-int | 20f45cb6cde958ebb901341a0253abfb95efe92d | [
"BSD-3-Clause"
] | null | null | null | 22.701508 | 298 | 0.484771 | true | 4,225 | Qwen/Qwen-72B | 1. YES
2. YES | 0.810479 | 0.73412 | 0.594988 | __label__kor_Hang | 0.501574 | 0.220688 |
# Cerebellar Model Articulation Controller(CMAC)
## 概论
小脑模型的初始设想很简单，希望设计一个这样的模型：
1. **足够快**
2. **拟合**
### 足够快
对于第一点，传统的神经网络均是使用浮点数进行计算，使用浮点数显然存在两个缺点：其一是占用空间大，其二是运算不够快。但也存在非常明显的优点：计算精度高。
若为了在不太降低精度的条件下尽可能提高模型运算效率，显然有两个角度：其一是改进模型，其二是改变数值存储方式。
量化技术就是这样一类通过改变数值存储方式提高模型运算效率的方式。对于现代神经网络，训练通常采用32位浮点数，推理时则可以选用16位浮点数以提高精度... | ef52c4fbcbdc0853ba4bd8dd5788227bac118ac3 | 522,139 | ipynb | Jupyter Notebook | CMAC.ipynb | koolo233/NeuralNetworks | 39e532646ada8f1e821e0b6e3565379c1f73126c | [
"MIT"
] | null | null | null | CMAC.ipynb | koolo233/NeuralNetworks | 39e532646ada8f1e821e0b6e3565379c1f73126c | [
"MIT"
] | null | null | null | CMAC.ipynb | koolo233/NeuralNetworks | 39e532646ada8f1e821e0b6e3565379c1f73126c | [
"MIT"
] | null | null | null | 647.012392 | 464,826 | 0.941529 | true | 7,693 | Qwen/Qwen-72B | 1. YES
2. YES | 0.847968 | 0.672332 | 0.570116 | __label__yue_Hant | 0.06908 | 0.162899 |
#Snippets and Programs from Chapter 4: Algebra and Symbolic Math with SymPy
```python
%matplotlib inline
```
```python
#P96/97: Basic factorization and expansion
from sympy import Symbol, factor, expand
x = Symbol('x')
y = Symbol('y')
expr = x**2 - y**2
f = factor(expr)
print(f)
# Expand
print(expand(f))
```
```p... | 6075efe3ee36ffd05522e9f499072db93c58a992 | 72,623 | ipynb | Jupyter Notebook | chapter4/Chapter4.ipynb | hexu1985/Doing.Math.With.Python | b6a02805cd450325e794a49f55d2d511f9db15a5 | [
"MIT"
] | 109 | 2015-08-28T10:23:24.000Z | 2022-02-15T01:39:51.000Z | chapter4/Chapter4.ipynb | hexu1985/Doing.Math.With.Python | b6a02805cd450325e794a49f55d2d511f9db15a5 | [
"MIT"
] | 6 | 2015-12-07T19:35:30.000Z | 2021-05-01T07:25:42.000Z | chapter4/Chapter4.ipynb | hexu1985/Doing.Math.With.Python | b6a02805cd450325e794a49f55d2d511f9db15a5 | [
"MIT"
] | 74 | 2015-10-15T18:09:15.000Z | 2022-01-30T05:06:21.000Z | 115.274603 | 14,682 | 0.858447 | true | 2,128 | Qwen/Qwen-72B | 1. YES
2. YES | 0.951863 | 0.903294 | 0.859813 | __label__eng_Latn | 0.768788 | 0.835966 |
### Example 5: Laplace equation
In this tutorial we will look constructing the steady-state heat example using the Laplace equation. In contrast to the previous tutorials this example is entirely driven by the prescribed Dirichlet and Neumann boundary conditions, instead of an initial condition. We will also demonstra... | 58d49f68b1cf1959c1c419377e0bc05376f4463b | 608,087 | ipynb | Jupyter Notebook | examples/cfd/05_laplace.ipynb | RajatRasal/devito | 162abb6b318e77eaa4e8f719047327c45782056f | [
"MIT"
] | null | null | null | examples/cfd/05_laplace.ipynb | RajatRasal/devito | 162abb6b318e77eaa4e8f719047327c45782056f | [
"MIT"
] | null | null | null | examples/cfd/05_laplace.ipynb | RajatRasal/devito | 162abb6b318e77eaa4e8f719047327c45782056f | [
"MIT"
] | null | null | null | 1,604.451187 | 108,748 | 0.958851 | true | 2,685 | Qwen/Qwen-72B | 1. YES
2. YES | 0.833325 | 0.851953 | 0.709953 | __label__eng_Latn | 0.99292 | 0.487791 |
```python
# import packages
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sympy import *
from scipy.optimize import fsolve
%matplotlib inline
```
```python
# set up constants
length = 25 #(nm)
dx = length/12 #(nm)
dt = 0.016667*332/12 #(s)
M = 2.75*10**(-15) #(m mol/J s) it should be det... | ec92a4ee9953fc29c31f5de7aefcf172a55e7c31 | 28,995 | ipynb | Jupyter Notebook | ode_pve/GITT.ipynb | ode-pve/ODE_PVE | a19b26c2aa260820b8c0e51bae5f654c7de97ba0 | [
"MIT"
] | null | null | null | ode_pve/GITT.ipynb | ode-pve/ODE_PVE | a19b26c2aa260820b8c0e51bae5f654c7de97ba0 | [
"MIT"
] | null | null | null | ode_pve/GITT.ipynb | ode-pve/ODE_PVE | a19b26c2aa260820b8c0e51bae5f654c7de97ba0 | [
"MIT"
] | null | null | null | 123.382979 | 5,690 | 0.676806 | true | 934 | Qwen/Qwen-72B | 1. YES
2. YES | 0.903294 | 0.672332 | 0.607313 | __label__eng_Latn | 0.19601 | 0.249323 |
# Circuitos RL sem fonte
Jupyter Notebook desenvolvido por [Gustavo S.S.](https://github.com/GSimas)
Considere a conexão em série de um resistor e um indutor, conforme mostra a
Figura 7.11. Em t = 0, supomos que o indutor tenha uma
corrente inicial Io.
\begin{align}
I(0) = I_0
\end{align}
Assim, a energia correspon... | 3071f5ecf085e60fcec7e007e2cbe60326494c04 | 9,828 | ipynb | Jupyter Notebook | Aula 10 - Circuitos RL.ipynb | ofgod2/Circuitos-electricos-Boylestad-12ed-Portugues | 60e815f6904858f3cda8b5c7ead8ea77aa09c7fd | [
"MIT"
] | 7 | 2019-08-13T13:33:15.000Z | 2021-11-16T16:46:06.000Z | Aula 10 - Circuitos RL.ipynb | ofgod2/Circuitos-electricos-Boylestad-12ed-Portugues | 60e815f6904858f3cda8b5c7ead8ea77aa09c7fd | [
"MIT"
] | 1 | 2017-08-24T17:36:15.000Z | 2017-08-24T17:36:15.000Z | Aula 10 - Circuitos RL.ipynb | ofgod2/Circuitos-electricos-Boylestad-12ed-Portugues | 60e815f6904858f3cda8b5c7ead8ea77aa09c7fd | [
"MIT"
] | 8 | 2019-03-29T14:31:49.000Z | 2021-12-30T17:59:23.000Z | 23.625 | 117 | 0.43549 | true | 2,329 | Qwen/Qwen-72B | 1. YES
2. YES | 0.73412 | 0.746139 | 0.547755 | __label__por_Latn | 0.736297 | 0.110948 |
# Automation's impact on the economic growth.
## Importing modules
```python
import numpy as np
import scipy as sp
from scipy import linalg
from scipy import optimize
from scipy import interpolate
import sympy as sm
%matplotlib inline
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot... | 0d3f4f9b45a0e60f552b5a77420c38b39f75149c | 291,293 | ipynb | Jupyter Notebook | modelproject/modelproject-Final.ipynb | NumEconCopenhagen/projects-2019-ob4ever | d2027137e69e71f09a4a0fca7a597810cff08c0d | [
"MIT"
] | null | null | null | modelproject/modelproject-Final.ipynb | NumEconCopenhagen/projects-2019-ob4ever | d2027137e69e71f09a4a0fca7a597810cff08c0d | [
"MIT"
] | 8 | 2019-04-09T12:42:45.000Z | 2019-05-14T12:44:28.000Z | modelproject/modelproject-Final.ipynb | NumEconCopenhagen/projects-2019-ob4ever | d2027137e69e71f09a4a0fca7a597810cff08c0d | [
"MIT"
] | null | null | null | 288.981151 | 94,128 | 0.911385 | true | 7,151 | Qwen/Qwen-72B | 1. YES
2. YES | 0.746139 | 0.795658 | 0.593672 | __label__eng_Latn | 0.974298 | 0.217628 |
<p align="center">
</p>
## Interactive Variogram Calculation Demonstration
### Michael Pyrcz, Associate Professor, University of Texas at Austin
##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.g... | 45168b11a9f94894cf4be0231b95f390af138f6c | 298,185 | ipynb | Jupyter Notebook | Interactive_Variogram_Calculation.ipynb | caf3676/PythonNumericalDemos | 206a3d876f79e137af88b85ba98aff171e8d8e06 | [
"MIT"
] | 403 | 2017-10-15T02:07:38.000Z | 2022-03-30T15:27:14.000Z | Interactive_Variogram_Calculation.ipynb | caf3676/PythonNumericalDemos | 206a3d876f79e137af88b85ba98aff171e8d8e06 | [
"MIT"
] | 4 | 2019-08-21T10:35:09.000Z | 2021-02-04T04:57:13.000Z | Interactive_Variogram_Calculation.ipynb | caf3676/PythonNumericalDemos | 206a3d876f79e137af88b85ba98aff171e8d8e06 | [
"MIT"
] | 276 | 2018-06-27T11:20:30.000Z | 2022-03-25T16:04:24.000Z | 284.799427 | 195,280 | 0.902996 | true | 8,789 | Qwen/Qwen-72B | 1. YES
2. YES | 0.749087 | 0.740174 | 0.554455 | __label__eng_Latn | 0.914899 | 0.126515 |
# Introduction to `esys.escript`
## Outline
This unit gives an introduction into solving partial differential equations (PDEs) in python. This
section assumed that you have a basic understanding how to work with python.
We are particularly looking at PDEs as they arise in geophysical problems. Of course it would t... | b77641889f8c656151a7a10731025533ae405aaf | 46,254 | ipynb | Jupyter Notebook | B_GeophyicalModeling/EscriptBasics.ipynb | uqzzhao/Programming-Geophysics-in-Python | e6e8299116b4698892921b78927b71fc47ee018a | [
"Apache-2.0"
] | 20 | 2019-11-06T09:08:54.000Z | 2021-12-03T08:37:47.000Z | B_GeophyicalModeling/EscriptBasics.ipynb | uqzzhao/Programming-Geophysics-in-Python | e6e8299116b4698892921b78927b71fc47ee018a | [
"Apache-2.0"
] | null | null | null | B_GeophyicalModeling/EscriptBasics.ipynb | uqzzhao/Programming-Geophysics-in-Python | e6e8299116b4698892921b78927b71fc47ee018a | [
"Apache-2.0"
] | 3 | 2020-11-23T14:16:06.000Z | 2022-03-31T14:45:46.000Z | 33.493121 | 2,161 | 0.589095 | true | 6,534 | Qwen/Qwen-72B | 1. YES
2. YES | 0.851953 | 0.810479 | 0.69049 | __label__eng_Latn | 0.991697 | 0.44257 |
```python
from ngames.evaluation.extensivegames import ExtensiveFormGame, plot_game,\
subgame_perfect_equilibrium, DFS_equilibria_paths
```
# Market game
From S. Fatima, S. Kraus, M. Wooldridge, Principles of Automated Negotiation,
Cambridge University Press, 2014, see Figure 3.3.
```python
m = ExtensiveFormGam... | 7b79c09b321f801a5faf0a268e92e643cf06872c | 1,024,884 | ipynb | Jupyter Notebook | examples/examples.ipynb | nmontesg/norms-games | ee4d7ad4f3cc774020cd5617e6957e804995ef70 | [
"MIT"
] | 1 | 2021-07-22T14:28:31.000Z | 2021-07-22T14:28:31.000Z | examples/examples.ipynb | nmontesg/norms-games | ee4d7ad4f3cc774020cd5617e6957e804995ef70 | [
"MIT"
] | null | null | null | examples/examples.ipynb | nmontesg/norms-games | ee4d7ad4f3cc774020cd5617e6957e804995ef70 | [
"MIT"
] | null | null | null | 511.93007 | 290,553 | 0.939709 | true | 11,734 | Qwen/Qwen-72B | 1. YES
2. YES | 0.785309 | 0.774583 | 0.608287 | __label__eng_Latn | 0.696444 | 0.251585 |
```python
%pylab inline
```
Populating the interactive namespace from numpy and matplotlib
```python
from sympy import symbols, sympify, latex, integrate, solve, solveset, Matrix, expand, factor, primitive, simplify, factor_list
from sympy.parsing.sympy_parser import parse_expr
```
```python
M = Matrix(np.res... | f687a87231881be3e96d6972123f952bfb16cfb2 | 13,930 | ipynb | Jupyter Notebook | Calcupy/matrix heatmap.ipynb | darkeclipz/jupyter-notebooks | 5de784244ad9db12cfacbbec3053b11f10456d7e | [
"Unlicense"
] | 1 | 2018-08-28T12:16:12.000Z | 2018-08-28T12:16:12.000Z | Calcupy/matrix heatmap.ipynb | darkeclipz/jupyter-notebooks | 5de784244ad9db12cfacbbec3053b11f10456d7e | [
"Unlicense"
] | null | null | null | Calcupy/matrix heatmap.ipynb | darkeclipz/jupyter-notebooks | 5de784244ad9db12cfacbbec3053b11f10456d7e | [
"Unlicense"
] | null | null | null | 105.530303 | 10,240 | 0.872721 | true | 779 | Qwen/Qwen-72B | 1. YES
2. YES | 0.843895 | 0.7773 | 0.65596 | __label__yue_Hant | 0.139734 | 0.362345 |
# Confidence interval approximations for the AUROC
The area under the receiver operating curve (AUROC) is one of the most commonly used performance metrics for binary classification. Visually, the AUROC is the integral between the sensitivity and false positive rate curves across all thresholds for a binary classifier... | 81cb896f982194dccf9862437be257b80a403a45 | 266,867 | ipynb | Jupyter Notebook | _rmd/extra_AUC_CI/auc_sim.ipynb | erikdrysdale/erikdrysdale.github.io | ff337117e063be7f909bc2d1f3ff427781d29f31 | [
"MIT"
] | null | null | null | _rmd/extra_AUC_CI/auc_sim.ipynb | erikdrysdale/erikdrysdale.github.io | ff337117e063be7f909bc2d1f3ff427781d29f31 | [
"MIT"
] | null | null | null | _rmd/extra_AUC_CI/auc_sim.ipynb | erikdrysdale/erikdrysdale.github.io | ff337117e063be7f909bc2d1f3ff427781d29f31 | [
"MIT"
] | 2 | 2017-09-13T15:16:36.000Z | 2020-03-03T15:37:01.000Z | 376.930791 | 81,032 | 0.914246 | true | 8,504 | Qwen/Qwen-72B | 1. YES
2. YES | 0.888759 | 0.855851 | 0.760645 | __label__eng_Latn | 0.949725 | 0.605566 |
# Announcements
- __Please familiarize yourself with the term projects, and sign up for your (preliminary) choice__ using [this form](https://forms.gle/ByLLpsthrpjCcxG89). _You may revise your choice, but I'd recommend settling on a choice well before Thanksgiving._
- Recommended reading on ODEs: [Lecture notes by Prof... | c5c30683690b52430e96b1748434ecf58a5ed9b6 | 221,394 | ipynb | Jupyter Notebook | Lectures/Lecture 19/Lecture19_IntroLA.ipynb | astroarshn2000/PHYS305S20 | 18f4ebf0a51ba62fba34672cf76bd119d1db6f1e | [
"MIT"
] | 3 | 2020-09-10T06:45:46.000Z | 2020-10-20T13:50:11.000Z | Lectures/Lecture 19/Lecture19_IntroLA.ipynb | astroarshn2000/PHYS305S20 | 18f4ebf0a51ba62fba34672cf76bd119d1db6f1e | [
"MIT"
] | null | null | null | Lectures/Lecture 19/Lecture19_IntroLA.ipynb | astroarshn2000/PHYS305S20 | 18f4ebf0a51ba62fba34672cf76bd119d1db6f1e | [
"MIT"
] | null | null | null | 174.188828 | 42,264 | 0.865904 | true | 12,766 | Qwen/Qwen-72B | 1. YES
2. YES | 0.73412 | 0.79053 | 0.580344 | __label__eng_Latn | 0.968386 | 0.186663 |
# Class V - Conic modelling in JuMP
This notebook describes conic modelling in JuMP through a number of examples.
```julia
import Pkg
Pkg.activate(@__DIR__)
Pkg.instantiate()
```
[32m[1m Updating[22m[39m registry at `C:\Users\Oscar\.julia\registries\General`
[32m[1m Updating[22m[39m git-repo `http... | 90a953881a3fcf3406f30be6d5d491c51e6c0d21 | 403,725 | ipynb | Jupyter Notebook | Class V - Conic modelling in JuMP.ipynb | edgBR/tutorial-grid-science-2019 | c743684ad8e5693948629a680546243ed95a7e93 | [
"MIT"
] | null | null | null | Class V - Conic modelling in JuMP.ipynb | edgBR/tutorial-grid-science-2019 | c743684ad8e5693948629a680546243ed95a7e93 | [
"MIT"
] | null | null | null | Class V - Conic modelling in JuMP.ipynb | edgBR/tutorial-grid-science-2019 | c743684ad8e5693948629a680546243ed95a7e93 | [
"MIT"
] | 1 | 2020-09-03T18:53:00.000Z | 2020-09-03T18:53:00.000Z | 921.746575 | 300,275 | 0.684206 | true | 196,286 | Qwen/Qwen-72B | 1. YES
2. YES | 0.833325 | 0.863392 | 0.719485 | __label__yue_Hant | 0.126026 | 0.509937 |
# Lecture 24 - Sequential Monte Carlo in `PyMC3`
```python
import numpy as np
import pymc3 as pm
import theano as T
from theano import shared, function, tensor as tt
from sample_smc import sample_smc
try:
import sympy
except:
_=!pip install sympy
import sympy
import matplotlib.pyplot as plt
import sea... | a6d773aefd28776fa3a851cb7f12a7b25ace083f | 303,181 | ipynb | Jupyter Notebook | lectures/lecture_24.ipynb | PredictiveScienceLab/uq-course | ddbe0865c9f91c4bd9b12e9b85d4293168306438 | [
"MIT"
] | 218 | 2016-01-04T15:31:44.000Z | 2022-03-23T20:09:27.000Z | lectures/lecture_24.ipynb | ragusa/uq-course | ddbe0865c9f91c4bd9b12e9b85d4293168306438 | [
"MIT"
] | 2 | 2019-02-22T08:13:54.000Z | 2020-02-08T19:25:16.000Z | lectures/lecture_24.ipynb | ragusa/uq-course | ddbe0865c9f91c4bd9b12e9b85d4293168306438 | [
"MIT"
] | 112 | 2016-01-05T18:50:34.000Z | 2022-03-15T04:33:28.000Z | 171.579513 | 91,968 | 0.845957 | true | 24,043 | Qwen/Qwen-72B | 1. YES
2. YES | 0.872347 | 0.853913 | 0.744909 | __label__yue_Hant | 0.346496 | 0.569004 |
```python
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.set_context('notebook', font_scale=1.5)
```
The first exercise is about using Newton's method to find the cube roots of unity - find $z$ such that $z^3 = 1$. From the fundamental theorem of algebra, we know there ... | ca41e004b39b3084f7d97f22a31b4db361a25d82 | 306,334 | ipynb | Jupyter Notebook | homework/08_Optimization_Solutions.ipynb | cliburn/sta-663-2017 | 89e059dfff25a4aa427cdec5ded755ab456fbc16 | [
"MIT"
] | 52 | 2017-01-11T03:16:00.000Z | 2021-01-15T05:28:48.000Z | homework/08_Optimization_Solutions.ipynb | slimdt/Duke_Stat633_2017 | 89e059dfff25a4aa427cdec5ded755ab456fbc16 | [
"MIT"
] | 1 | 2017-04-16T17:10:49.000Z | 2017-04-16T19:13:03.000Z | homework/08_Optimization_Solutions.ipynb | slimdt/Duke_Stat633_2017 | 89e059dfff25a4aa427cdec5ded755ab456fbc16 | [
"MIT"
] | 47 | 2017-01-13T04:50:54.000Z | 2021-06-23T11:48:33.000Z | 330.101293 | 106,190 | 0.909409 | true | 5,315 | Qwen/Qwen-72B | 1. YES
2. YES | 0.76908 | 0.800692 | 0.615796 | __label__eng_Latn | 0.934802 | 0.269032 |
# Decaimiento radiactivo
Existen muchos modelos de decaimiento radiactivo, sin embargo, uno de los más sencillos es el considerar que la cantidad de material radioactivo decae de forma proporcional a la cantidad que tenga en un tiempo $t$. esto puede ser escrito de forma sencilla en el siguiente modelo:
$$\frac{\Delta... | a93c56792e3d12ff4fe46d0d2c99949fb9b89441 | 83,027 | ipynb | Jupyter Notebook | Seccion_1/Ejercicio_3 (1).ipynb | ComputoCienciasUniandes/FISI2029-201910 | 88909a78e562f8d5c61f3fd9178ed5f59f973945 | [
"MIT"
] | null | null | null | Seccion_1/Ejercicio_3 (1).ipynb | ComputoCienciasUniandes/FISI2029-201910 | 88909a78e562f8d5c61f3fd9178ed5f59f973945 | [
"MIT"
] | null | null | null | Seccion_1/Ejercicio_3 (1).ipynb | ComputoCienciasUniandes/FISI2029-201910 | 88909a78e562f8d5c61f3fd9178ed5f59f973945 | [
"MIT"
] | 5 | 2019-04-03T19:28:00.000Z | 2019-06-28T15:18:56.000Z | 189.127563 | 18,472 | 0.903706 | true | 1,928 | Qwen/Qwen-72B | 1. YES
2. YES | 0.861538 | 0.822189 | 0.708347 | __label__spa_Latn | 0.915461 | 0.48406 |
# Covariate Shift
A fundamental assumption in almost all [supervised learning](https://en.wikipedia.org/wiki/Supervised_learning) methods is that training and test samples are drawn from the same [probability distribution](https://en.wikipedia.org/wiki/Probability_distribution). However, in practice, this assumption i... | 5588c6ec970199986cf7d9fe5a16ba8640e5a613 | 21,023 | ipynb | Jupyter Notebook | nobel_physics_prizes/notebooks/5.1-covariate-shift.ipynb | covuworie/nobel-physics-prizes | f89a32cd6eb9bbc9119a231bffee89b177ae847a | [
"MIT"
] | 3 | 2019-08-21T05:35:42.000Z | 2020-10-08T21:28:51.000Z | nobel_physics_prizes/notebooks/5.1-covariate-shift.ipynb | covuworie/nobel-physics-prizes | f89a32cd6eb9bbc9119a231bffee89b177ae847a | [
"MIT"
] | 139 | 2018-09-01T23:15:59.000Z | 2021-02-02T22:01:39.000Z | nobel_physics_prizes/notebooks/5.1-covariate-shift.ipynb | covuworie/nobel-physics-prizes | f89a32cd6eb9bbc9119a231bffee89b177ae847a | [
"MIT"
] | null | null | null | 54.18299 | 1,148 | 0.695904 | true | 3,722 | Qwen/Qwen-72B | 1. YES
2. YES | 0.891811 | 0.851953 | 0.759781 | __label__eng_Latn | 0.987019 | 0.603558 |
# Estimation of Temperature and Pressure of a Constant Volume Propane-Oxygen Mixture
A recent project required a first-order approximation to determine if an explosive gas mixture would result in a tank rupture. The following analysis done in Python follows Coopers analysis [[1]] It provides a reasonable approximati... | 9cb109ee4275f5a05b04259581e52d35baaa1067 | 160,071 | ipynb | Jupyter Notebook | _jupyter/tank_burst_analysis.ipynb | lightsquared/lightsquared.github.io | d7cb83732d325ad8c76d3328ffd6cd183785bc50 | [
"MIT"
] | null | null | null | _jupyter/tank_burst_analysis.ipynb | lightsquared/lightsquared.github.io | d7cb83732d325ad8c76d3328ffd6cd183785bc50 | [
"MIT"
] | null | null | null | _jupyter/tank_burst_analysis.ipynb | lightsquared/lightsquared.github.io | d7cb83732d325ad8c76d3328ffd6cd183785bc50 | [
"MIT"
] | null | null | null | 143.048257 | 92,770 | 0.85905 | true | 5,060 | Qwen/Qwen-72B | 1. YES
2. YES | 0.897695 | 0.727975 | 0.6535 | __label__eng_Latn | 0.740201 | 0.356631 |
# Extremal linkage networks
This notebook contains code accompanying the paper [extremal linkage networks](https://arxiv.org/abs/1904.01817).
We first implement the network dynamics and then rely on [TikZ](https://github.com/pgf-tikz/pgf) for visualization.
## The Model
We define a random network on an infinite se... | 4abcc8503d5368a877c105a13c61459eab919fcf | 7,388 | ipynb | Jupyter Notebook | simulation.ipynb | Christian-Hirsch/extremal_linkage | dea32732b2b8ec53d5b356f38c215de1381fa35f | [
"MIT"
] | null | null | null | simulation.ipynb | Christian-Hirsch/extremal_linkage | dea32732b2b8ec53d5b356f38c215de1381fa35f | [
"MIT"
] | null | null | null | simulation.ipynb | Christian-Hirsch/extremal_linkage | dea32732b2b8ec53d5b356f38c215de1381fa35f | [
"MIT"
] | null | null | null | 28.972549 | 349 | 0.458446 | true | 1,222 | Qwen/Qwen-72B | 1. YES
2. YES | 0.841826 | 0.740174 | 0.623098 | __label__eng_Latn | 0.897365 | 0.285995 |
# Pulse stream recovery under additive noise
In the previous notebooks, we considered the pulse stream recovery problem under no noise. Here we investigate the effect of **_additive noise_** on our recovery process. Moreover, we consider:
\begin{align}
y_{meas}[n] = y_{BL}[n] + w[n], \nonumber
\end{align}
where $w... | 48faa760852e494fc643c6a1f9ee1368d397dfec | 220,040 | ipynb | Jupyter Notebook | notebooks/fri_part3_additive_noise.ipynb | ebezzam/frius | c3acc98288c949085b7dea08ef3708581f86ce25 | [
"MIT"
] | null | null | null | notebooks/fri_part3_additive_noise.ipynb | ebezzam/frius | c3acc98288c949085b7dea08ef3708581f86ce25 | [
"MIT"
] | null | null | null | notebooks/fri_part3_additive_noise.ipynb | ebezzam/frius | c3acc98288c949085b7dea08ef3708581f86ce25 | [
"MIT"
] | 1 | 2018-11-26T10:10:33.000Z | 2018-11-26T10:10:33.000Z | 298.561737 | 57,936 | 0.907785 | true | 5,656 | Qwen/Qwen-72B | 1. YES
2. YES | 0.885631 | 0.863392 | 0.764647 | __label__eng_Latn | 0.925001 | 0.614863 |
# Monte Carlo Methods: Lab 1
Take a look at Chapter 10 of Newman's *Computational Physics with Python* where much of this material is drawn from.
```
from IPython.core.display import HTML
css_file = '../ipython_notebook_styles/ngcmstyle.css'
HTML(open(css_file, "r").read())
```
<link href='http://fonts.googleapi... | c34600a807d90c17543ee404f2587b385571f3e7 | 101,939 | ipynb | Jupyter Notebook | FEEG6016 Simulation and Modelling/2014/Monte Carlo Lab 1.ipynb | ngcm/training-public | e5a0d8830df4292315c8879c4b571eef722fdefb | [
"MIT"
] | 7 | 2015-06-23T05:50:49.000Z | 2016-06-22T10:29:53.000Z | FEEG6016 Simulation and Modelling/2014/Monte Carlo Lab 1.ipynb | Jhongesell/training-public | e5a0d8830df4292315c8879c4b571eef722fdefb | [
"MIT"
] | 1 | 2017-11-28T08:29:55.000Z | 2017-11-28T08:29:55.000Z | FEEG6016 Simulation and Modelling/2014/Monte Carlo Lab 1.ipynb | Jhongesell/training-public | e5a0d8830df4292315c8879c4b571eef722fdefb | [
"MIT"
] | 24 | 2015-04-18T21:44:48.000Z | 2019-01-09T17:35:58.000Z | 162.323248 | 47,311 | 0.861574 | true | 2,924 | Qwen/Qwen-72B | 1. YES
2. YES | 0.665411 | 0.817574 | 0.544023 | __label__eng_Latn | 0.950145 | 0.102277 |
<hr style="height:2px;border:none"/>
<H1 align='center'> Image Interpolation </H1>
<H3> INF-285 Computación Científica </H3>
<H3> Autor: Francisco Andrades</H3>
Lenguaje: Python
Temas:
- Image Interpolation
- Interpolación Bicúbica
- Lagrange, Newton, Spline
<hr style="height:2px;border:none"/>
```... | 8ddc148b12f54fb6f9ca68f471d1643db821879f | 180,747 | ipynb | Jupyter Notebook | Otros/BicubicInterpolation.ipynb | franciscoandrades/Portafolio | 69a538b16ee2a6e8aa000c2e13ce1803f8c9f636 | [
"Apache-2.0"
] | null | null | null | Otros/BicubicInterpolation.ipynb | franciscoandrades/Portafolio | 69a538b16ee2a6e8aa000c2e13ce1803f8c9f636 | [
"Apache-2.0"
] | null | null | null | Otros/BicubicInterpolation.ipynb | franciscoandrades/Portafolio | 69a538b16ee2a6e8aa000c2e13ce1803f8c9f636 | [
"Apache-2.0"
] | null | null | null | 326.258123 | 82,856 | 0.922206 | true | 4,421 | Qwen/Qwen-72B | 1. YES
2. YES | 0.76908 | 0.754915 | 0.58059 | __label__spa_Latn | 0.791565 | 0.187236 |
```python
import pandas as pd
from matplotlib import pyplot as plt
import numpy as np
import sympy
import matplotlib as mpl
```
```python
mpl.rc("text", usetex=False)
```
# Punto A #
```python
p=pd.read_csv("DataSet_Resolution_50",header=None)
```
```python
fig1,ax1=plt.subplots(figsize=(8,7))
(n1, bins1, patche... | 71f58c8f52962cb7095ef9fca3795420a4d25716 | 200,224 | ipynb | Jupyter Notebook | Data_Analysis/Fenu_Maldera/Esercizio_2.ipynb | andreasemeraro/MPM_Space_Sciences | b9171f8b926f6ab355c4d87b6f715944b29b05ec | [
"MIT"
] | null | null | null | Data_Analysis/Fenu_Maldera/Esercizio_2.ipynb | andreasemeraro/MPM_Space_Sciences | b9171f8b926f6ab355c4d87b6f715944b29b05ec | [
"MIT"
] | null | null | null | Data_Analysis/Fenu_Maldera/Esercizio_2.ipynb | andreasemeraro/MPM_Space_Sciences | b9171f8b926f6ab355c4d87b6f715944b29b05ec | [
"MIT"
] | null | null | null | 330.40264 | 26,772 | 0.936271 | true | 2,064 | Qwen/Qwen-72B | 1. YES
2. YES | 0.863392 | 0.782662 | 0.675744 | __label__eng_Latn | 0.109482 | 0.408311 |
# PageRank Algorithm
This notebook implements the PageRank algorithm, prepared as a homework in BLG202E - Numerical Methods in CE class at ITU, Spring 2020.
```python
!pip install mechanize
```
Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: mecha... | a9535e3ff61bf761c668565600181c90ec44bfef | 24,166 | ipynb | Jupyter Notebook | pagerank.ipynb | marifdemirtas/pagerank | 0d5796c5720a35aa84b2aa1ef98343a28016d390 | [
"MIT"
] | null | null | null | pagerank.ipynb | marifdemirtas/pagerank | 0d5796c5720a35aa84b2aa1ef98343a28016d390 | [
"MIT"
] | null | null | null | pagerank.ipynb | marifdemirtas/pagerank | 0d5796c5720a35aa84b2aa1ef98343a28016d390 | [
"MIT"
] | null | null | null | 32.568733 | 455 | 0.583175 | true | 3,739 | Qwen/Qwen-72B | 1. YES
2. YES | 0.92523 | 0.843895 | 0.780797 | __label__eng_Latn | 0.972892 | 0.652386 |
## Numerical method
Here we will solve few problems by numerical method using Lagrangian grid which deformed and moved together with system.
We will use Wilkins Method.
### Numerical implementation of boundary conditions
#### Basics of the system
Lets consider tho slabs collision problem. Suppose slabs have areas $A... | 3f8d4a041d82d07de032e39deea309da29a8657f | 3,240 | ipynb | Jupyter Notebook | numerical_method.ipynb | CorpGlory/codebang | 69aff8e91ec661318397f684106d9fd1cc51df57 | [
"MIT"
] | null | null | null | numerical_method.ipynb | CorpGlory/codebang | 69aff8e91ec661318397f684106d9fd1cc51df57 | [
"MIT"
] | null | null | null | numerical_method.ipynb | CorpGlory/codebang | 69aff8e91ec661318397f684106d9fd1cc51df57 | [
"MIT"
] | null | null | null | 30.857143 | 188 | 0.549074 | true | 534 | Qwen/Qwen-72B | 1. YES
2. YES | 0.899121 | 0.689306 | 0.619769 | __label__eng_Latn | 0.990774 | 0.278262 |
```python
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
import random
%matplotlib inline
```
**Note:** This Jupyter notebook is a slightly shortened version of "HT>.ipynb" found at this GitHub repository __[here](https://github.com/craw-daddy/Introductory-DS)__.
## Statistical Hyp... | e701088e514bbfcadf755173b9497caa3040f25a | 54,086 | ipynb | Jupyter Notebook | HT&BT-Short.ipynb | craw-daddy/Introductory-DS | 77590ef50a1e8fb9311daac3a0e65ddcc0559988 | [
"MIT"
] | 1 | 2020-10-17T12:25:22.000Z | 2020-10-17T12:25:22.000Z | HT&BT-Short.ipynb | craw-daddy/Introductory-DS | 77590ef50a1e8fb9311daac3a0e65ddcc0559988 | [
"MIT"
] | null | null | null | HT&BT-Short.ipynb | craw-daddy/Introductory-DS | 77590ef50a1e8fb9311daac3a0e65ddcc0559988 | [
"MIT"
] | 1 | 2019-12-10T07:01:19.000Z | 2019-12-10T07:01:19.000Z | 73.686649 | 23,984 | 0.753929 | true | 6,099 | Qwen/Qwen-72B | 1. YES
2. YES | 0.819893 | 0.855851 | 0.701707 | __label__eng_Latn | 0.995122 | 0.468631 |
# Chapter 2 Exercises
In this notebook we will go through the exercises of chapter 2 of Introduction to Stochastic Processes with R by Robert Dobrow.
```python
import numpy as np
```
## 3.1
Consider a Markov chain with transition Matrix
$$P=\left(\begin{array}{cc}
1/2 & 1/4 & 0 & 1/4 \\
0 & 1/2 & 1/2 & 0\\
1/4 & 1/4... | c9d1d0ca25f353ab2be72ffd60a665fbba8e12c3 | 12,665 | ipynb | Jupyter Notebook | Chapter03_py.ipynb | larispardo/StochasticProcessR | a2f8b6c41f2fe451629209317fc32f2c28e0e4ee | [
"MIT"
] | null | null | null | Chapter03_py.ipynb | larispardo/StochasticProcessR | a2f8b6c41f2fe451629209317fc32f2c28e0e4ee | [
"MIT"
] | null | null | null | Chapter03_py.ipynb | larispardo/StochasticProcessR | a2f8b6c41f2fe451629209317fc32f2c28e0e4ee | [
"MIT"
] | null | null | null | 30.890244 | 314 | 0.471931 | true | 3,087 | Qwen/Qwen-72B | 1. YES
2. YES | 0.944177 | 0.810479 | 0.765235 | __label__eng_Latn | 0.96421 | 0.616231 |
```python
import os.path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from traitlets import traitlets
from IPython.display import display
from ipywidgets import HBox, VBox, BoundedFloatText, BoundedIntText, Text, Layout, Button
```
# Processin... | c058443d4c98d3b6592730ba734fb386b6896500 | 543,501 | ipynb | Jupyter Notebook | HD_prevalence_JNNP_2020.ipynb | mazzalab/playgrounds | ead719e92abe1f20e6d83c25d61aebbd24ae1663 | [
"MIT"
] | null | null | null | HD_prevalence_JNNP_2020.ipynb | mazzalab/playgrounds | ead719e92abe1f20e6d83c25d61aebbd24ae1663 | [
"MIT"
] | null | null | null | HD_prevalence_JNNP_2020.ipynb | mazzalab/playgrounds | ead719e92abe1f20e6d83c25d61aebbd24ae1663 | [
"MIT"
] | 1 | 2021-04-26T18:04:48.000Z | 2021-04-26T18:04:48.000Z | 377.43125 | 104,444 | 0.924898 | true | 12,101 | Qwen/Qwen-72B | 1. YES
2. YES | 0.865224 | 0.841826 | 0.728368 | __label__eng_Latn | 0.3971 | 0.530574 |
# Computer lab 2 - Automatic control 2
$ \newcommand{\mexp}[1]{\mathrm{e}^{#1}} $
$ \newcommand{\transp}{ ^{\mathrm{T}} }$
## Preparations
### Exercise 1 - the spectral factorization theorem
Determine a filter
\begin{equation}
H(z) = \frac{b}{z+a}
\end{equation}
That generates a signal with spectral density
\begin{eq... | e673858960d3ece1003b8a12da2e6513087a7a25 | 50,799 | ipynb | Jupyter Notebook | state-space/notebooks/Spectral-factorization-example.ipynb | kjartan-at-tec/mr2007-computerized-control | 16e35f5007f53870eaf344eea1165507505ab4aa | [
"MIT"
] | 2 | 2020-11-07T05:20:37.000Z | 2020-12-22T09:46:13.000Z | state-space/notebooks/Spectral-factorization-example.ipynb | alfkjartan/control-computarizado | 5b9a3ae67602d131adf0b306f3ffce7a4914bf8e | [
"MIT"
] | 4 | 2020-06-12T20:44:41.000Z | 2020-06-12T20:49:00.000Z | state-space/notebooks/Spectral-factorization-example.ipynb | alfkjartan/control-computarizado | 5b9a3ae67602d131adf0b306f3ffce7a4914bf8e | [
"MIT"
] | 1 | 2019-09-25T20:02:23.000Z | 2019-09-25T20:02:23.000Z | 75.369436 | 19,376 | 0.758322 | true | 5,049 | Qwen/Qwen-72B | 1. YES
2. YES | 0.849971 | 0.826712 | 0.702681 | __label__eng_Latn | 0.315179 | 0.470895 |
### A問題
```python
a,b = map(int, input().split())
print('Yay!' if max(a, b) <= 8 else ':(')
```
10 6
:(
### B問題
```python
a,b =map(int,input().split())
if a == 0:
print(b)
elif a == 1:
print(b*100**1)
elif a == 2:
print(b*100**2)
else:
pass
```
2 100
1000000
```python
a,... | e512e2dbb874533e507530070f1185421995aa54 | 9,452 | ipynb | Jupyter Notebook | ABC100.ipynb | ryosukehata/ABC_practice | a35ba66c6af28752fcea9f409ec66b685e67e40a | [
"MIT"
] | null | null | null | ABC100.ipynb | ryosukehata/ABC_practice | a35ba66c6af28752fcea9f409ec66b685e67e40a | [
"MIT"
] | null | null | null | ABC100.ipynb | ryosukehata/ABC_practice | a35ba66c6af28752fcea9f409ec66b685e67e40a | [
"MIT"
] | null | null | null | 19.691667 | 82 | 0.446995 | true | 2,285 | Qwen/Qwen-72B | 1. YES
2. YES | 0.70253 | 0.712232 | 0.500365 | __label__yue_Hant | 0.09991 | 0.000843 |
```
# default_exp definition.interval
```
# definition.interval
```
#hide
from mathbook.utility.markdown import *
from mathbook.configs import *
# uncomment for editing.
# DESTINATION = 'notebook'
# ORIGIN = 'notebook'
```
## Interval
```
#export
if __name__ == '__main__':
embed_markdown_file('definition.int... | c5a6681762caf94a00bd73b5ac17913c19f3c425 | 2,955 | ipynb | Jupyter Notebook | nbs/definition.interval.ipynb | hyunjongkimmath/mathbook | 058f1b804824198ab35e1273ad9091e66985fde6 | [
"Apache-2.0"
] | null | null | null | nbs/definition.interval.ipynb | hyunjongkimmath/mathbook | 058f1b804824198ab35e1273ad9091e66985fde6 | [
"Apache-2.0"
] | null | null | null | nbs/definition.interval.ipynb | hyunjongkimmath/mathbook | 058f1b804824198ab35e1273ad9091e66985fde6 | [
"Apache-2.0"
] | null | null | null | 28.142857 | 348 | 0.503892 | true | 509 | Qwen/Qwen-72B | 1. YES
2. YES | 0.752013 | 0.754915 | 0.567706 | __label__eng_Latn | 0.960042 | 0.1573 |
# Overview of the Devito domain specific language
```python
from sympy import *
from devito import *
```
## From equations to code in a few lines of Python -- the main objective of this notebook is to demonstrate how Devito and its [SymPy](http://www.sympy.org/en/index.html)-powered symbolic API can be used to solve... | e3c036a98c57385fa7eb8755251229a5cd37ba4a | 14,655 | ipynb | Jupyter Notebook | presentations/devito-dsl.ipynb | devitocodes/devitocodes.github.io | be1828f200d96a1c477a187372fc5d445ccffd75 | [
"Apache-2.0"
] | 2 | 2018-12-18T18:58:14.000Z | 2020-01-22T20:07:57.000Z | presentations/devito-dsl.ipynb | devitoproject/devitoproject.github.io | 93599e9a7f58e2a5c6cf81f84e4e29a813fedad8 | [
"Apache-2.0"
] | 6 | 2018-06-15T14:50:40.000Z | 2019-09-19T08:56:03.000Z | presentations/devito-dsl.ipynb | devitoproject/devitoproject.github.io | 93599e9a7f58e2a5c6cf81f84e4e29a813fedad8 | [
"Apache-2.0"
] | 3 | 2018-06-22T07:03:42.000Z | 2020-01-22T20:08:13.000Z | 24.343854 | 410 | 0.562129 | true | 1,973 | Qwen/Qwen-72B | 1. YES
2. YES | 0.853913 | 0.766294 | 0.654348 | __label__eng_Latn | 0.992264 | 0.3586 |
Universidade Federal do Rio Grande do Sul (UFRGS)
Programa de Pós-Graduação em Engenharia Civil (PPGEC)
# Pré-Introdução à teoria das vibrações
## Aula 4 - equilíbrio dinâmico para vibração livre não amortecida
### *Daniel Barbosa Mapurunga Matos (Aluno PPGEC/UFRGS)*
```python
import numpy as np
import matp... | 482c8ac1564e779d308a052a3eab569cc91b042a | 58,806 | ipynb | Jupyter Notebook | Aula 4- VIbracao livre.ipynb | danielbmmatos/Pre-Vibracoes | 2b62c532fa78060c05be9bf5c4a3330f0751d966 | [
"MIT"
] | 2 | 2020-03-25T01:25:10.000Z | 2020-05-25T14:44:08.000Z | Aula 4- VIbracao livre.ipynb | danielbmmatos/Pre-Vibracoes | 2b62c532fa78060c05be9bf5c4a3330f0751d966 | [
"MIT"
] | null | null | null | Aula 4- VIbracao livre.ipynb | danielbmmatos/Pre-Vibracoes | 2b62c532fa78060c05be9bf5c4a3330f0751d966 | [
"MIT"
] | 4 | 2021-03-10T18:05:51.000Z | 2021-04-12T01:14:59.000Z | 226.176923 | 50,448 | 0.909227 | true | 1,807 | Qwen/Qwen-72B | 1. YES
2. YES | 0.79053 | 0.849971 | 0.671928 | __label__por_Latn | 0.993288 | 0.399445 |
Probabilistic Programming
=====
and Bayesian Methods for Hackers
========
Original content ([this Jupyter notebook](https://nbviewer.jupyter.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter1_Introduction/Ch1_Introduction_PyMC2.ipynb)) created by Cam Davidson-... | 3b9b55729a487a284db85bcfef28ede44a318891 | 506,308 | ipynb | Jupyter Notebook | Chapter1_Introduction/Ch1_Introduction_Gen.ipynb | Fifthist/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | f396b0c917b7e09687d9eea3af208059f67d2a0a | [
"MIT"
] | null | null | null | Chapter1_Introduction/Ch1_Introduction_Gen.ipynb | Fifthist/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | f396b0c917b7e09687d9eea3af208059f67d2a0a | [
"MIT"
] | null | null | null | Chapter1_Introduction/Ch1_Introduction_Gen.ipynb | Fifthist/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | f396b0c917b7e09687d9eea3af208059f67d2a0a | [
"MIT"
] | null | null | null | 442.190393 | 151,962 | 0.928526 | true | 11,587 | Qwen/Qwen-72B | 1. YES
2. YES | 0.607663 | 0.831143 | 0.505055 | __label__eng_Latn | 0.998144 | 0.011741 |
```python
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,
AutoMinorLocator)
```
# Superposition of two waves in perpendicular direction
\begin{equation}
x = a \sin (2\pi f_1 t)\\
y=b \sin (2\pi f_2 t - \phi)
\end{equa... | ba6341d873ccba0073742e9b90416a6a7f3a72bb | 389,107 | ipynb | Jupyter Notebook | Lissajous.ipynb | AmbaPant/NPS | 0500f39f6708388d5c3f2b8d3e5ee5e56a1f646f | [
"MIT"
] | 1 | 2020-09-16T03:21:55.000Z | 2020-09-16T03:21:55.000Z | Lissajous.ipynb | AmbaPant/NPS | 0500f39f6708388d5c3f2b8d3e5ee5e56a1f646f | [
"MIT"
] | null | null | null | Lissajous.ipynb | AmbaPant/NPS | 0500f39f6708388d5c3f2b8d3e5ee5e56a1f646f | [
"MIT"
] | 2 | 2020-08-10T12:17:21.000Z | 2020-09-13T14:31:02.000Z | 1,435.819188 | 180,712 | 0.956793 | true | 1,159 | Qwen/Qwen-72B | 1. YES
2. YES | 0.955319 | 0.885631 | 0.846061 | __label__eng_Latn | 0.33651 | 0.804016 |
# Working with numerical features
We have to prepare our data to work with ML algorithms. In the case of numerical values we have some methods that we should apply before we start working with ML algorithms. Some of those methosts are:
Imputation
Handling Outliers
Feature Scaling
Feature Transformatio... | ffd927a3f856b31687f7b4d925186705d1f7cedb | 353,711 | ipynb | Jupyter Notebook | 4-Machine_Learning/Feature Engineering/Numericas/Practica/1_Numerical_Features - solucion.ipynb | erfederuiz/thebridge_ft_nov21 | 00f7216024ac0cf05e564eb8b1be6e888f277ea4 | [
"MIT"
] | null | null | null | 4-Machine_Learning/Feature Engineering/Numericas/Practica/1_Numerical_Features - solucion.ipynb | erfederuiz/thebridge_ft_nov21 | 00f7216024ac0cf05e564eb8b1be6e888f277ea4 | [
"MIT"
] | null | null | null | 4-Machine_Learning/Feature Engineering/Numericas/Practica/1_Numerical_Features - solucion.ipynb | erfederuiz/thebridge_ft_nov21 | 00f7216024ac0cf05e564eb8b1be6e888f277ea4 | [
"MIT"
] | null | null | null | 85.43744 | 70,386 | 0.818657 | true | 15,159 | Qwen/Qwen-72B | 1. YES
2. YES | 0.92079 | 0.890294 | 0.819774 | __label__eng_Latn | 0.757305 | 0.742942 |
# Derivations and Equation Reference
This guide explains the origin and derivation of the equations used in ``LEGWORK`` functions. Let's go through each of the modules and build up to an equation for the signal-to-noise ratio for a given LISA source.
At the end of this document ([here](#Equation-to-Function-Table)) is... | 07f122d86d44e0733ba5cb153a4d1e39f8a6ee4a | 52,691 | ipynb | Jupyter Notebook | docs/notebooks/Derivations.ipynb | arfon/LEGWORK | 91ca299d00ed6892acdf5980f33826421fa348ef | [
"MIT"
] | 14 | 2021-09-28T21:53:24.000Z | 2022-02-05T14:29:44.000Z | docs/notebooks/Derivations.ipynb | arfon/LEGWORK | 91ca299d00ed6892acdf5980f33826421fa348ef | [
"MIT"
] | 44 | 2021-10-31T15:04:26.000Z | 2022-03-15T19:01:40.000Z | docs/notebooks/Derivations.ipynb | katiebreivik/LEGWORK | 07c3938697ca622fc39d9617d74f28262ac2b1aa | [
"MIT"
] | 4 | 2021-11-18T09:20:53.000Z | 2022-03-16T11:30:44.000Z | 37.85273 | 717 | 0.559849 | true | 12,443 | Qwen/Qwen-72B | 1. YES
2. YES | 0.919643 | 0.867036 | 0.797363 | __label__eng_Latn | 0.878983 | 0.690874 |
# Intro to neural net training with autograd
In this notebook, we'll practice
* using the **autograd** Python package to compute gradients
* using gradient descent to train a basic linear regression (a NN with 0 hidden layers)
* using gradient descent to train a basic neural network for regression (NN with 1+ hidden ... | 512a3363f3d5ab9a09def7380f33d24a4b151a1c | 494,116 | ipynb | Jupyter Notebook | labs/IntroToAutogradAndBackpropForNNets.ipynb | tufts-ml-courses/comp135-19s-assignments | d54f4356e022150d85cfa58ebbf8ccdf66e0f1a9 | [
"MIT"
] | 8 | 2019-02-23T00:28:06.000Z | 2020-01-28T20:45:57.000Z | labs/IntroToAutogradAndBackpropForNNets.ipynb | tufts-ml-courses/comp135-19s-assignments | d54f4356e022150d85cfa58ebbf8ccdf66e0f1a9 | [
"MIT"
] | null | null | null | labs/IntroToAutogradAndBackpropForNNets.ipynb | tufts-ml-courses/comp135-19s-assignments | d54f4356e022150d85cfa58ebbf8ccdf66e0f1a9 | [
"MIT"
] | 18 | 2019-01-24T20:45:04.000Z | 2022-03-21T20:27:11.000Z | 235.517636 | 92,172 | 0.917497 | true | 9,531 | Qwen/Qwen-72B | 1. YES
2. YES | 0.897695 | 0.92079 | 0.826589 | __label__eng_Latn | 0.80206 | 0.758775 |
# ベイズ推定
## ベルヌーイ分布のベイズ推定
具体例 コイントスの確率推定
```python
import numpy as np
import sympy
import matplotlib.pyplot as plt
from scipy.special import gamma
%matplotlib inline
```
```python
mu = sympy.Symbol("u")
def posterior(D, prior):
global mu
# 尤度
likelihood = mu**D[0] * (1-mu)**(D[1]-D[0])
# 事後確率
... | c14fdbb5be07c05b01781d8cebe56d9d5c72eee1 | 117,392 | ipynb | Jupyter Notebook | PRML/BayesianInference.ipynb | naktd31/jupyter-notebook | 4fdd4bea40bafd93d647bf09b2b04524f7427960 | [
"MIT"
] | null | null | null | PRML/BayesianInference.ipynb | naktd31/jupyter-notebook | 4fdd4bea40bafd93d647bf09b2b04524f7427960 | [
"MIT"
] | null | null | null | PRML/BayesianInference.ipynb | naktd31/jupyter-notebook | 4fdd4bea40bafd93d647bf09b2b04524f7427960 | [
"MIT"
] | null | null | null | 304.124352 | 32,598 | 0.915045 | true | 2,106 | Qwen/Qwen-72B | 1. YES
2. YES | 0.903294 | 0.658418 | 0.594745 | __label__yue_Hant | 0.088893 | 0.220121 |
# Bayesian classifier
In statistical classification, the Bayes classifier minimizes the probability of misclassification.
```python
import random
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
random.seed(42) # define the seed (important to reproduce the results)
```
```python
#data = pd.rea... | 2656944e1822e24855b52637981c630279439c61 | 240,045 | ipynb | Jupyter Notebook | bayes_classifier.ipynb | marcelns/data-analysis | 1ea76b4876253f408db7a2c13fdfb8c75eb627dc | [
"Apache-2.0"
] | null | null | null | bayes_classifier.ipynb | marcelns/data-analysis | 1ea76b4876253f408db7a2c13fdfb8c75eb627dc | [
"Apache-2.0"
] | null | null | null | bayes_classifier.ipynb | marcelns/data-analysis | 1ea76b4876253f408db7a2c13fdfb8c75eb627dc | [
"Apache-2.0"
] | null | null | null | 264.950331 | 66,232 | 0.910054 | true | 6,402 | Qwen/Qwen-72B | 1. YES
2. YES | 0.849971 | 0.861538 | 0.732283 | __label__por_Latn | 0.523682 | 0.53967 |
## CCNSS 2018 Module 1: Neurons, synapses and networks
# Tutorial 3: Spike timing dependent plasticity
[source](https://colab.research.google.com/drive/1pE0nERUutXNIjCBQIWD_TdlE-mDLhtR1)
Please execute the cell below to initialise the notebook environment.
```
%autosave 0
import matplotlib.pyplot as plt # import... | c92fdaa450dfe871fce3877085fc79c7207a7978 | 50,246 | ipynb | Jupyter Notebook | module1/3_spike_timing_dependent_plasticity/3_Spike_timing_dependent_plasticity.ipynb | ruyuanzhang/ccnss2018_students | 978b2414ade6116da01c19a945304f9c514fb93f | [
"CC-BY-4.0"
] | 12 | 2018-07-01T10:51:09.000Z | 2021-11-15T22:57:17.000Z | module1/3_spike_timing_dependent_plasticity/3_Spike_timing_dependent_plasticity.ipynb | marcelomattar/ccnss2018_students | 978b2414ade6116da01c19a945304f9c514fb93f | [
"CC-BY-4.0"
] | null | null | null | module1/3_spike_timing_dependent_plasticity/3_Spike_timing_dependent_plasticity.ipynb | marcelomattar/ccnss2018_students | 978b2414ade6116da01c19a945304f9c514fb93f | [
"CC-BY-4.0"
] | 13 | 2018-05-15T02:54:07.000Z | 2021-11-15T22:57:19.000Z | 71.78 | 25,674 | 0.739462 | true | 3,285 | Qwen/Qwen-72B | 1. YES
2. YES | 0.863392 | 0.779993 | 0.673439 | __label__eng_Latn | 0.98399 | 0.402956 |
## Objectives:
- Student should be able to Explain why we care about linear algebra in the scope of data science
- Student should be able to Conceptualize and utilize vectors and matrices through matrix operations and properties such as: square matrix, identity matrix, transpose and inverse
- Student should be able to ... | 1fc57bda8188b1218b725513981d50c1d33f7bd1 | 466,192 | ipynb | Jupyter Notebook | 05-Linear-Algebra/01_Linear_Algebra.ipynb | ashishpatel26/Data-Science-Tutorial-By-Lambda-School | c145f5cc0559ee8ba7260b53e011c165e842fde0 | [
"MIT"
] | 15 | 2019-07-23T20:17:55.000Z | 2021-12-09T02:32:53.000Z | 05-Linear-Algebra/01_Linear_Algebra.ipynb | pesobreiro/data-science-journal | 82a72b4ed5ce380988fac17b0acd97254c2b5c86 | [
"MIT"
] | null | null | null | 05-Linear-Algebra/01_Linear_Algebra.ipynb | pesobreiro/data-science-journal | 82a72b4ed5ce380988fac17b0acd97254c2b5c86 | [
"MIT"
] | 23 | 2019-10-12T15:32:41.000Z | 2022-03-13T05:05:13.000Z | 341.282577 | 118,890 | 0.916757 | true | 7,003 | Qwen/Qwen-72B | 1. YES
2. YES | 0.843895 | 0.826712 | 0.697658 | __label__eng_Latn | 0.994843 | 0.459225 |
```python
# ref https://www.coder.work/article/5024474
from sympy.diffgeom import Manifold, Patch, CoordSystem, TensorProduct
# from sympy.abc import theta, eta, psi
import sympy as sym
x,y,z,a = sym.symbols("x y z a")
m = Manifold("M",3)
patch = Patch("P",m)
cartesian = CoordSystem("cartesian",patch)
# toroidal = C... | 337c272f3a50747f5a90743a185b3d50fc2392d6 | 119,180 | ipynb | Jupyter Notebook | knowledge/sympy_diffgeom_metric_to_Riemann_components.ipynb | partnernetsoftware/openlab | faa4e58486a7bc4140ad3d56545bfb736cb86696 | [
"MIT"
] | 1 | 2020-09-26T05:27:30.000Z | 2020-09-26T05:27:30.000Z | knowledge/sympy_diffgeom_metric_to_Riemann_components.ipynb | partnernetsoftware/openlab | faa4e58486a7bc4140ad3d56545bfb736cb86696 | [
"MIT"
] | null | null | null | knowledge/sympy_diffgeom_metric_to_Riemann_components.ipynb | partnernetsoftware/openlab | faa4e58486a7bc4140ad3d56545bfb736cb86696 | [
"MIT"
] | null | null | null | 1,527.948718 | 90,259 | 0.539596 | true | 33,438 | Qwen/Qwen-72B | 1. YES
2. YES | 0.957278 | 0.715424 | 0.68486 | __label__yue_Hant | 0.166522 | 0.429489 |
#Probing convexity of some functions planted as exercises
For all problems we asume that $x_1, x_2 \in {\rm I\!R}$ and we asume that $z = \alpha x_1 + (1 - \alpha)x_2$, is usable in all functions for probing the convexity and for last we asume that $\alpha \in [0,1]$
1. Show that $f(x) = a \cdot x + b$ is convex, ... | 5531e227f146a310810ed3b16447d6ebe6147070 | 4,457 | ipynb | Jupyter Notebook | Practice/First exam/Convexity.ipynb | QuantumGorilla/Optimization | ed7dc755cb1346d208f5f33fc4814e3931c9b44d | [
"MIT"
] | null | null | null | Practice/First exam/Convexity.ipynb | QuantumGorilla/Optimization | ed7dc755cb1346d208f5f33fc4814e3931c9b44d | [
"MIT"
] | null | null | null | Practice/First exam/Convexity.ipynb | QuantumGorilla/Optimization | ed7dc755cb1346d208f5f33fc4814e3931c9b44d | [
"MIT"
] | null | null | null | 4,457 | 4,457 | 0.498093 | true | 1,554 | Qwen/Qwen-72B | 1. YES
2. YES | 0.884039 | 0.808067 | 0.714363 | __label__eng_Latn | 0.324285 | 0.498037 |
```python
import matplotlib.pyplot as plt
plt.style.use('classic')
%matplotlib inline
```
# Class 3: NumPy
NumPy is a powerful Python module for scientific computing. Among other things, NumPy defines an N-dimensional array object that is especially convenient to use for plotting functions and for simulating and stor... | 1c4428e6797d941c2041597b48c89860883e855c | 177,839 | ipynb | Jupyter Notebook | Lecture Notebooks/Econ126_Class_03.ipynb | t-hdd/econ126 | 17029937bd6c40e606d145f8d530728585c30a1d | [
"MIT"
] | null | null | null | Lecture Notebooks/Econ126_Class_03.ipynb | t-hdd/econ126 | 17029937bd6c40e606d145f8d530728585c30a1d | [
"MIT"
] | null | null | null | Lecture Notebooks/Econ126_Class_03.ipynb | t-hdd/econ126 | 17029937bd6c40e606d145f8d530728585c30a1d | [
"MIT"
] | null | null | null | 168.248817 | 66,212 | 0.887111 | true | 7,303 | Qwen/Qwen-72B | 1. YES
2. YES | 0.800692 | 0.92079 | 0.737269 | __label__eng_Latn | 0.922233 | 0.551255 |
```python
# Método para resolver las energías y eigenfunciones de un sistema cuántico numéricamente por Teoría de Pertubaciones
# Modelado Molecular 2
# By: José Manuel Casillas Martín 22-oct-2017
import numpy as np
from sympy import *
from sympy.physics.qho_1d import E_n, psi_n
from sympy.physics.hydrogen import E... | 90206a92c90ca5ad1befd95e6a177373e1d27dd7 | 80,351 | ipynb | Jupyter Notebook | Perturbaciones/Chema/Ejemplo_perturbaciones_(particula en una caja).ipynb | lazarusA/Density-functional-theory | c74fd44a66f857de570dc50471b24391e3fa901f | [
"MIT"
] | null | null | null | Perturbaciones/Chema/Ejemplo_perturbaciones_(particula en una caja).ipynb | lazarusA/Density-functional-theory | c74fd44a66f857de570dc50471b24391e3fa901f | [
"MIT"
] | null | null | null | Perturbaciones/Chema/Ejemplo_perturbaciones_(particula en una caja).ipynb | lazarusA/Density-functional-theory | c74fd44a66f857de570dc50471b24391e3fa901f | [
"MIT"
] | null | null | null | 86.772138 | 24,548 | 0.757713 | true | 5,431 | Qwen/Qwen-72B | 1. YES
2. YES | 0.73412 | 0.752013 | 0.552067 | __label__spa_Latn | 0.529465 | 0.120967 |
```python
from scipy.stats import gaussian_kde
from scipy.interpolate import interp1d
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
rc('font', **{'family': 'serif', 'serif': ['Computer Modern']})
rc('text', usetex=True)
```
# Building the joint prior
In this repository there exists cod... | 428fa365ec959f2e3bb484ef14f98d3898709a87 | 145,240 | ipynb | Jupyter Notebook | Joint-ChiEff-ChiP-Prior.ipynb | tcallister/effective-spin-priors | cd5813890de043b2dc59bfaaf9f5eb7d57882641 | [
"MIT"
] | 4 | 2021-04-08T05:21:14.000Z | 2021-11-08T07:05:24.000Z | Joint-ChiEff-ChiP-Prior.ipynb | tcallister/effective-spin-priors | cd5813890de043b2dc59bfaaf9f5eb7d57882641 | [
"MIT"
] | 2 | 2021-05-20T00:47:02.000Z | 2021-06-02T15:26:27.000Z | Joint-ChiEff-ChiP-Prior.ipynb | tcallister/effective-spin-priors | cd5813890de043b2dc59bfaaf9f5eb7d57882641 | [
"MIT"
] | 2 | 2021-04-21T01:13:35.000Z | 2021-05-03T01:10:05.000Z | 317.811816 | 41,940 | 0.924029 | true | 3,720 | Qwen/Qwen-72B | 1. YES
2. YES | 0.808067 | 0.782662 | 0.632444 | __label__eng_Latn | 0.93818 | 0.30771 |
## Topics covered in this notebook:
1. What is K-Nearest Neighbors(kNN) mean?
2. Implementation.
3. How to choose K?
4. Common Issues & Fix.
5. Where kNN can fail?
6. References.
## 1. K - Nearest Neighbors:
1. The idea is to make prediction using the closest know data points.
2. Look at the image below:
1. There... | a0280591296d1c4499535f026ddad23e42c74665 | 5,689 | ipynb | Jupyter Notebook | 3.K-Nearest Neighbors/0.Theory/KNN.ipynb | ananth-repos/machine-learning | a510dcf81fab9137c33f568e73d65262667b3973 | [
"MIT"
] | null | null | null | 3.K-Nearest Neighbors/0.Theory/KNN.ipynb | ananth-repos/machine-learning | a510dcf81fab9137c33f568e73d65262667b3973 | [
"MIT"
] | null | null | null | 3.K-Nearest Neighbors/0.Theory/KNN.ipynb | ananth-repos/machine-learning | a510dcf81fab9137c33f568e73d65262667b3973 | [
"MIT"
] | null | null | null | 43.098485 | 153 | 0.595008 | true | 1,124 | Qwen/Qwen-72B | 1. YES
2. YES | 0.819893 | 0.771843 | 0.632829 | __label__eng_Latn | 0.986451 | 0.308605 |
<figure>
<IMG SRC="gfx/Logo_norsk_pos.png" WIDTH=100 ALIGN="right">
</figure>
# Diatomic Molecules and Spectroscopy
*Roberto Di Remigio*, *Luca Frediani*
Spectroscopy probes the electronic structure of atoms and molecules by measuring their interaction with light.
Different portions of the light spectrum can be use... | 04135c389126cfdcdd38cd729767b3a5b927408d | 6,911 | ipynb | Jupyter Notebook | 09_diatomics-spectroscopy.ipynb | ilfreddy/seminars | c7e13874b41cc906a45b672e5b85c57d6880473e | [
"MIT"
] | 4 | 2017-02-04T01:34:33.000Z | 2021-06-12T12:27:37.000Z | 09_diatomics-spectroscopy.ipynb | ilfreddy/seminars | c7e13874b41cc906a45b672e5b85c57d6880473e | [
"MIT"
] | 3 | 2020-03-30T11:00:35.000Z | 2020-05-12T05:42:24.000Z | 09_diatomics-spectroscopy.ipynb | ilfreddy/seminars | c7e13874b41cc906a45b672e5b85c57d6880473e | [
"MIT"
] | 7 | 2016-04-26T20:42:43.000Z | 2022-02-06T11:12:57.000Z | 47.335616 | 310 | 0.653306 | true | 1,318 | Qwen/Qwen-72B | 1. YES
2. YES | 0.795658 | 0.682574 | 0.543095 | __label__eng_Latn | 0.991998 | 0.100122 |
<a href="https://colab.research.google.com/github/aschelin/SimulacoesAGFE/blob/main/SC_EDO_sistemasequacoes.ipynb" target="_parent"></a>
# Sistemas de EDOs
Considere o sistema abaixo:
\begin{equation}
\begin{aligned}
\dot{x} &= f(t,x(t),y(t)) \\
\dot{y} &= g(t,x(t),y(t))
\end{aligned}
\end{equation}
com $x(t=0)=x_0... | 8a5b180b2ca77098413969ba0e3c490725c04828 | 258,911 | ipynb | Jupyter Notebook | SC_EDO_sistemasequacoes.ipynb | aschelin/SimulacoesAGFE | 5294771ff8bf85a1129611bd3406780ef64ac75a | [
"MIT"
] | null | null | null | SC_EDO_sistemasequacoes.ipynb | aschelin/SimulacoesAGFE | 5294771ff8bf85a1129611bd3406780ef64ac75a | [
"MIT"
] | null | null | null | SC_EDO_sistemasequacoes.ipynb | aschelin/SimulacoesAGFE | 5294771ff8bf85a1129611bd3406780ef64ac75a | [
"MIT"
] | null | null | null | 396.49464 | 52,574 | 0.927346 | true | 2,437 | Qwen/Qwen-72B | 1. YES
2. YES | 0.884039 | 0.749087 | 0.662223 | __label__por_Latn | 0.534338 | 0.376896 |
<h1><center>MLHEP 2019</center></h1>
<h2><center>Seminar: Unsupervised Learning</center></h2>
# About
The goal of this seminar is to consider main domains of unsupervised learning and demonstrate algorithms implemented in [scikit-learn](https://scikit-learn.org) library.
Topics:
- Clustering
- Data Scaling
- Princip... | 6af33e865d3905e223044bf9f1ab018e52b4487f | 43,211 | ipynb | Jupyter Notebook | notebooks/day-2/Clustering/Clustering.ipynb | Meshreki/mlhep2019 | 7934173666267ee21faa88d939e26cafe8c5323e | [
"MIT"
] | null | null | null | notebooks/day-2/Clustering/Clustering.ipynb | Meshreki/mlhep2019 | 7934173666267ee21faa88d939e26cafe8c5323e | [
"MIT"
] | null | null | null | notebooks/day-2/Clustering/Clustering.ipynb | Meshreki/mlhep2019 | 7934173666267ee21faa88d939e26cafe8c5323e | [
"MIT"
] | null | null | null | 27.986399 | 391 | 0.514522 | true | 7,167 | Qwen/Qwen-72B | 1. YES
2. YES | 0.870597 | 0.831143 | 0.723591 | __label__eng_Latn | 0.71935 | 0.519476 |
# Lagrangian mechanics
> Marcos Duarte
> [Laboratory of Biomechanics and Motor Control](http://pesquisa.ufabc.edu.br/bmclab)
> Federal University of ABC, Brazil
<center><div style="background-color:#f2f2f2;border:1px solid black;width:72%;padding:5px 10px 5px 10px;text-align:left;">
<i>"The theoretical developmen... | 4eeacc1509556b9c357e661aec6ef1c6eb3da47a | 396,428 | ipynb | Jupyter Notebook | notebooks/lagrangian_mechanics.ipynb | e-moncao-lima/BMC | 98c3abbf89e630d64b695b535b0be4ddc8b2724b | [
"CC-BY-4.0"
] | 1 | 2021-03-15T20:07:52.000Z | 2021-03-15T20:07:52.000Z | notebooks/lagrangian_mechanics.ipynb | e-moncao-lima/BMC | 98c3abbf89e630d64b695b535b0be4ddc8b2724b | [
"CC-BY-4.0"
] | null | null | null | notebooks/lagrangian_mechanics.ipynb | e-moncao-lima/BMC | 98c3abbf89e630d64b695b535b0be4ddc8b2724b | [
"CC-BY-4.0"
] | 1 | 2018-10-13T17:35:16.000Z | 2018-10-13T17:35:16.000Z | 84.256748 | 59,516 | 0.781766 | true | 27,339 | Qwen/Qwen-72B | 1. YES
2. YES | 0.754915 | 0.810479 | 0.611843 | __label__eng_Latn | 0.844907 | 0.259846 |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Objectives" data-toc-modified-id="Objectives-1"><span class="toc-item-num">1 </span>Objectives</a></span></li><li><span><a href="#What-&-Why-of-Linear-Algebra" data-toc-modified-id="What-&... | 0cbd083d9ce774dfda6a95d67d544598e6d27cf6 | 40,086 | ipynb | Jupyter Notebook | Phase_3/ds-linear_algebra-main/linear_algebra.ipynb | VaneezaAhmad/ds-east-042621-lectures | 334f98bb4bd4f8020055e95994764b1587a809c0 | [
"MIT"
] | null | null | null | Phase_3/ds-linear_algebra-main/linear_algebra.ipynb | VaneezaAhmad/ds-east-042621-lectures | 334f98bb4bd4f8020055e95994764b1587a809c0 | [
"MIT"
] | null | null | null | Phase_3/ds-linear_algebra-main/linear_algebra.ipynb | VaneezaAhmad/ds-east-042621-lectures | 334f98bb4bd4f8020055e95994764b1587a809c0 | [
"MIT"
] | 20 | 2021-04-27T19:27:58.000Z | 2021-06-16T15:08:50.000Z | 25.679693 | 4,966 | 0.523674 | true | 6,742 | Qwen/Qwen-72B | 1. YES
2. YES | 0.79053 | 0.865224 | 0.683986 | __label__eng_Latn | 0.903538 | 0.42746 |
# Scale bijectors and LinearOperator
This reading is an introduction to scale bijectors, as well as the `LinearOperator` class, which can be used with them.
```python
!pip install tensorflow=='2.2.0'
```
Collecting tensorflow==2.2.0
Downloading tensorflow-2.2.0-cp37-cp37m-manylinux2010_x86_64.whl (516.2 M... | 7d2fdafb4c28c42030e200e7ec309674cff4a22c | 219,937 | ipynb | Jupyter Notebook | Week3/Scale bijectors and LinearOperator.ipynb | stevensmiley1989/Prob_TF2_Examples | fa022e58a44563d09792070be5d015d0798ca00d | [
"MIT"
] | null | null | null | Week3/Scale bijectors and LinearOperator.ipynb | stevensmiley1989/Prob_TF2_Examples | fa022e58a44563d09792070be5d015d0798ca00d | [
"MIT"
] | null | null | null | Week3/Scale bijectors and LinearOperator.ipynb | stevensmiley1989/Prob_TF2_Examples | fa022e58a44563d09792070be5d015d0798ca00d | [
"MIT"
] | null | null | null | 219,937 | 219,937 | 0.914057 | true | 8,052 | Qwen/Qwen-72B | 1. YES
2. YES | 0.743168 | 0.731059 | 0.543299 | __label__eng_Latn | 0.939124 | 0.100596 |
# Transformations, Eigenvectors, and Eigenvalues
Matrices and vectors are used together to manipulate spatial dimensions. This has a lot of applications, including the mathematical generation of 3D computer graphics, geometric modeling, and the training and optimization of machine learning algorithms. We're not going ... | 9d82599ed391b15e116e045e155f801fae22cf37 | 35,597 | ipynb | Jupyter Notebook | Vector and Matrices by Hiren/03-05-Transformations Eigenvectors and Eigenvalues.ipynb | awesome-archive/Basic-Mathematics-for-Machine-Learning | b6699a9c29ec070a0b1615c46952cb0deeb73b54 | [
"MIT"
] | 401 | 2018-08-29T04:55:26.000Z | 2022-03-29T11:03:39.000Z | Vector and Matrices by Hiren/03-05-Transformations Eigenvectors and Eigenvalues.ipynb | aligeekk/Basic-Mathematics-for-Machine-Learning | 8662076d60e89f58a6e81e4ca1377569472760a2 | [
"Apache-2.0"
] | 1 | 2020-09-28T13:52:53.000Z | 2020-09-28T18:13:53.000Z | Vector and Matrices by Hiren/03-05-Transformations Eigenvectors and Eigenvalues.ipynb | aligeekk/Basic-Mathematics-for-Machine-Learning | 8662076d60e89f58a6e81e4ca1377569472760a2 | [
"Apache-2.0"
] | 135 | 2018-08-29T05:04:00.000Z | 2022-03-30T07:04:25.000Z | 34.426499 | 425 | 0.538107 | true | 7,828 | Qwen/Qwen-72B | 1. YES
2. YES | 0.950411 | 0.893309 | 0.849011 | __label__eng_Latn | 0.90896 | 0.81087 |
# 単回帰分析と重回帰分析
本章では、基礎的な機械学習手法として代表的な**単回帰分析**と**重回帰分析**の仕組みを、数式を用いて説明します。
また次章では、本章で紹介した数式を Python によるプログラミングで実装する例も紹介します。本章と次章を通じて、数学とプログラミングの結びつきを体験して理解することができます。
本チュートリアルの主題であるディープラーニングの前に、単回帰分析と重回帰分析を紹介することには 2 つの理由があります。
1 つ目は、単回帰分析と重回帰分析の数学がニューラルネットワーク含めたディープラーニングの数学の基礎となるためです。
2 つ目は、単回帰分析のアルゴリズムを通して微分、重回帰分析のアル... | 5d4fab06d94dbca1076613901575b43d705dbeff | 42,503 | ipynb | Jupyter Notebook | ja/07_Regression_Analysis_ja.ipynb | youtalk/tutorials | 7b1a517a9f4b43715151add449962bbc7206374f | [
"BSD-3-Clause"
] | null | null | null | ja/07_Regression_Analysis_ja.ipynb | youtalk/tutorials | 7b1a517a9f4b43715151add449962bbc7206374f | [
"BSD-3-Clause"
] | null | null | null | ja/07_Regression_Analysis_ja.ipynb | youtalk/tutorials | 7b1a517a9f4b43715151add449962bbc7206374f | [
"BSD-3-Clause"
] | null | null | null | 38.119283 | 259 | 0.328494 | true | 11,765 | Qwen/Qwen-72B | 1. YES
2. YES | 0.841826 | 0.685949 | 0.57745 | __label__yue_Hant | 0.650637 | 0.179939 |
# Assignment 02 Companion Notebook
This notebook contains some exercises to walk you through implementing the linear regression algorithm. We'll pay special attention to debugging and visualization as we go along.
## A Toy Linear Regression Problem Revisited
As we discovered in the last assignment, the idea of a to... | bc9186a0ba77c4542a8b769de83ae6f3c39d54f5 | 714,876 | ipynb | Jupyter Notebook | M1/Assignment2/Assignment_02_Companion.ipynb | SSModelGit/MachineLearningOlin19 | bd9f4a11f57c6151d266db76193ce9d923fbc4c9 | [
"MIT"
] | null | null | null | M1/Assignment2/Assignment_02_Companion.ipynb | SSModelGit/MachineLearningOlin19 | bd9f4a11f57c6151d266db76193ce9d923fbc4c9 | [
"MIT"
] | null | null | null | M1/Assignment2/Assignment_02_Companion.ipynb | SSModelGit/MachineLearningOlin19 | bd9f4a11f57c6151d266db76193ce9d923fbc4c9 | [
"MIT"
] | null | null | null | 753.293994 | 579,282 | 0.925803 | true | 7,486 | Qwen/Qwen-72B | 1. YES
2. YES | 0.76908 | 0.872347 | 0.670905 | __label__eng_Latn | 0.995804 | 0.397068 |
# ShiftIfYouCan Example Notebook
In this notebook we present a walkthrough example for the ShiftIfYouCan visualisation code.
```python
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
```
<style>.container { width:100% !important; }</style>
##... | a047a182cb87104e954cc5db7cff8dbd707fc0a9 | 740,338 | ipynb | Jupyter Notebook | ShiftIfYouCan.ipynb | MR-T77/ShiftIfYouCan | 88f08463df909dd0029b5f60f693b8dcadccbf9f | [
"MIT"
] | 3 | 2020-10-22T13:41:15.000Z | 2022-01-11T13:19:30.000Z | ShiftIfYouCan.ipynb | MR-T77/ShiftIfYouCan | 88f08463df909dd0029b5f60f693b8dcadccbf9f | [
"MIT"
] | null | null | null | ShiftIfYouCan.ipynb | MR-T77/ShiftIfYouCan | 88f08463df909dd0029b5f60f693b8dcadccbf9f | [
"MIT"
] | null | null | null | 179.955761 | 182,508 | 0.850684 | true | 2,512 | Qwen/Qwen-72B | 1. YES
2. YES | 0.715424 | 0.721743 | 0.516352 | __label__eng_Latn | 0.97722 | 0.037989 |
# Vibration modes of membranes with convex polygonal shape
Nicolás Guarín Zapata
## Description
The idea is to find the modes of vibration for membranes with (convex) polygonal shape. These are found as eigenvalues for the [Helmholtz equation](http://en.wikipedia.org/wiki/Helmholtz_equation)
$$\left(\nabla^2 + \frac... | 324922991f7a355dce624c1cdf540127618b46ce | 536,460 | ipynb | Jupyter Notebook | variational/poly_ritz.ipynb | nicoguaro/FEM_resources | 32f032a4e096fdfd2870e0e9b5269046dd555aee | [
"MIT"
] | 28 | 2015-11-06T16:59:39.000Z | 2022-02-25T18:18:49.000Z | variational/poly_ritz.ipynb | oldninja/FEM_resources | e44f315be217fd78ba95c09e3c94b1693773c047 | [
"MIT"
] | null | null | null | variational/poly_ritz.ipynb | oldninja/FEM_resources | e44f315be217fd78ba95c09e3c94b1693773c047 | [
"MIT"
] | 9 | 2018-06-24T22:12:00.000Z | 2022-01-12T15:57:37.000Z | 116.3183 | 200,164 | 0.787835 | true | 4,303 | Qwen/Qwen-72B | 1. YES
2. YES | 0.936285 | 0.867036 | 0.811793 | __label__eng_Latn | 0.791391 | 0.724399 |
$$ \LaTeX \text{ command declarations here.}
\newcommand{\R}{\mathbb{R}}
\renewcommand{\vec}[1]{\mathbf{#1}}
\newcommand{\X}{\mathcal{X}}
\newcommand{\D}{\mathcal{D}}
\newcommand{\vx}{\mathbf{x}}
\newcommand{\vy}{\mathbf{y}}
\newcommand{\vt}{\mathbf{t}}
\newcommand{\vb}{\mathbf{b}}
\newcommand{\vw}{\mathbf{w}}
$$
```... | 58107d185c358b12ba15072b8d9bdee191004043 | 938,060 | ipynb | Jupyter Notebook | lecture10_bias-variance-tradeoff/lecture10_bias-variance-tradeoff.ipynb | xipengwang/umich-eecs445-f16 | 298407af9fd417c1b6daa6127b17cb2c34c2c772 | [
"MIT"
] | 97 | 2016-09-11T23:15:35.000Z | 2022-02-22T08:03:24.000Z | lecture10_bias-variance-tradeoff/lecture10_bias-variance-tradeoff.ipynb | eecs445-f16/umich-eecs445-f16 | 298407af9fd417c1b6daa6127b17cb2c34c2c772 | [
"MIT"
] | null | null | null | lecture10_bias-variance-tradeoff/lecture10_bias-variance-tradeoff.ipynb | eecs445-f16/umich-eecs445-f16 | 298407af9fd417c1b6daa6127b17cb2c34c2c772 | [
"MIT"
] | 77 | 2016-09-12T20:50:46.000Z | 2022-01-03T14:41:23.000Z | 447.334287 | 188,380 | 0.927476 | true | 9,614 | Qwen/Qwen-72B | 1. YES
2. YES | 0.760651 | 0.70253 | 0.53438 | __label__eng_Latn | 0.944838 | 0.079873 |
---
layout: page
title: Teorema Central do Limite
nav_order: 7
---
[](https://colab.research.google.com/github/icd-ufmg/icd-ufmg.github.io/blob/master/_lessons/07-tcl.ipynb)
# Teorema Central do Limite
{: .no_toc .mb-2 }
O teorema base para os nossos testes de hipóteses
{: .fs-6 .fw-300 }
{: .no_toc .text-delta }
R... | ae70c1099cf3ec6c700f103f03e985bc20f550ce | 457,162 | ipynb | Jupyter Notebook | _lessons/.ipynb_checkpoints/07-tcl-checkpoint.ipynb | icd-ufmg/icd-ufmg.github.io | 5bc96e818938f8dec09dc93d786e4b291d298a02 | [
"MIT"
] | 3 | 2019-02-25T18:25:49.000Z | 2021-05-20T19:22:24.000Z | _lessons/.ipynb_checkpoints/07-tcl-checkpoint.ipynb | thiagomrs/icd-ufmg.github.io | f72c0eca5a0f97d83be214aff52715c986b078a7 | [
"MIT"
] | null | null | null | _lessons/.ipynb_checkpoints/07-tcl-checkpoint.ipynb | thiagomrs/icd-ufmg.github.io | f72c0eca5a0f97d83be214aff52715c986b078a7 | [
"MIT"
] | 3 | 2021-06-05T20:49:02.000Z | 2022-02-11T20:21:44.000Z | 432.508988 | 68,484 | 0.938394 | true | 4,351 | Qwen/Qwen-72B | 1. YES
2. YES | 0.819893 | 0.749087 | 0.614172 | __label__por_Latn | 0.973616 | 0.265257 |
# Fangohr, Hans. Introduction to Python for Computational Science and Engineering, 2015.
Embleton | 20160910 | Notes
### General Notes
* Use `help()` with a command for details
* Use `dir()` with a command for a list of available methods
## Chapter 2, A Powerful Calculator
```python
import math
```
```python
di... | 4f615a67405c3dbdfe3c62abf137f1122836a929 | 134,982 | ipynb | Jupyter Notebook | public/ipy/Fangohr_2015/Fangohr_Python_Intro.ipynb | stembl/stembl.github.io | 5108fc33dccd8c321e1840b62a4a493309a6eeff | [
"MIT"
] | 1 | 2016-12-10T04:04:33.000Z | 2016-12-10T04:04:33.000Z | public/ipy/Fangohr_2015/Fangohr_Python_Intro.ipynb | stembl/stembl.github.io | 5108fc33dccd8c321e1840b62a4a493309a6eeff | [
"MIT"
] | 3 | 2021-05-18T07:27:17.000Z | 2022-02-26T02:16:11.000Z | public/ipy/Fangohr_2015/Fangohr_Python_Intro.ipynb | stembl/stembl.github.io | 5108fc33dccd8c321e1840b62a4a493309a6eeff | [
"MIT"
] | null | null | null | 84.628213 | 27,336 | 0.815864 | true | 7,348 | Qwen/Qwen-72B | 1. YES
2. YES | 0.921922 | 0.934395 | 0.861439 | __label__eng_Latn | 0.931547 | 0.839746 |
# DCEGM Upper Envelope
## ["The endogenous grid method for discrete-continuous dynamic choice models with (or without) taste shocks"](https://onlinelibrary.wiley.com/doi/abs/10.3982/QE643)
<p style="text-align: center;"><small><small><small>For the following badges: GitHub does not allow click-through redirects; right... | ec6a3a269034badbc2638b461aa8d82e935d7db1 | 259,651 | ipynb | Jupyter Notebook | notebooks/DCEGM-Upper-Envelope.ipynb | sbenthall/DemARK | ef1c010091d28c7dea2e5d4fa0f746e67c6b23f4 | [
"Apache-2.0"
] | null | null | null | notebooks/DCEGM-Upper-Envelope.ipynb | sbenthall/DemARK | ef1c010091d28c7dea2e5d4fa0f746e67c6b23f4 | [
"Apache-2.0"
] | null | null | null | notebooks/DCEGM-Upper-Envelope.ipynb | sbenthall/DemARK | ef1c010091d28c7dea2e5d4fa0f746e67c6b23f4 | [
"Apache-2.0"
] | null | null | null | 259.132735 | 21,916 | 0.917177 | true | 5,810 | Qwen/Qwen-72B | 1. YES
2. YES | 0.763484 | 0.731059 | 0.558151 | __label__eng_Latn | 0.977673 | 0.135102 |
# Fundamentals of Data Science
Winter Semester 2021
## Prof. Fabio Galasso, Guido D'Amely, Alessandro Flaborea, Luca Franco, Muhammad Rameez Ur Rahman and Alessio Sampieri
<galasso@di.uniroma1.it>, <damely@di.uniroma1.it>, <flaborea@di.uniroma1.it>, <franco@diag.uniroma1.it>, <rahman@di.uniroma1.it>, <alessiosampieri2... | aad98649cfd4895ded342e245e173e1b64fa86e5 | 534,927 | ipynb | Jupyter Notebook | .ipynb_checkpoints/FDS_Exercise2_Assignment-checkpoint.ipynb | SimBoex/FDS-homework2 | 4f64f78ceb59bc81f2b966cc9009be4790403879 | [
"MIT"
] | null | null | null | .ipynb_checkpoints/FDS_Exercise2_Assignment-checkpoint.ipynb | SimBoex/FDS-homework2 | 4f64f78ceb59bc81f2b966cc9009be4790403879 | [
"MIT"
] | null | null | null | .ipynb_checkpoints/FDS_Exercise2_Assignment-checkpoint.ipynb | SimBoex/FDS-homework2 | 4f64f78ceb59bc81f2b966cc9009be4790403879 | [
"MIT"
] | null | null | null | 170.576212 | 53,420 | 0.893425 | true | 14,652 | Qwen/Qwen-72B | 1. YES
2. YES | 0.800692 | 0.779993 | 0.624534 | __label__eng_Latn | 0.940911 | 0.289332 |
```python
from sympy import *
init_printing()
```
```python
def skew(l):
l1, l2, l3 = l
return Matrix([
[0, -l3, l2],
[l3, 0, -l1],
[-l2, l1, 0]
])
```
```python
# define state variables
x, y, z, eta0, eps1, eps2, eps3, u, v, w, p, q, r = symbols('x y z et0 eps1 eps2 eps3 u v w p... | d11bfd8ebf74a363f94282abdecb81c8f0a535ac | 36,291 | ipynb | Jupyter Notebook | sam_dynamics/notebooks/dynamics.ipynb | Jollerprutt/sam_common | dd8b43b3c69eee76fe0c35a98db9dfb67f2b79f2 | [
"BSD-3-Clause"
] | 1 | 2020-06-09T18:23:53.000Z | 2020-06-09T18:23:53.000Z | sam_dynamics/notebooks/dynamics.ipynb | Jollerprutt/sam_common | dd8b43b3c69eee76fe0c35a98db9dfb67f2b79f2 | [
"BSD-3-Clause"
] | 3 | 2020-10-06T09:46:03.000Z | 2021-03-10T13:40:44.000Z | sam_dynamics/notebooks/dynamics.ipynb | Jollerprutt/sam_common | dd8b43b3c69eee76fe0c35a98db9dfb67f2b79f2 | [
"BSD-3-Clause"
] | 5 | 2020-01-20T18:33:55.000Z | 2020-12-29T12:34:22.000Z | 109.310241 | 1,695 | 0.658924 | true | 1,916 | Qwen/Qwen-72B | 1. YES
2. YES | 0.839734 | 0.782662 | 0.657228 | __label__krc_Cyrl | 0.291595 | 0.365292 |
# 成為初級資料分析師 | Python 程式設計
> 函式：參考解答
## 郭耀仁
## 隨堂練習：定義一個函式 `product(*args)` 能回傳 `*args` 所組成之數列的乘積
- 預期輸入：彈性參數 `*args`
- 預期輸出：一個數值
```python
def product(*args):
"""
>>> product(0, 1, 2)
0
>>> product(1, 2, 3, 4, 5)
120
>>> product(1, 3, 5, 7, 9)
945
"""
ans = 1
for i in args:... | 875cf368ff9f7d637b4d11bf7f2a32b0d567f210 | 8,151 | ipynb | Jupyter Notebook | suggested_answers/07-suggested-answers.ipynb | datainpoint/classroom-introduction-to-python | a5d4036829eda3a0ed1a0a0af752f541e4e015e7 | [
"MIT"
] | null | null | null | suggested_answers/07-suggested-answers.ipynb | datainpoint/classroom-introduction-to-python | a5d4036829eda3a0ed1a0a0af752f541e4e015e7 | [
"MIT"
] | null | null | null | suggested_answers/07-suggested-answers.ipynb | datainpoint/classroom-introduction-to-python | a5d4036829eda3a0ed1a0a0af752f541e4e015e7 | [
"MIT"
] | null | null | null | 25.794304 | 175 | 0.465832 | true | 1,924 | Qwen/Qwen-72B | 1. YES
2. YES | 0.859664 | 0.7773 | 0.668217 | __label__yue_Hant | 0.481112 | 0.390822 |
# Quantization of Signals
*This jupyter notebook is part of a [collection of notebooks](../index.ipynb) on various topics of Digital Signal Processing. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-rostock.de).*
## Quantization Error of a Linear Uniform Quantizer
As... | 5162ef41c22c0714b2b609c75dcc338fbd960a8f | 377,633 | ipynb | Jupyter Notebook | quantization/linear_uniform_quantization_error.ipynb | ZeroCommits/digital-signal-processing-lecture | e1e65432a5617a309ec02327a14962e37a0f7ec5 | [
"MIT"
] | 630 | 2016-01-05T17:11:43.000Z | 2022-03-30T07:48:27.000Z | quantization/linear_uniform_quantization_error.ipynb | alirezaopmc/digital-signal-processing-lecture | e1e65432a5617a309ec02327a14962e37a0f7ec5 | [
"MIT"
] | 12 | 2016-11-07T15:49:55.000Z | 2022-03-10T13:05:50.000Z | quantization/linear_uniform_quantization_error.ipynb | alirezaopmc/digital-signal-processing-lecture | e1e65432a5617a309ec02327a14962e37a0f7ec5 | [
"MIT"
] | 172 | 2015-12-26T21:05:40.000Z | 2022-03-10T23:13:30.000Z | 60.132643 | 28,822 | 0.616382 | true | 5,087 | Qwen/Qwen-72B | 1. YES
2. YES | 0.721743 | 0.835484 | 0.603005 | __label__eng_Latn | 0.984686 | 0.239312 |
# Multibody dynamics of simple biomechanical models
> Marcos Duarte
> Laboratory of Biomechanics and Motor Control ([http://demotu.org/](http://demotu.org/))
> Federal University of ABC, Brazil
The human body is composed of multiple interconnected segments (which can be modeled as rigid or flexible) and each segm... | 5968af1262beb97547e26c2c8747bd6aef4d318d | 330,696 | ipynb | Jupyter Notebook | courses/modsim2018/tasks/Tasks_DuringLecture18/BMC-master/notebooks/MultibodyDynamics.ipynb | raissabthibes/bmc | 840800fb94ea3bf188847d0771ca7197dfec68e3 | [
"MIT"
] | null | null | null | courses/modsim2018/tasks/Tasks_DuringLecture18/BMC-master/notebooks/MultibodyDynamics.ipynb | raissabthibes/bmc | 840800fb94ea3bf188847d0771ca7197dfec68e3 | [
"MIT"
] | null | null | null | courses/modsim2018/tasks/Tasks_DuringLecture18/BMC-master/notebooks/MultibodyDynamics.ipynb | raissabthibes/bmc | 840800fb94ea3bf188847d0771ca7197dfec68e3 | [
"MIT"
] | null | null | null | 192.601048 | 43,712 | 0.874483 | true | 14,392 | Qwen/Qwen-72B | 1. YES
2. YES | 0.718594 | 0.73412 | 0.527534 | __label__eng_Latn | 0.896528 | 0.063968 |
## Gaussian Process Regression
## Part I - Multivariate Gaussian Distribution
## 2nd Machine Learning in Heliophysics
## Boulder, CO
### 21 - 25 March 2022
### Enrico Camporeale (University of Colorado, Boulder & NOAA Space Weather Prediction Center)
#### enrico.camporeale@noaa.gov
This work is licensed under a <a re... | 532c2541c0383c41e39764c4c8e93ac355cb4786 | 138,451 | ipynb | Jupyter Notebook | Gaussian Process Regression Part 1.ipynb | ecamporeale/GP_lecture_MLHelio | 194fd9f2c2908bd286c5945d9f243a91163e1397 | [
"MIT"
] | 8 | 2022-03-21T21:43:24.000Z | 2022-03-30T12:40:47.000Z | Gaussian Process Regression Part 1.ipynb | ecamporeale/GP_lecture_MLHelio | 194fd9f2c2908bd286c5945d9f243a91163e1397 | [
"MIT"
] | null | null | null | Gaussian Process Regression Part 1.ipynb | ecamporeale/GP_lecture_MLHelio | 194fd9f2c2908bd286c5945d9f243a91163e1397 | [
"MIT"
] | 1 | 2022-03-28T13:44:21.000Z | 2022-03-28T13:44:21.000Z | 145.279119 | 58,188 | 0.875573 | true | 6,793 | Qwen/Qwen-72B | 1. YES
2. YES | 0.903294 | 0.774583 | 0.699677 | __label__eng_Latn | 0.405657 | 0.463915 |
# Example Notebook for sho1d.py
Import the sho1d.py file as well as the test_sho1d.py file
```
from sympy import *
from IPython.display import display_pretty
from sympy.physics.quantum import *
from sympy.physics.quantum.sho1d import *
from sympy.physics.quantum.tests.test_sho1d import *
init_printing(pretty_print=F... | 28679ff682c327f2bb6cfc5b53d15cf3d24b8cf3 | 25,888 | ipynb | Jupyter Notebook | examples/notebooks/sho1d_example.ipynb | utkarshdeorah/sympy | dcdf59bbc6b13ddbc329431adf72fcee294b6389 | [
"BSD-3-Clause"
] | 8,323 | 2015-01-02T15:51:43.000Z | 2022-03-31T13:13:19.000Z | examples/notebooks/sho1d_example.ipynb | utkarshdeorah/sympy | dcdf59bbc6b13ddbc329431adf72fcee294b6389 | [
"BSD-3-Clause"
] | 15,102 | 2015-01-01T01:33:17.000Z | 2022-03-31T22:53:13.000Z | examples/notebooks/sho1d_example.ipynb | utkarshdeorah/sympy | dcdf59bbc6b13ddbc329431adf72fcee294b6389 | [
"BSD-3-Clause"
] | 4,490 | 2015-01-01T17:48:07.000Z | 2022-03-31T17:24:05.000Z | 19.686692 | 154 | 0.408336 | true | 2,251 | Qwen/Qwen-72B | 1. YES
2. YES | 0.891811 | 0.819893 | 0.73119 | __label__eng_Latn | 0.797938 | 0.537131 |
```python
# Check that have our correct Kernel running
import sys
print(sys.executable)
print(sys.version)
print(sys.version_info)
```
/opt/conda/envs/python/bin/python
3.8.3 (default, Jul 2 2020, 16:21:59)
[GCC 7.3.0]
sys.version_info(major=3, minor=8, micro=3, releaselevel='final', serial=0)
# P... | 8632f402860d35e1cb6d5ac1b17d5c0ba2c1f1da | 50,609 | ipynb | Jupyter Notebook | 9-MyJupyterNotebooks/43-SolidsInRivers/SolidsInRivers.ipynb | dustykat/engr-1330-psuedo-course | 3e7e31a32a1896fcb1fd82b573daa5248e465a36 | [
"CC0-1.0"
] | null | null | null | 9-MyJupyterNotebooks/43-SolidsInRivers/SolidsInRivers.ipynb | dustykat/engr-1330-psuedo-course | 3e7e31a32a1896fcb1fd82b573daa5248e465a36 | [
"CC0-1.0"
] | null | null | null | 9-MyJupyterNotebooks/43-SolidsInRivers/SolidsInRivers.ipynb | dustykat/engr-1330-psuedo-course | 3e7e31a32a1896fcb1fd82b573daa5248e465a36 | [
"CC0-1.0"
] | null | null | null | 70.095568 | 13,408 | 0.722026 | true | 4,837 | Qwen/Qwen-72B | 1. YES
2. YES | 0.682574 | 0.752013 | 0.513304 | __label__eng_Latn | 0.897069 | 0.030906 |
## **Viscoelastic wave equation implementation on a staggered grid**
This is a first attempt at implementing the viscoelastic wave equation as described in [1]. See also the FDELMODC implementation by Jan Thorbecke [2].
In the following example, a three dimensional toy problem will be introduced consisting of a sing... | 3026496855823994183880899015961f2f72dffe | 161,153 | ipynb | Jupyter Notebook | examples/seismic/tutorials/09_viscoelastic.ipynb | rhodrin/devito | cd1ae745272eb0315aa1c36038a3174f1817e0d0 | [
"MIT"
] | 1 | 2020-06-08T20:44:35.000Z | 2020-06-08T20:44:35.000Z | examples/seismic/tutorials/09_viscoelastic.ipynb | rhodrin/devito | cd1ae745272eb0315aa1c36038a3174f1817e0d0 | [
"MIT"
] | null | null | null | examples/seismic/tutorials/09_viscoelastic.ipynb | rhodrin/devito | cd1ae745272eb0315aa1c36038a3174f1817e0d0 | [
"MIT"
] | 1 | 2021-01-05T07:27:35.000Z | 2021-01-05T07:27:35.000Z | 330.231557 | 43,560 | 0.931947 | true | 2,130 | Qwen/Qwen-72B | 1. YES
2. YES | 0.899121 | 0.682574 | 0.613717 | __label__eng_Latn | 0.780155 | 0.2642 |
# **Is there a reasonable (physical) interpretation of neural network weights - or is this even a thing to care about?**
## **Maybe yes, maybe no, definitely sometimes**
## **NOTES:**
- Okay .... I'm just going to assume everybody knows some basics of NNets
- I ended up going down this path because of the work I... | 08cda0e44a6f534a87ee16f2686537fe3c69e9a0 | 112,245 | ipynb | Jupyter Notebook | NNet_weight_interpretation.ipynb | Vincent-de-Comarmond/phys-wght-interp | fdb971f81ec9415c4e148b2195b8db67a3ae9974 | [
"MIT"
] | null | null | null | NNet_weight_interpretation.ipynb | Vincent-de-Comarmond/phys-wght-interp | fdb971f81ec9415c4e148b2195b8db67a3ae9974 | [
"MIT"
] | null | null | null | NNet_weight_interpretation.ipynb | Vincent-de-Comarmond/phys-wght-interp | fdb971f81ec9415c4e148b2195b8db67a3ae9974 | [
"MIT"
] | null | null | null | 144.088575 | 36,644 | 0.872654 | true | 3,004 | Qwen/Qwen-72B | 1. YES
2. YES | 0.746139 | 0.845942 | 0.631191 | __label__eng_Latn | 0.989774 | 0.304798 |
# Google Page Rank Algorithm
In this notebook, we learn and code up a simplified version of Google's Page Rank Algorithm, which is a direct application of Eigenvectors and Eigenvalues we learnt in Linear Algebra.
Reference to the original paper: $\href{http://ilpubs.stanford.edu:8090/422/1/1999-66.pdf}{here}$
Re... | f109cf6edb299e272771fba288461a5b6090e987 | 10,235 | ipynb | Jupyter Notebook | day8/morning/Google Page Rank Algorithm Assignment/Page Rank Algorithm.ipynb | avani17101/CVIT-Workshop | 0339021123b82dfa55c6f6fa4d8c4322ecf7e687 | [
"MIT"
] | 4 | 2020-06-27T06:38:10.000Z | 2021-06-01T15:37:33.000Z | day8/morning/Google Page Rank Algorithm Assignment/Page Rank Algorithm.ipynb | avani17101/CVIT-Workshop | 0339021123b82dfa55c6f6fa4d8c4322ecf7e687 | [
"MIT"
] | 4 | 2020-06-08T18:41:11.000Z | 2020-07-27T10:25:24.000Z | day8/morning/Google Page Rank Algorithm Assignment/Page Rank Algorithm.ipynb | avani17101/CVIT-Workshop | 0339021123b82dfa55c6f6fa4d8c4322ecf7e687 | [
"MIT"
] | null | null | null | 30.46131 | 383 | 0.542648 | true | 1,933 | Qwen/Qwen-72B | 1. YES
2. YES | 0.94079 | 0.91118 | 0.857229 | __label__eng_Latn | 0.953623 | 0.829962 |
# GPyTorch Regression Tutorial
<a href="https://colab.research.google.com/github/jwangjie/gpytorch/blob/master/examples/01_Exact_GPs/Simple_GP_Regression.ipynb" target="_parent"></a>
## Introduction
In this notebook, we demonstrate many of the design features of GPyTorch using the simplest example, training an RBF k... | addaabc764b7f6f2b769f1222d4ab1729fd6342d | 103,854 | ipynb | Jupyter Notebook | examples/01_Exact_GPs/Simple_GP_Regression.ipynb | jwangjie/gpytorch | 15979dacf1997af7daf0fdeddbdbfcef0730b007 | [
"MIT"
] | 2 | 2021-10-30T03:50:28.000Z | 2022-02-22T22:01:14.000Z | examples/01_Exact_GPs/Simple_GP_Regression.ipynb | jwangjie/gpytorch | 15979dacf1997af7daf0fdeddbdbfcef0730b007 | [
"MIT"
] | null | null | null | examples/01_Exact_GPs/Simple_GP_Regression.ipynb | jwangjie/gpytorch | 15979dacf1997af7daf0fdeddbdbfcef0730b007 | [
"MIT"
] | 3 | 2020-09-18T18:58:12.000Z | 2021-05-27T15:39:00.000Z | 161.514774 | 28,066 | 0.855875 | true | 4,546 | Qwen/Qwen-72B | 1. YES
2. YES | 0.712232 | 0.849971 | 0.605377 | __label__eng_Latn | 0.932383 | 0.244824 |
# 问题设定
在小车倒立杆（CartPole）游戏中，我们希望通过强化学习训练一个智能体（agent），尽可能不断地左右移动小车，使得小车上的杆不倒，我们首先定义CartPole游戏：
CartPole游戏即是强化学习模型的enviorment，它与agent交互，实时更新state，内部定义了reward function，其中state有以下定义：
$$
state \in \mathbb{R}^4
$$
state每一个维度分别代表了：
- 小车位置，它的取值范围是-2.4到2.4
- 小车速度，它的取值范围是负无穷到正无穷
- 杆的角度，它的取值范围是-41.8°到41.8°
- 杆的角速，它的取值范围是负无穷到正... | 611d2fd6c368bf41c1c4612a5f263f8883a71915 | 30,868 | ipynb | Jupyter Notebook | note/PolicyGradient.ipynb | Ceruleanacg/Learning-Notes | 1b2718dc85e622e35670fffbb525bb50d385f9a3 | [
"MIT"
] | 95 | 2018-06-01T03:57:39.000Z | 2021-12-31T04:51:21.000Z | note/PolicyGradient.ipynb | Ceruleanacg/Descent | 1b2718dc85e622e35670fffbb525bb50d385f9a3 | [
"MIT"
] | 1 | 2020-02-28T13:27:15.000Z | 2020-02-28T13:27:15.000Z | note/PolicyGradient.ipynb | Ceruleanacg/Descent | 1b2718dc85e622e35670fffbb525bb50d385f9a3 | [
"MIT"
] | 15 | 2018-06-24T07:33:29.000Z | 2020-10-03T04:12:27.000Z | 60.054475 | 17,316 | 0.751782 | true | 2,850 | Qwen/Qwen-72B | 1. YES
2. YES | 0.743168 | 0.695958 | 0.517214 | __label__eng_Latn | 0.125451 | 0.039991 |
# Neural Network Fundamentals
## Gradient Descent Introduction:
https://www.youtube.com/watch?v=IxBYhjS295w
```python
from IPython.display import YouTubeVideo
YouTubeVideo("IxBYhjS295w")
```
```python
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
np.... | 579d49b063b104ea1866841d861d00303db60ae5 | 98,058 | ipynb | Jupyter Notebook | jupyter/Keras_TensorFlow_Course/Lesson 02 - GradientDescent.ipynb | multivacplatform/multivac-dl | 54cb33960ba14f32ed9ac185a4c151a6b72a97ca | [
"MIT"
] | 1 | 2018-11-24T10:47:49.000Z | 2018-11-24T10:47:49.000Z | jupyter/Keras_TensorFlow_Course/Lesson 02 - GradientDescent.ipynb | multivacplatform/multivac-dl | 54cb33960ba14f32ed9ac185a4c151a6b72a97ca | [
"MIT"
] | null | null | null | jupyter/Keras_TensorFlow_Course/Lesson 02 - GradientDescent.ipynb | multivacplatform/multivac-dl | 54cb33960ba14f32ed9ac185a4c151a6b72a97ca | [
"MIT"
] | null | null | null | 138.304654 | 27,052 | 0.88621 | true | 2,212 | Qwen/Qwen-72B | 1. YES
2. YES | 0.9659 | 0.851953 | 0.822901 | __label__eng_Latn | 0.488653 | 0.750207 |
<!--NAVIGATION-->
< [Biological Computing in Python I](05-Python_I.ipynb) | [Main Contents](Index.ipynb) | [Biological Computing in R](07-R.ipynb) >
# Biological Computing in Python II <span class="tocSkip"> <a name="chap:python_II"></a>
>> ...some things in life are bad. They can really make you mad. Other thin... | efdafea78efa0a689de37337e427182ad397dc84 | 215,747 | ipynb | Jupyter Notebook | notebooks/06-Python_II.ipynb | mathemage/TheMulQuaBio | 63a0ad6803e2aa1b808bc4517009c18a8c190b4c | [
"MIT"
] | 1 | 2019-10-12T13:33:14.000Z | 2019-10-12T13:33:14.000Z | notebooks/06-Python_II.ipynb | OScott19/TheMulQuaBio | 197d710f76163469dfc7fa9d2d95ba3a739eccc7 | [
"MIT"
] | null | null | null | notebooks/06-Python_II.ipynb | OScott19/TheMulQuaBio | 197d710f76163469dfc7fa9d2d95ba3a739eccc7 | [
"MIT"
] | null | null | null | 53.402723 | 45,740 | 0.730703 | true | 19,152 | Qwen/Qwen-72B | 1. YES
2. YES | 0.689306 | 0.872347 | 0.601314 | __label__eng_Latn | 0.968052 | 0.235384 |
```python
from sympy import *
from math import factorial
```
# Discrete Random Variables
```python
"""
Definition:
The cumulative distribution function (CDF), F(·), of a random
variable, X, is defined by
F(x) := P(X ≤ x).
"""
```
```python
#Exemplo: Jogar dado
x = (1,2,3,4,5,6)
wp = 1/len(x)
```
```python
"""
... | 6dd4e5721ddb6e000f8e25e79019951687dd25ed | 8,703 | ipynb | Jupyter Notebook | Financial Engineering & Risk Management/Introduction to Financial Engineering and Risk Management/probability(I).ipynb | MaikeRM/FinancialEngineering | d5881995ff3097e77cb62633ab22d25625c81ee7 | [
"MIT"
] | null | null | null | Financial Engineering & Risk Management/Introduction to Financial Engineering and Risk Management/probability(I).ipynb | MaikeRM/FinancialEngineering | d5881995ff3097e77cb62633ab22d25625c81ee7 | [
"MIT"
] | null | null | null | Financial Engineering & Risk Management/Introduction to Financial Engineering and Risk Management/probability(I).ipynb | MaikeRM/FinancialEngineering | d5881995ff3097e77cb62633ab22d25625c81ee7 | [
"MIT"
] | null | null | null | 21.330882 | 90 | 0.47363 | true | 1,325 | Qwen/Qwen-72B | 1. YES
2. YES | 0.923039 | 0.909907 | 0.83988 | __label__eng_Latn | 0.859196 | 0.789655 |
# Making a Binary Decision
## _Visualizing Binary Regression_
```
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stats
from scipy.optimize import curve_fit
%matplotlib inline
```
A normal decision when working with binary data (situations where the response $y \in \{0,1\}$) is to perform... | 67caa078db5631996e9c2686ccabc31b051b7526 | 656,447 | ipynb | Jupyter Notebook | bayes_analysis_vegetabile/tutorials/LogisticRegression.ipynb | sot/aca_stats | 0b0b393cd42d2e9162ce4925b468037f2b7c7a18 | [
"BSD-3-Clause"
] | null | null | null | bayes_analysis_vegetabile/tutorials/LogisticRegression.ipynb | sot/aca_stats | 0b0b393cd42d2e9162ce4925b468037f2b7c7a18 | [
"BSD-3-Clause"
] | 3 | 2017-05-22T20:16:17.000Z | 2018-11-27T12:44:03.000Z | bayes_analysis_vegetabile/tutorials/LogisticRegression.ipynb | sot/aca_stats | 0b0b393cd42d2e9162ce4925b468037f2b7c7a18 | [
"BSD-3-Clause"
] | null | null | null | 647.383629 | 52,589 | 0.934173 | true | 4,529 | Qwen/Qwen-72B | 1. YES
2. YES | 0.887205 | 0.718594 | 0.63754 | __label__eng_Latn | 0.524044 | 0.31955 |
# Hybrid Monte Carlo
## Affine Short Rate Models
In this notebook we analyse yield curve modelling based on affine term structure models. We start with a classical CIR model. Then we analyse initial yield curve calibration via deterministic shift extension. Finally, we also analyse the impact of square root processes... | 07a176f32c974dac0befc96ffd0ab389cf56c437 | 9,238 | ipynb | Jupyter Notebook | doc/AffineShortRateModels.ipynb | sschlenkrich/HybridMonteCarlo | 72f54aa4bcd742430462b27b72d70369c01f9ac4 | [
"MIT"
] | 3 | 2021-08-18T18:34:41.000Z | 2021-12-24T07:05:19.000Z | doc/AffineShortRateModels.ipynb | sschlenkrich/HybridMonteCarlo | 72f54aa4bcd742430462b27b72d70369c01f9ac4 | [
"MIT"
] | null | null | null | doc/AffineShortRateModels.ipynb | sschlenkrich/HybridMonteCarlo | 72f54aa4bcd742430462b27b72d70369c01f9ac4 | [
"MIT"
] | 3 | 2021-01-31T11:41:19.000Z | 2022-03-25T19:51:20.000Z | 31.52901 | 296 | 0.549361 | true | 2,044 | Qwen/Qwen-72B | 1. YES
2. YES | 0.919643 | 0.808067 | 0.743133 | __label__eng_Latn | 0.304767 | 0.564879 |
<a href="https://colab.research.google.com/github/julianovale/project_trains/blob/master/Exemplo_03.ipynb" target="_parent"></a>
```
from sympy import I, Matrix, symbols, Symbol, eye
from datetime import datetime
import numpy as np
import pandas as pd
```
```
# Rotas
R1 = Matrix([[0,"R1_p1",0],[0,0,"R1_v1"],[0,0,0... | 04c7d06209ea67e2a37fb845b4a623938c2e55d7 | 55,764 | ipynb | Jupyter Notebook | Exemplo_03.ipynb | julianovale/project_trains | 73f698ab9618363b93777ab7337be813bf14d688 | [
"MIT"
] | null | null | null | Exemplo_03.ipynb | julianovale/project_trains | 73f698ab9618363b93777ab7337be813bf14d688 | [
"MIT"
] | null | null | null | Exemplo_03.ipynb | julianovale/project_trains | 73f698ab9618363b93777ab7337be813bf14d688 | [
"MIT"
] | null | null | null | 35.00565 | 235 | 0.244172 | true | 8,857 | Qwen/Qwen-72B | 1. YES
2. YES | 0.76908 | 0.651355 | 0.500944 | __label__cym_Latn | 0.151307 | 0.00219 |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Singular-Value-Decomposition-(SVD)" data-toc-modified-id="Singular-Value-Decomposition-(SVD)-1"><span class="toc-item-num">1 </span>Singular Value Decomposition (SVD)</a></span><ul class="toc-ite... | 7986d2aed38d485b26ae6d0b2c7fc19a1f8e3fa5 | 158,757 | ipynb | Jupyter Notebook | dim_reduct/svd.ipynb | certara-ShengnanHuang/machine-learning | d21dfbeabf2876ffe49fcef444ca4516c4d36df0 | [
"MIT"
] | 2,104 | 2016-04-15T13:35:55.000Z | 2022-03-28T10:39:51.000Z | dim_reduct/svd.ipynb | certara-ShengnanHuang/machine-learning | d21dfbeabf2876ffe49fcef444ca4516c4d36df0 | [
"MIT"
] | 10 | 2017-04-07T14:25:23.000Z | 2021-05-18T03:16:15.000Z | dim_reduct/svd.ipynb | certara-ShengnanHuang/machine-learning | d21dfbeabf2876ffe49fcef444ca4516c4d36df0 | [
"MIT"
] | 539 | 2015-12-10T04:23:44.000Z | 2022-03-31T07:15:28.000Z | 49.970727 | 44,236 | 0.628533 | true | 21,016 | Qwen/Qwen-72B | 1. YES
2. YES | 0.715424 | 0.817574 | 0.584912 | __label__eng_Latn | 0.625341 | 0.197278 |
```python
%matplotlib inline
```
```python
# configure matplotlib
import matplotlib as mpl
mpl.rcParams['text.usetex'] = True
import collections
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from chmp.ds import reload
reload('chmp.ds')
from chmp.ds import (
color... | 6deda6e4d4fc0a9a4ec2a88a504e19e9ac8c84af | 354,203 | ipynb | Jupyter Notebook | 20180107-Causality/Notes.ipynb | chmp/misc-exp | 2edc2ed598eb59f4ccb426e7a5c1a23343a6974b | [
"MIT"
] | 6 | 2017-10-31T20:54:37.000Z | 2020-10-23T19:03:00.000Z | 20180107-Causality/Notes.ipynb | chmp/misc-exp | 2edc2ed598eb59f4ccb426e7a5c1a23343a6974b | [
"MIT"
] | 7 | 2020-03-24T16:14:34.000Z | 2021-03-18T20:51:37.000Z | 20180107-Causality/Notes.ipynb | chmp/misc-exp | 2edc2ed598eb59f4ccb426e7a5c1a23343a6974b | [
"MIT"
] | 1 | 2019-07-29T07:55:49.000Z | 2019-07-29T07:55:49.000Z | 651.108456 | 91,380 | 0.946576 | true | 3,112 | Qwen/Qwen-72B | 1. YES
2. YES | 0.685949 | 0.79053 | 0.542264 | __label__eng_Latn | 0.385038 | 0.09819 |
## Example
Let's begin with an example. Suppose that you conduct an opinion survey amongst pilots. In the survey, you ask them basic demographics (gender, race, etc) and whether they agree/disagree with a statement on a scale.
You then have a set of categorical data with which you can compare responses to questions be... | ed77731a54fba15875dfd98f63aa99755c645b61 | 8,669 | ipynb | Jupyter Notebook | Chi_squared.ipynb | rbnsnsd2/quantitative_stats | 620b2b0724fd3486f1d81eb0fb2241020781340b | [
"MIT"
] | null | null | null | Chi_squared.ipynb | rbnsnsd2/quantitative_stats | 620b2b0724fd3486f1d81eb0fb2241020781340b | [
"MIT"
] | null | null | null | Chi_squared.ipynb | rbnsnsd2/quantitative_stats | 620b2b0724fd3486f1d81eb0fb2241020781340b | [
"MIT"
] | null | null | null | 38.02193 | 317 | 0.465106 | true | 1,608 | Qwen/Qwen-72B | 1. YES
2. YES | 0.94079 | 0.843895 | 0.793928 | __label__eng_Latn | 0.993665 | 0.682893 |
# The Efficient Frontier of Optimal Portfolio Transactions
### Introduction
[Almgren and Chriss](https://cims.nyu.edu/~almgren/papers/optliq.pdf) showed that for each value of risk aversion there is a unique optimal execution strategy. The optimal strategy is obtained by minimizing the **Utility Function** $U(x)$:
\... | 08f89eb5fa88620a89b101caf33e7b1fa80714fd | 97,595 | ipynb | Jupyter Notebook | 11. Deep RL for Finance - 1/.ipynb_checkpoints/Efficient Frontier-checkpoint.ipynb | soheillll/reinforcement-learning-tutorials | 5ae57267ce3d806333cd0056ac96d591c8ef7123 | [
"MIT"
] | 4 | 2019-05-27T12:05:16.000Z | 2020-06-08T11:06:34.000Z | 11. Deep RL for Finance - 1/.ipynb_checkpoints/Efficient Frontier-checkpoint.ipynb | soheillll/reinforcement-learning-tutorials | 5ae57267ce3d806333cd0056ac96d591c8ef7123 | [
"MIT"
] | null | null | null | 11. Deep RL for Finance - 1/.ipynb_checkpoints/Efficient Frontier-checkpoint.ipynb | soheillll/reinforcement-learning-tutorials | 5ae57267ce3d806333cd0056ac96d591c8ef7123 | [
"MIT"
] | 2 | 2020-06-30T15:25:29.000Z | 2020-07-23T02:47:08.000Z | 337.698962 | 44,716 | 0.919811 | true | 2,113 | Qwen/Qwen-72B | 1. YES
2. YES | 0.863392 | 0.812867 | 0.701823 | __label__eng_Latn | 0.997415 | 0.468901 |
```python
import sympy as sp
from sympy import *
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d
from scipy.integrate import quad
from scipy.optimize import fmin
import scipy.integrate as integrate
import scipy.special as special
import scipy.stats as st
import sys
font1 = {'si... | 8d0ff2bcf218131b23b4728ea99e3d2d22399750 | 231,854 | ipynb | Jupyter Notebook | plots.ipynb | nebblu/PBH | b896bb65d0f204603e26b3579265d4cd613c48e7 | [
"MIT"
] | null | null | null | plots.ipynb | nebblu/PBH | b896bb65d0f204603e26b3579265d4cd613c48e7 | [
"MIT"
] | null | null | null | plots.ipynb | nebblu/PBH | b896bb65d0f204603e26b3579265d4cd613c48e7 | [
"MIT"
] | null | null | null | 677.935673 | 115,288 | 0.945034 | true | 2,669 | Qwen/Qwen-72B | 1. YES
2. YES | 0.942507 | 0.787931 | 0.74263 | __label__eng_Latn | 0.125516 | 0.563711 |
## Nonlinear Dimensionality Reduction
G. Richards (2016, 2018), based on materials from Ivezic, Connolly, Miller, Leighly, and VanderPlas.
Today we will talk about the concepts of
* manifold learning
* nonlinear dimensionality reduction
Specifically using the following algorithms
* local linear embedding (LLE)
* iso... | 2c2edccf760174bd0d9fd53a94be4e2c419b3864 | 169,730 | ipynb | Jupyter Notebook | notebooks/NonlinearDimensionReduction.ipynb | pranphy/PHYST580-F18 | 52e375765c4b24886b14ce3b08a9c5526da85ff5 | [
"MIT"
] | null | null | null | notebooks/NonlinearDimensionReduction.ipynb | pranphy/PHYST580-F18 | 52e375765c4b24886b14ce3b08a9c5526da85ff5 | [
"MIT"
] | null | null | null | notebooks/NonlinearDimensionReduction.ipynb | pranphy/PHYST580-F18 | 52e375765c4b24886b14ce3b08a9c5526da85ff5 | [
"MIT"
] | null | null | null | 322.680608 | 54,432 | 0.927644 | true | 2,594 | Qwen/Qwen-72B | 1. YES
2. YES | 0.815232 | 0.815232 | 0.664604 | __label__eng_Latn | 0.959201 | 0.382429 |
```python
import numpy as np
```
```python
%%markdown
# Recursion - Examples
## Factorial
\begin{align}
!n &= !(n-1).n \\
!0 &= 1
\end{align}
```
# Recursion - Examples
## Factorial
\begin{align}
!n &= !(n-1).n \\
!0 &= 1
\end{align}
```python
def factorial (n):
if (n >= 1):
return (n-1)*n
else:
... | 725c755d1e15fed5af00ec3354310b3a6cc5b148 | 9,408 | ipynb | Jupyter Notebook | general/recursion-examples.ipynb | machine-learning-helpers/induction-books-python | d26816f92d4f6a64e8c4c2ed6c7c8343c77cd3ad | [
"RSA-MD"
] | 3 | 2018-02-11T12:34:19.000Z | 2021-09-22T18:06:01.000Z | general/recursion-examples.ipynb | machine-learning-helpers/induction-books-python | d26816f92d4f6a64e8c4c2ed6c7c8343c77cd3ad | [
"RSA-MD"
] | 17 | 2019-11-22T00:48:20.000Z | 2022-01-16T11:00:50.000Z | general/recursion-examples.ipynb | machine-learning-helpers/induction-python | 631a735a155f0feb7012472fbca13efbc273dfb0 | [
"RSA-MD"
] | null | null | null | 29.037037 | 249 | 0.426552 | true | 2,220 | Qwen/Qwen-72B | 1. YES
2. YES | 0.661923 | 0.817574 | 0.541171 | __label__eng_Latn | 0.332163 | 0.095652 |
```python
import sympy as sp
x = sp.Symbol('x')
t = sp.Symbol('t')
y = (5*t)*((0.2969*x**0.5)-(0.1260*x)-(0.3516*x**2)+(0.2843*x**3)-(0.1015*x**4))
dy = sp.diff(y,x)
print (dy)
```
5*t*(0.14845*x**(-0.5) - 0.406*x**3 + 0.8529*x**2 - 0.7032*x - 0.126)
```python
```
| 4873ebf19f39af0c4c020fc26ef53801e4269815 | 1,079 | ipynb | Jupyter Notebook | Airfoil Lab/Untitled.ipynb | pantartas/Lab-report | ca1b6722150070f6ecf126a86820418b316b0b7d | [
"MIT"
] | null | null | null | Airfoil Lab/Untitled.ipynb | pantartas/Lab-report | ca1b6722150070f6ecf126a86820418b316b0b7d | [
"MIT"
] | null | null | null | Airfoil Lab/Untitled.ipynb | pantartas/Lab-report | ca1b6722150070f6ecf126a86820418b316b0b7d | [
"MIT"
] | 1 | 2021-12-16T06:32:34.000Z | 2021-12-16T06:32:34.000Z | 18.929825 | 89 | 0.468026 | true | 134 | Qwen/Qwen-72B | 1. YES
2. YES
| 0.956634 | 0.833325 | 0.797187 | __label__yue_Hant | 0.411363 | 0.690465 |
# Input Driven HMM
This notebook is a simple example of an HMM with exogenous inputs. The inputs modulate the probability of discrete state transitions via a multiclass logistic regression. Let $z_t \in \{1, \ldots, K\}$ denote the discrete latent state at time $t$ and $u_t \in \mathbb{R}^U$ be the exogenous input at... | 8f623118b5bbd8c80f29ddcd4c8dfaa5ffc95e17 | 106,383 | ipynb | Jupyter Notebook | notebooks/2 Input Driven HMM.ipynb | nhat-le/ssm | 2f386c04bf7540b0075f40b5d0ae3923296d8bfd | [
"MIT"
] | null | null | null | notebooks/2 Input Driven HMM.ipynb | nhat-le/ssm | 2f386c04bf7540b0075f40b5d0ae3923296d8bfd | [
"MIT"
] | null | null | null | notebooks/2 Input Driven HMM.ipynb | nhat-le/ssm | 2f386c04bf7540b0075f40b5d0ae3923296d8bfd | [
"MIT"
] | null | null | null | 195.198165 | 44,476 | 0.898668 | true | 3,101 | Qwen/Qwen-72B | 1. YES
2. YES | 0.934395 | 0.795658 | 0.743459 | __label__eng_Latn | 0.969825 | 0.565636 |
<center>
</center>
# Non Linear Regression Analysis
Estimated time needed: **20** minutes
## Objectives
After completing this lab you will be able to:
- Differentiate between Linear and non-linear regression
- Use Non-linear regression model in Python
If the data shows a curvy trend, then linear regress... | 14e4b591323540368f1d6b7edbe3027e34c02774 | 172,829 | ipynb | Jupyter Notebook | ML0101EN-Reg-NoneLinearRegression-py-v1.ipynb | naha7789/ML-IBM-Exercise- | f2897bcaa28fa5c8786147416bf1b0e0078a79a5 | [
"BSD-4-Clause-UC"
] | 3 | 2020-12-03T09:19:16.000Z | 2020-12-04T18:02:24.000Z | ML0101EN-Reg-NoneLinearRegression-py-v1.ipynb | naha7789/ML-IBM-Exercise | f2897bcaa28fa5c8786147416bf1b0e0078a79a5 | [
"BSD-4-Clause-UC"
] | null | null | null | ML0101EN-Reg-NoneLinearRegression-py-v1.ipynb | naha7789/ML-IBM-Exercise | f2897bcaa28fa5c8786147416bf1b0e0078a79a5 | [
"BSD-4-Clause-UC"
] | null | null | null | 199.571594 | 18,572 | 0.904651 | true | 3,628 | Qwen/Qwen-72B | 1. YES
2. YES | 0.847968 | 0.805632 | 0.68315 | __label__eng_Latn | 0.956549 | 0.425518 |
# Explicit Methods for the Model Hyperbolic PDE
The one-dimensional wave equation or linear convection equation is given by the following partial differential equation.
$$
\frac{\partial u}{\partial t} + c \frac{\partial u}{\partial x} = 0
$$
When we solve this PDE numerically, we divide the spatial and temporal dom... | 0d3b42f5c5a58015e1efdbc00bf907fca0530b29 | 17,656 | ipynb | Jupyter Notebook | Notebooks/LinearConvection/5-LinearConvection-ExplicitMethods.ipynb | jcschulz/ae269 | 5c467a6e70808bb00e27ffdb8bb0495e0c820ca0 | [
"MIT"
] | null | null | null | Notebooks/LinearConvection/5-LinearConvection-ExplicitMethods.ipynb | jcschulz/ae269 | 5c467a6e70808bb00e27ffdb8bb0495e0c820ca0 | [
"MIT"
] | null | null | null | Notebooks/LinearConvection/5-LinearConvection-ExplicitMethods.ipynb | jcschulz/ae269 | 5c467a6e70808bb00e27ffdb8bb0495e0c820ca0 | [
"MIT"
] | null | null | null | 44.250627 | 743 | 0.555392 | true | 4,058 | Qwen/Qwen-72B | 1. YES
2. YES | 0.930458 | 0.912436 | 0.848984 | __label__eng_Latn | 0.965907 | 0.810807 |
# Model of the potassium A-type (transient) current
*equations taken from Sterratt et al. book*
\begin{equation}
C_m\frac{dV}{dt} = -\bar{g}_{Na}m^3h(V-E_{Na}) - \bar{g}n^4_K(V-E_K) - g_{lk}(V-E_{lk}) - I_A
\end{equation}
\begin{equation}
I_A = \bar{g}_Aa^3b(V-E_A);
\end{equation}
\begin{equation}
a_\infty = \... | 1c1383724116ba207accc70f1660e14d42913877 | 539,568 | ipynb | Jupyter Notebook | A-type current model.ipynb | abrazhe/nbpc | 8465cf8e8db1583b609ce7d8894a840ca728c90d | [
"CC0-1.0"
] | null | null | null | A-type current model.ipynb | abrazhe/nbpc | 8465cf8e8db1583b609ce7d8894a840ca728c90d | [
"CC0-1.0"
] | null | null | null | A-type current model.ipynb | abrazhe/nbpc | 8465cf8e8db1583b609ce7d8894a840ca728c90d | [
"CC0-1.0"
] | null | null | null | 741.164835 | 413,089 | 0.822586 | true | 2,305 | Qwen/Qwen-72B | 1. YES
2. YES | 0.865224 | 0.785309 | 0.679468 | __label__yue_Hant | 0.143338 | 0.416963 |
```python
from sympy import *
init_printing()
from IPython.display import display
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d as a3
import matplotlib.animation as animation
%matplotlib notebook
# specify a second time as a workaround to get interactive plots working
%matplotlib notebook
#reload(gaussian... | c291a93f52283399dd9b07d49dea89203d9cecd3 | 282,716 | ipynb | Jupyter Notebook | Wavefunctions/CuspCorrection.ipynb | QMCPACK/qmc_algorithms | 015fd1973e94f98662149418adc6b06dcd78946d | [
"MIT"
] | 3 | 2018-02-06T06:15:19.000Z | 2019-11-26T23:54:53.000Z | Wavefunctions/CuspCorrection.ipynb | chrinide/qmc_algorithms | 015fd1973e94f98662149418adc6b06dcd78946d | [
"MIT"
] | null | null | null | Wavefunctions/CuspCorrection.ipynb | chrinide/qmc_algorithms | 015fd1973e94f98662149418adc6b06dcd78946d | [
"MIT"
] | 4 | 2017-11-14T20:25:00.000Z | 2022-02-28T06:02:01.000Z | 118.539203 | 34,035 | 0.80739 | true | 6,157 | Qwen/Qwen-72B | 1. YES
2. YES | 0.896251 | 0.812867 | 0.728533 | __label__eng_Latn | 0.35066 | 0.530959 |
# Perron-Frobenius matrix completion
The DGP atom library has several functions of positive matrices, including the trace, (matrix) product, sum, Perron-Frobenius eigenvalue, and $(I - X)^{-1}$ (eye-minus-inverse). In this notebook, we use some of these atoms to formulate and solve an interesting matrix completion pro... | ca2f028039c54fac897fb1173ab73872575b2b2e | 2,907 | ipynb | Jupyter Notebook | examples/notebooks/dgp/pf_matrix_completion.ipynb | jasondark/cvxpy | 56aaa01b0e9d98ae5a91a923708129a7b37a6f18 | [
"ECL-2.0",
"Apache-2.0"
] | 3,285 | 2015-01-03T04:02:29.000Z | 2021-04-19T14:51:29.000Z | examples/notebooks/dgp/pf_matrix_completion.ipynb | h-vetinari/cvxpy | 86307f271819bb78fcdf64a9c3a424773e8269fa | [
"ECL-2.0",
"Apache-2.0"
] | 1,138 | 2015-01-01T19:40:14.000Z | 2021-04-18T23:37:31.000Z | examples/notebooks/dgp/pf_matrix_completion.ipynb | h-vetinari/cvxpy | 86307f271819bb78fcdf64a9c3a424773e8269fa | [
"ECL-2.0",
"Apache-2.0"
] | 765 | 2015-01-02T19:29:39.000Z | 2021-04-20T00:50:43.000Z | 30.6 | 296 | 0.528724 | true | 603 | Qwen/Qwen-72B | 1. YES
2. YES | 0.946597 | 0.857768 | 0.81196 | __label__eng_Latn | 0.940356 | 0.724789 |
Vi har et vektorfelt
$$
\vec{F} = y \mathbf{i} + x \mathbf{j} + z \mathbf{k}
$$
Er vektorfeltet $\vec{F}$ konservativt?
Vi ser på kurveintegralet
$$
\int_{C_i} \vec{F} \cdot d\vec{r}
$$
mellom punktene $A=(0, 0, 0)$ og $B=(1,1,2)$, langs to forskjellige baner gitt ved
$$
C_1 : \begin{cases}
x(t) = t \\
y(... | 48a399a1ad475c65f5805cd1909bc427215948f5 | 167,572 | ipynb | Jupyter Notebook | notebooks/Konservativt vektorfelt.ipynb | mikaem/MEK1100-22 | cddd990347d14983ffc61305182a1810f8af9367 | [
"BSD-2-Clause"
] | 2 | 2022-01-19T23:27:44.000Z | 2022-02-07T12:59:47.000Z | notebooks/Konservativt vektorfelt.ipynb | mikaem/MEK1100-22 | cddd990347d14983ffc61305182a1810f8af9367 | [
"BSD-2-Clause"
] | null | null | null | notebooks/Konservativt vektorfelt.ipynb | mikaem/MEK1100-22 | cddd990347d14983ffc61305182a1810f8af9367 | [
"BSD-2-Clause"
] | null | null | null | 172.221994 | 128,495 | 0.853406 | true | 744 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91848 | 0.872347 | 0.801234 | __label__nob_Latn | 0.185847 | 0.699867 |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content-dl/blob/main/tutorials/W1D2_LinearDeepLearning/student/W1D2_Tutorial1.ipynb" target="_parent"></a>
# Tutorial 1: Gradient Descent and AutoGrad
**Week 1, Day 2: Linear Deep Learning**
**By Neuromatch Academy**
__Content creators:__ Sae... | b8c94ef8370266877a64269a2a6163021685156f | 444,190 | ipynb | Jupyter Notebook | tutorials/W1D2_LinearDeepLearning/student/ED_W1D2_Tutorial1.ipynb | eduardojdiniz/course-content-dl | 8d66641683651bce7b0179b6d890aef5a048a8b9 | [
"CC-BY-4.0",
"BSD-3-Clause"
] | null | null | null | tutorials/W1D2_LinearDeepLearning/student/ED_W1D2_Tutorial1.ipynb | eduardojdiniz/course-content-dl | 8d66641683651bce7b0179b6d890aef5a048a8b9 | [
"CC-BY-4.0",
"BSD-3-Clause"
] | null | null | null | tutorials/W1D2_LinearDeepLearning/student/ED_W1D2_Tutorial1.ipynb | eduardojdiniz/course-content-dl | 8d66641683651bce7b0179b6d890aef5a048a8b9 | [
"CC-BY-4.0",
"BSD-3-Clause"
] | null | null | null | 252.524161 | 256,748 | 0.913251 | true | 11,954 | Qwen/Qwen-72B | 1. YES
2. YES | 0.752013 | 0.689306 | 0.518367 | __label__eng_Latn | 0.875465 | 0.042668 |
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