code stringlengths 2.5k 150k | kind stringclasses 1
value |
|---|---|
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import operator
df_Confirmed=pd.read_csv('/content/drive/My Drive/datasets/Tensorflow community challenge /Datasets /time_series_2019-ncov-Confirmed (1).csv')
df_Confirmed.head()
draft=df_Confirmed.copy()
df_Confirmed.keys... | github_jupyter |
# Introduction
A mass on a spring experiences a force described by Hookes law.
For a displacment $x$, the force is
$$F=-kx,$$
where $k$ is the spring constant with units of N/m.
The equation of motion is
$$ F = ma $$
or
$$ -k x = m a .$$
Because acceleration is the second derivative of displacment, this is
a differ... | github_jupyter |
# Module 5 Lab - Data
```
% matplotlib inline
```
The special command above will make all the `matplotlib` images appear in the notebook.
## Directions
**Failure to follow the directions will result in a "0"**
The due dates for each are indicated in the Syllabus and the course calendar. If anything is unclear, ple... | github_jupyter |
A **Deep Q Network** implementation in tensorflow with target network & random
experience replay. The code is tested with Gym's discrete action space
environment, CartPole-v0 on Colab.
---
## Notations:
Model network = $Q_{\theta}$
Model parameter = $\theta$
Model network Q value = $Q_{\theta}$ (s, a)
Target netw... | github_jupyter |
# Using PS GPIO with PYNQ
## Goal
The aim of this notebook is to show how to use the Zynq PS GPIO from PYNQ. The PS GPIO are simple wires from the PS, and don't need a controller in the programmable logic.
Up to 96 input, output and tri-state PS GPIO are available via the EMIO in the Zynq Ultrascale+. They can be u... | github_jupyter |
### Basics of Tensorflow
```
import numpy as np
from tensorflow.python.layers import base
import tensorflow as tf
import tensorflow.contrib.slim as slim
# To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
# to make... | github_jupyter |
# Example 1d: Spin-Bath model, fitting of spectrum and correlation functions
### Introduction
The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices.
In this example we show the evolution of a single two-level system... | github_jupyter |
```
import random
g = 10 # number of genes
m = 10 # max length of a gene, a gene is a collection of integers "v"
v = 100 # max value for "v"
genome = [[random.randint(0,v) for _ in range(random.randint(0,m))] for _ in range(g)]
genome
def mutate(genome, t='deletion', p=0.1):
if t == 'SNP':
new_genome... | github_jupyter |
```
import pyspark.sql
import pyspark.sql.functions as sf
spark = pyspark.sql.SparkSession.Builder().getOrCreate()
```
# Watson Sales Product Sample Data
In this example, we want to have a look at the pivoting capabilities of Spark. Since pivoting is commonly used with sales data containing information for different ... | github_jupyter |
```
# export
from local.imports import *
from local.notebook.core import *
from local.notebook.export import *
import nbformat,inspect
from nbformat.sign import NotebookNotary
from nbconvert.preprocessors import ExecutePreprocessor
from local.test import *
from local.core import *
# default_exp notebook.test
```
# Ext... | github_jupyter |
```
from scripts.constants import SimulationConstants
from scripts.epidemic_metrics import dead_ratio, infected_ratio
%load_ext autoreload
%autoreload 2
from scripts.simulation import init_run_simulation
import scripts.visualization as viz
viz.load_matplotlib()
plt = viz.plt
contants = SimulationConstants()
metrics =... | github_jupyter |
# Developing an AI application
Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall appli... | github_jupyter |
<a href="https://colab.research.google.com/github/Tessellate-Imaging/Monk_Object_Detection/blob/master/example_notebooks/4_efficientdet/train%20-%20with%20validation%20dataset.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Installation
- Run th... | github_jupyter |
# Generating conditional probability tables subject to constraints
```
import os
from pathlib import Path
from itertools import product
import numpy as np
import pandas as pd
from fake_data_for_learning.fake_data_for_learning import (
BayesianNodeRV, FakeDataBayesianNetwork, SampleValue
)
from fake_data_for_lea... | github_jupyter |
```
# python David_2_2_2_train_detector.py --class "../../../CV_PyImageSearch/Dataset/Chapter_Specific/chp2_2_stop_sign/stop_sign_images --annotations ../../../CV_PyImageSearch/Dataset/Chapter_Specific/chp2_2_stop_sign/stop_sign_annotations --output ../../../CV_PyImageSearch/Dataset/Chapter_Specific/chp2_2_stop_sign/ou... | github_jupyter |
# Spatiotemporal distribution of AxFUCCI cells
```
# Required libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import scipy
import os
import seaborn as sns
from pyabc import (Distribution, History)
# Experimental data
outgrowth_df = pd.read_csv('./outgrowth.csv')
out... | github_jupyter |
```
import scanpy as sc
import pandas as pd
import xarray as xr
from numpy import ones
from pandas_plink import read_plink1_bin
from numpy.linalg import cholesky
import matplotlib.pyplot as plt
import time
from limix.qc import quantile_gaussianize
import cellregmap
cellregmap
from cellregmap import estimate_betas
mydir... | github_jupyter |
# Set up Azure ML Automated Machine Learning on SQL Server 2019 CTP 2.4 big data cluster
\# Prerequisites:
\# - An Azure subscription and resource group
\# - An Azure Machine Learning workspace
\# - A SQL Server 2019 CTP 2.4 big data cluster with Internet access and a database named 'automl'
\# - Azure C... | github_jupyter |
```
import itertools
from fractions import Fraction
from typing import Tuple, Sequence
from pprint import pprint
import numpy as np
import scipy.stats as st
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import theano as th
import theano.tensor as T
import pymc3 as pm
from hypothesis impor... | github_jupyter |
(parallel)=
# Parallelization
```
%config InlineBackend.figure_format = "retina"
from matplotlib import rcParams
rcParams["savefig.dpi"] = 100
rcParams["figure.dpi"] = 100
rcParams["font.size"] = 20
import multiprocessing
multiprocessing.set_start_method("fork")
```
:::{note}
Some builds of NumPy (including the ... | github_jupyter |
```
#Note: You need to reset the kernel for the keras installation to take place
#Todo: Remove this line once it is installed, reset the kernel: Menu > Kernel > Reset & Clear Output
!git clone https://github.com/fchollet/keras.git && cd keras && python setup.py install --user
import keras
from keras.applications.incept... | github_jupyter |
# MAT281 - Laboratorio Nยฐ01
<a id='p1'></a>
## Problema 01
### a) Calcular el nรบmero $\pi$
En los siglos XVII y XVIII, James Gregory y Gottfried Leibniz descubrieron una serie infinita que sirve para calcular $\pi$:
$$\displaystyle \pi = 4 \sum_{k=1}^{\infty}\dfrac{(-1)^{k+1}}{2k-1} = 4(1-\dfrac{1}{3}+\dfrac{1}{5}... | github_jupyter |
# Depression Detection in Social Media Posts
#### Imports
```
import warnings
warnings.filterwarnings("ignore")
import ftfy
import matplotlib.pyplot as plt
import nltk
import numpy as np
import pandas as pd
import re
from math import exp
from numpy import sign
from sklearn.metrics import classification_report, con... | github_jupyter |
# Index Day/Night - Run in python
This only needs to be done once
```
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
import numpy as np
import cmocean
import warnings
from matplotlib import rcParams
from mpl_toolkits.axes_grid1 import make_axes_locatable
warnings.filterwarnings('i... | github_jupyter |
# Feldman and Cousins intervals with asymptotics.
This is a copy of `FC_interval_freq.ipynb` using the asymptotic formulae instead of toys.
```
import numpy as np
import matplotlib.pyplot as plt
import os
import time
import zfit
from zfit.loss import UnbinnedNLL
from zfit.minimize import Minuit
zfit.settings.set_see... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import xarray as xr
import cartopy.crs as ccrs
import glob
import os
import scipy.stats
from matplotlib import cm
import seaborn as sns
import dask
import pickle
from datetime import datetime
import ast
from dask.distributed import Client, Local... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
```
%load_ext watermark
%watermark -p torch,pytorch_lightning,torchvision,torchmetrics,matplotlib
%load_ext pycodestyle_magic
%flake8_on --ignore W291,W293,E703
```
<a href="https://pytorch.org"><img src="https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg" width="90"... | github_jupyter |
# Investigate HPS profile
```
%matplotlib inline
import sys
sys.path.append('/home/surchs/Repositories/abide_univariate/')
import asdfc
import pandas as pd
import scipy as sp
import numpy as np
import patsy as pat
import nibabel as nib
import pathlib as pal
import seaborn as sbn
import matplotlib as mpl
from matplotli... | github_jupyter |
## Drawing Edgeworth Box
```
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
%matplotlib inline
def Edgeworth_box(u1, u2, util1, util2, MRS1, MRS2, ttl, ttl_margin= 1, top=0.7, fname=None):
l1 = 0.00000001
x1 = np.arange(l1, u1, 0.01)
x2 = np.arange(l1, u2, 0.01)... | github_jupyter |
```
import os
import networkx as nx
import pandas as pd
import numpy as np
import pickle as pkl
import itertools
from sklearn.manifold import TSNE
import stellargraph as sg
from stellargraph import StellarGraph
from stellargraph import globalvar
from stellargraph.mapper import GraphSAGENodeGenerator
from stellargraph... | github_jupyter |
## Test monthly means with leap year for GNSS-RO data
```
import xarray as xr
import matplotlib.pyplot as plt
import numpy as np
from netCDF4 import num2date
import func as func
ds_obs = xr.open_dataset('GPS-RO__CP_LR_5x5_2007-2018.nc')
ds_era5 = xr.open_dataset('FULL-ERA5.monthmean.2007-2018.concat_new.nc')
ds_erai =... | github_jupyter |
# Transforming Vertical Coordinates
A common need in the analysis of ocean and atmospheric data is to transform the vertical coordinate from its original coordinate (e.g. depth) to a new coordinate (e.g. density).
Xgcm supports this sort of one-dimensional coordinate transform on `Axis` and `Grid` objects using the `t... | github_jupyter |
# Transpose of a Matrix
In this set of exercises, you will work with the transpose of a matrix.
Your first task is to write a function that takes the transpose of a matrix. Think about how to use nested for loops efficiently.
The second task will be to write a new matrix multiplication function that takes advantage ... | github_jupyter |
# 1์ฅ ํ๋์ ๋ณด๋ ๋จธ์ ๋ฌ๋
#### ๊ฐ์ฌ์ ๊ธ
์๋ฃ๋ฅผ ๊ณต๊ฐํ ์ ์ ์ค๋ ๋ฆฌ์ ์ ๋กฑ๊ณผ ๊ฐ์์๋ฃ๋ฅผ ์ง์ํ ํ๋น์์นด๋ฐ๋ฏธ์๊ฒ ์ง์ฌ์ด๋ฆฐ ๊ฐ์ฌ๋ฅผ ์ ํฉ๋๋ค.
## 1.1 ๋จธ์ ๋ฌ๋์ด๋?
- ์์ ์๋ฎค์ผ(Artuhr Samuel), 1959
> ๋จธ์ ๋ฌ๋์ **๋ช
์์ ์ธ ํ๋ก๊ทธ๋๋ฐ** ์์ด ์ปดํจํฐ๊ฐ ํ์ตํ๋ ๋ฅ๋ ฅ์ ๊ฐ์ถ๊ฒ ํ๋ ์ฐ๊ตฌ ๋ถ์ผ
- ํฐ ๋ฏธ์ฒผ(Tom Michell), 1977
> ์ด๋ค ์์
T์ ๋ํ ์ปดํจํฐ ํ๋ก๊ทธ๋จ์ ์ฑ๋ฅ์ P๋ก ์ธก์ ํ์ ๋
> ๊ฒฝํ E๋ก ์ธํด ์ฑ๋ฅ์ด ํฅ์๋์๋ค๋ฉด,
> ์ด ์ปดํจํฐ ํ๋ก๊ทธ๋จ์ __์์
T์ ์ฑ๋ฅ ์ธก์ P์ ๋ํด ๊ฒฝํ E๋ก๋ถํฐ ํ์ตํ... | github_jupyter |
```
#Setup and imports
import pandas as pd
import numpy as np
df = pd.read_csv('raw/jantojun2020.csv', dtype=object)
existing = pd.read_csv('raw/airlines-corgis.csv')
#For looking at our new dataset
df.head(5).iloc[:,0:]
#For looking at the old dataset
existing.head(5)
existing.iloc[0:10, 5:]
#Start similar dataframe f... | github_jupyter |
#### ฮ ฮกฮฮฃฮฮงฮ:
ฮคฮฑ joblib dumps ฯฯฮฝ ฯฮตฮปฮนฮบฯฮฝ `corpus_tf_idf.pkl` ฮบฮฑฮน `som.pkl` ฮดฮตฮฝ ฯฮตฯฮนฮญฯฮฟฮฝฯฮฑฮน ฯฯฮฟ zip file ฮบฮฑฮธฯฯ ฮตฮฏฯฮฑฮฝ ฮฑฯฮฑฮณฮฟฯฮตฯ
ฯฮนฮบฮฌ ฮผฮตฮณฮฌฮปฮฟ ฮผฮญฮณฮตฮธฮฟฯ. ฮฯ
ฯฯ ฮฮฮ ฮฟฯฮตฮฏฮปฮตฯฮฑฮน ฯฮต ฮดฮนฮบฮฎ ฮผฮฑฯ ฮตฮปฮปฮนฯฮฎ ฯ
ฮปฮฟฯฮฟฮฏฮทฯฮท, ฮฑฮปฮปฮฌ ฯฮต ฮผฮนฮฑ ฮนฮดฮนฮฟฮผฮฟฯฯฮฏฮฑ ฯฮฟฯ
corpus ฯฮฟฯ
ฮผฮฑฯ ฮฑฮฝฯฮนฯฯฮฟฮนฯฮตฮฏ ฮบฮฑฮน ฮฑฮฝฮฑฮณฮบฮฌฮถฮตฮน ฮฟฯฮนฯฮผฮญฮฝฮฟฯ
ฯ ฯฮฏฮฝฮฑฮบฮตฯ ฮฝฮฑ ฮฑฮฝฯฮนฯฯฮฟฮนฯฮฏฮถฮฟฮฝฯฮฑฮน ฮฑฯฯฮตฮฏฮฑฯฯฮฑ ฯฮต float... | github_jupyter |
### Abstract
This is an example to show to use use the basic API of TensorFlow, to construct a linear regression model.
This notebook is an exercise adapted from [the Medium.com blog](https://medium.com/@saxenarohan97/intro-to-tensorflow-solving-a-simple-regression-problem-e87b42fd4845).
Note that recent version of... | github_jupyter |
# High Accuracy CNN for MNIST
### Build your own CNN from scratch and try to achieve the highest possible accuracy on MNIST.
```
# Exercise: Build your own CNN from scratch and try to achieve the highest possible accuracy on MNIST.
# he following model uses 2 convolutional layers, followed by 1 pooling layer, then dr... | github_jupyter |
# [Module 2.1] ์ธ์ด์ง ๋ฉ์ด์ปค ๋ก์ปฌ ๋ชจ๋ ๋ฐ ์คํฌ๋ฆฝํธ ๋ชจ๋๋ก ํ๋ จ
๋ณธ ์ํฌ์ต์ ๋ชจ๋ ๋
ธํธ๋ถ์ **<font color="red">conda_tensorflow2_p36</font>** ๋ฅผ ์ฌ์ฉํฉ๋๋ค.
์ด ๋
ธํธ๋ถ์ ์๋์ ๊ฐ์ ์์
์ ํฉ๋๋ค.
- 1. ๊ธฐ๋ณธ ํ๊ฒฝ ์ธํ
- 2. ๋
ธํธ๋ถ์์ ์ธ์ด์ง ๋ฉ์ด์ปค ์คํฌ๋ฆฝํธ ๋ชจ๋ ์คํ์ผ๋ก ์ฝ๋ ๋ณ๊ฒฝ
- 3. ์ธ์ด์ง ๋ฉ์ด์ปค ๋ก์ปฌ ๋ชจ๋๋ก ํ๋ จ
- 4. ์ธ์ด์ง ๋ฉ์ด์ปค์ ํธ์คํธ ๋ชจ๋๋ก ํ๋ จ
- 5. ๋ชจ๋ธ ์ํฐํํธ ๊ฒฝ๋ก ์ ์ฅ
---
# 1. ๊ธฐ๋ณธ ํ๊ฒฝ ์ธํ
์ฌ์ฉํ๋ ํจํค์ง๋ import ์์ ์ ๋ค์ ์ฌ๋ก๋ฉ ํฉ๋๋ค.
``... | github_jupyter |
**This notebook is an exercise in the [AI Ethics](https://www.kaggle.com/learn/ai-ethics) course. You can reference the tutorial at [this link](https://www.kaggle.com/alexisbcook/ai-fairness).**
---
In the tutorial, you learned about different ways of measuring fairness of a machine learning model. In this exercise... | github_jupyter |
# Parsing Text and the LDA output
## a.1) Opening pdfs and extracting their text
Under the material for Lecture 3 I have added a folder called FOMC_pdf. This folder contains the transcripts of all the meetings that took place during the [Greenspan](https://en.wikipedia.org/wiki/Alan_Greenspan) era (August 11, 1987 to... | github_jupyter |
## 03 Intro to PyTorch
*special thanks to YSDA team for provided materials*
What comes today:
- Introduction to PyTorch
- Automatic gradient computation
- Logistic regression (it's a neural network, actually ;) )
__This notebook__ will teach you to... | github_jupyter |
Please find jax implementation of this notebook here: https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/book1/15/attention_jax.ipynb
<a href="https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/attention_torch.ipynb" target="_parent"><img src="https://colab.resea... | github_jupyter |
<a href="https://colab.research.google.com/github/yukinaga/lecture_pytorch/blob/master/lecture4/cnn.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# CNNใฎๅฎ่ฃ
PyTorchใไฝฟใฃใฆใ็ณใฟ่พผใฟใใฅใผใฉใซใใใใฏใผใฏ๏ผCNN๏ผใๅฎ่ฃ
ใใพใใ
CNN่ชไฝใฏCNNใฎๅฑคใ่ฟฝๅ ใใใฎใฟใงๅฎ่ฃ
ๅฏ่ฝใชใฎใงใใใไปๅใฏใใผใฟๆกๅผตใจใใญใใใขใฆใใฎๅฎ่ฃ
ใ่ก... | github_jupyter |
# Generative Adversarial Network
In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits!
GANs were [first reported on](https://arxiv.org/abs/1406.2661) in 2014 from Ian Goodfellow and others in Yoshua Bengio'... | github_jupyter |
# YES BANK DATATHON
## Machine Learning Challenge Round 3 - EDA
### Data Description
The data given is of credit records of individuals with certain attributes.
```
import numpy as np
import pandas as pd
import seaborn as sns
%matplotlib inline
import matplotlib.pyplot as plt
t... | github_jupyter |
```
%matplotlib inline
from jax.config import config; config.update("jax_enable_x64", True)
import jax.numpy as jnp
from jax import grad, jit, value_and_grad
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import ticker, colors
@jit
def loss_lik(mu,v):
b1 = 0.5; b2 = 0.01;
a1 = 2.0; a2 =... | github_jupyter |
Week 5 Notebook: Building a Deep Learning Model
===============================================================
Now, we'll look at a deep learning model based on low-level track features.
```
import tensorflow.keras as keras
import numpy as np
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt... | github_jupyter |
```
#Estre programa contรฉm os cรณdigo demonstrados na aula sobre Aplicaรงรฃo de ML+IoT para o Healthcare(previsรฃo de arritmia)
#importando o banco de dados a ser utilizado (comando necessรกrio para o google colab)
from google.colab import files
uploaded = files.upload()
#importando as bibliotecas
import pandas as pd #bibli... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from tqdm import tqdm
from astropy.table import Table
import astropy.units as u
import os
# Using `batman` to create & fit fake transit
import batman
# Using astropy BLS and scipy curve_fit to fit transit
from astropy.timeseries import BoxLeas... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
# Import Dependencies
```
import warnings
warnings.filterwarnings('ignore')
import keras
import matplotlib.pyplot as plt
```
## Define Types
```
from typing import Tuple
ImageShape = Tuple[int, int]
GrayScaleImageShape = Tuple[int, int, int]
```
# MNIST Sandbox Baseline Example
This sandbox example is meant mostly... | github_jupyter |
```
import os
import glob
import math
import time
import pandas as pd
import numpy as np
import scipy as sc
from sklearn.model_selection import KFold
import warnings
import matplotlib.pyplot as plt
import matplotlib
from sklearn.model_selection import train_test_split
from torch.utils.data import TensorDataset, DataLoa... | github_jupyter |
# Introduction
So... you've got some interesting property of the network uncovered. Is it real? How can you trust that what you've found **didn't arise by random chance**?
One useful way of thinking by using generative models of random graphs. By "generative" and "random", we mean that the graph was generated using s... | github_jupyter |
```
import matplotlib
#matplotlib.use('Agg')
%matplotlib tk
%autosave 180
import matplotlib.pyplot as plt
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
import matplotlib.cm as cm
from matplotlib import gridspec
import parmap
import numpy as np
impo... | github_jupyter |
# Learn Python for Economic Computation: A Crash Course
https://github.com/jlcatonjr/Learn-Python-for-Stats-and-Econ
> James Caton
>
> james.caton@ndsu.edu
>
> Cameron Harwick
>
> charwick@brockport.edu
### Table of Contents
[Introduction](https://github.com/jlcatonjr/Learn-Python-for-Stats-and-Econ/blob/master/Te... | github_jupyter |
# ์ ๊ท ํํ์ ์์ํ๊ธฐ
## ์ ๊ท ํํ์์ ๊ธฐ์ด, ๋ฉํ ๋ฌธ์
์ ๊ท ํํ์์์ ์ฌ์ฉํ๋ ๋ฉํ ๋ฌธ์(meta characters)์๋ ๋ค์๊ณผ ๊ฐ์ ๊ฒ์ด ์๋ค.
โป ๋ฉํ ๋ฌธ์๋ ์๋ ๊ทธ ๋ฌธ์๊ฐ ๊ฐ์ง ๋ป์ด ์๋ ํน๋ณํ ์ฉ๋๋ก ์ฌ์ฉํ๋ ๋ฌธ์๋ฅผ ๋งํ๋ค.
- ์ฌ์ฉํ๋ ๋ฉํ ๋ฌธ์:
. ^ $ * + ? { } [ ] \ | ( )
์ ๊ท ํํ์์ ์ ๋ฉํ ๋ฌธ์๋ฅผ ์ฌ์ฉํ๋ฉด ํน๋ณํ ์๋ฏธ๋ฅผ ๊ฐ๊ฒ ๋๋ค.
์, ๊ทธ๋ฌ๋ฉด ๊ฐ์ฅ ๊ฐ๋จํ ์ ๊ท ํํ์๋ถํฐ ์์ํด ๊ฐ ๋ฉํ ๋ฌธ์์ ์๋ฏธ์ ์ฌ์ฉ๋ฒ์ ์์๋ณด์.
## ๋ฌธ์ ํด๋์ค [ ]
... | github_jupyter |
```
# Import libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib as mpl
from scipy import stats
import statsmodels.api as sm
import warnings
from itertools import product
from datetime import datetime
warnings.filterwarnings('ignore')
plt.style.use('s... | github_jupyter |
# Abstract Data Structures: Thinking recursively
## 5.1.1 Identify a situation that requires the use of recursive thinking
What is this recursive thing? It's a very practical way of establishing an abstract pattern in our thinking. There are a class of problems that require thinking in a more abstract manner, and try... | github_jupyter |
# Creating a Sentiment Analysis Web App
## Using PyTorch and SageMaker
_Deep Learning Nanodegree Program | Deployment_
---
Now that we have a basic understanding of how SageMaker works we will try to use it to construct a complete project from end to end. Our goal will be to have a simple web page which a user can u... | github_jupyter |
\title{Digital Latches with myHDL}
\author{Steven K Armour}
\maketitle
# Refs
@book{brown_vranesic_2014, place={New York, NY}, edition={3}, title={Fundamentals of digital logic with Verilog design}, publisher={McGraw-Hill}, author={Brown, Stephen and Vranesic, Zvonko G}, year={2014} },
@book{lameres_2017, title={Intro... | github_jupyter |
```
%%capture
# { display-mode: 'form' }
# @title PyTTI-Tools [EzMode]: VQGAN
# @markdown ## Setup
# @markdown This may take a few minutes.
## 1. Install stuff
try:
import pytti
except ImportError:
!pip install kornia pytorch-lightning transformers
!pip install jupyter loguru einops PyGLM ftfy regex ... | github_jupyter |
# Example Script for Logistic Regression for Fishing Detection
```
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from patsy import dmatrices
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn import metrics
f... | github_jupyter |
<img src="../../images/banners/python-basics.png" width="600"/>
# <img src="../../images/logos/python.png" width="23"/> Python Program Lexical Structure
You have now covered Python variables, operators, and data types in depth, and youโve seen quite a bit of example code. Up to now, the code has consisted of short i... | github_jupyter |
# Load Data From Snowflake
## Overview
<a href="https://www.snowflake.com/" target='_blank' rel='noopener'>Snowflake</a> is a data platform built for the cloud that allows for fast SQL queries. This ... | github_jupyter |
```
%matplotlib inline
```
Spatial Transformer Networks Tutorial
=====================================
**Author**: `Ghassen HAMROUNI <https://github.com/GHamrouni>`_
.. figure:: /_static/img/stn/FSeq.png
In this tutorial, you will learn how to augment your network using
a visual attention mechanism called spatial t... | github_jupyter |
```
%matplotlib inline
import torch
import torch.nn as nn
import matplotlib.pyplot as pt
import numpy as np
device = torch.device('cpu')
# if torch.cuda.is_available() > 0:
# print("Using GPU")
# device = torch.device('cuda')
# else:
# print("Using CPU")
# Sizes of layers and batch size
n_in, n_h, n_out =... | github_jupyter |
<a href="https://colab.research.google.com/github/PyTorchLightning/lightning-flash/blob/master/flash_notebooks/image_classification.ipynb" target="_parent">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
In this notebook, we'll go over the basics of lightning Flash b... | github_jupyter |
```
import pandas as pd
import pathlib
# Store filepath in a variable
GDP_path = pathlib.Path("../Extraction/GDP_per_capita.csv")
# Read and display the CSV with Pandas
GDP_file_df = pd.read_csv(GDP_path, encoding="ISO-8859-1")
GDP_file_df
# Delete columns: 1960 through 2004, Indicator Name, and Indicator Code)
GDP_Dr... | github_jupyter |
# Symbolic Regression
This example combines neural differential equations with regularised evolution to discover the equations
$\frac{\mathrm{d} x}{\mathrm{d} t}(t) = \frac{y(t)}{1 + y(t)}$
$\frac{\mathrm{d} y}{\mathrm{d} t}(t) = \frac{-x(t)}{1 + x(t)}$
directly from data.
**References:**
This example appears as ... | github_jupyter |
```
# %cd /Users/Kunal/Projects/TCH_CardiacSignals_F20/
from numpy.random import seed
seed(1)
import numpy as np
import os
import matplotlib.pyplot as plt
import tensorflow
tensorflow.random.set_seed(2)
from tensorflow import keras
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.regularizers ... | github_jupyter |
```
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.decomposition import PCA # Principal Component Analysis module
from sklearn.cluster import KMeans # KMeans clustering
import matplotlib.pyplot as plt # Python defacto plotting library
import seab... | github_jupyter |
# Lecture 3: Birthday Problem, Properties of Probability
## The Birthday Problem
Given $k$ people, what is the probability of at least 2 people having the same birthday?
First, we need to define the problem:
1. there are 365 days in a year (no leap-years)
1. births can be on any day with equal probability (birthdays... | github_jupyter |
# Web Data Extraction (1)
by Dr Liang Jin
- Step 1: access crawler.idx files from SEC EDGAR
- Step 2: re-write crawler data to csv files
- Step 3: retrieve 10K filing information including URLs
- Step 4: read text from html
## Step 0: Setup...
```
# import packages as usual
import os, requests, csv, webbrowser
from ... | github_jupyter |
```
import pandas as pd
import numpy as np
from numpy import linalg
import matplotlib.pyplot as plt
import seaborn as sbn
from scipy.stats import invgamma
import logging
from notebookutils import root_dir; root_dir()
from model.utils import read_clean_kv17, read_testdata1, read_party_keys, matrix, vector, party_name_fr... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
from astropy import units as u
from astroduet.bbmag import bb_abmag_fluence
from astroduet.image_utils import construct_image, find
from astroduet.config import Telescope
from astroduet.background import background_pixel_rate
fr... | github_jupyter |
# Reinforcement learning
In this Python notebook, we will have you implement a simple reinforcement learning agent for the AI gym mountain car problem. Please first have a look at the description of the task here: <A HREF="https://github.com/openai/gym/wiki/MountainCar-v0" TARGET="_blank">Description</A>
## Heuristi... | github_jupyter |
```
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writi... | github_jupyter |
# Parsing Inputs
In the chapter on [Grammars](Grammars.ipynb), we discussed how grammars can be
used to represent various languages. We also saw how grammars can be used to
generate strings of the corresponding language. Grammars can also perform the
reverse. That is, given a string, one can decompose the string into ... | github_jupyter |
<a href="https://colab.research.google.com/github/txusser/Master_IA_Sanidad/blob/main/Modulo_2/2_3_3_Extraccion_de_caracteristicas.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Extracciรณn de caracterรญsticas
## Anรกlisis de la componente principa... | github_jupyter |
# Multi-linear regression: how many variables?
[](https://github.com/eabarnes1010/course_objective_analysis/tree/main/code)
[](https://colab.research.google.com/github/eabar... | github_jupyter |
## Keras implementation of https://phillipi.github.io/pix2pix
```
import os
os.environ['KERAS_BACKEND']='theano' # can choose theano, tensorflow, cntk
os.environ['THEANO_FLAGS']='floatX=float32,device=cuda,optimizer=fast_run,dnn.library_path=/usr/lib'
#os.environ['THEANO_FLAGS']='floatX=float32,device=cuda,optimizer=f... | github_jupyter |
# Practical session 1 - Some Python basics
Course: [SDIA-Python](https://github.com/guilgautier/sdia-python)
Dates: 09/21/2021-09/22/2021
Instructor: [Guillaume Gautier](https://guilgautier.github.io/)
Students (pair):
- [Hadrien SALEM]([link](https://github.com/SnowHawkeye))
- [Emilie SALEM]([link](https... | github_jupyter |
```
import gc
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import torch
import torch.nn as nn
import torchvision
# To view tensorboard metrics
# tensorboard --logdir=logs --port=6006 --bind_all
from torch.utils.tensorboard import SummaryWriter
from functools import partial
from evol... | github_jupyter |
The $k$-Means Algorithm
====================
Again, we start by generating some artificial data:
```
import numpy as np
import matplotlib.pyplot as plt
#from IPython.display import HTML
# This line tells the notebook to show plots inside of the notebook
%matplotlib inline
plt.jet() # set the color map. When your colo... | github_jupyter |
# Exploring Data with Python
A significant part of a a data scientist's role is to explore, analyze, and visualize data. There's a wide range of tools and programming languages that they can use to do this; and of of the most popular approaches is to use Jupyter notebooks (like this one) and Python.
Python is a flexi... | github_jupyter |
<a href="https://colab.research.google.com/github/mohd-faizy/CAREER-TRACK-Data-Scientist-with-Python/blob/main/Police_Activity_data_for_analysis.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
---
<strong>
<h1 align='center'>Preparing the Po... | github_jupyter |
# Workshop: Deep Learning 3
Outline
1. Regularization
2. Hand-Written Digits with Convolutional Neural Networks
3. Advanced Image Classification with Convolutional Neural Networks
Source: Deep Learning With Python, Part 1 - Chapter 4
## 1. Regularization
To prevent a model from learning misleading or irrelevant ... | github_jupyter |
<center>
<img src="https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png" width="300" alt="cognitiveclass.ai logo" />
</center>
# Density-Based Clustering
Estimated time needed: **25** minutes
## Objectives
After completing this lab you w... | github_jupyter |
```
from pyspark import SparkContext, SparkConf
from pyspark.sql import SQLContext, SparkSession
from pyspark.sql.types import StructType, StructField, DoubleType, IntegerType, StringType
from ibm_botocore.client import Config
import ibm_boto3
import os
from pyspark.sql.functions import unix_timestamp, to_date
import ... | github_jupyter |
```
from math import sqrt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# for the ML pipeline
from sklearn.model_selection import train_test_split
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.ensemble import GradientBoostingRegressor
f... | github_jupyter |
```
prefix = 'ens'
midx = '66'
import pandas as pd
import numpy as np
from sklearn.metrics import f1_score, confusion_matrix
new = pd.read_csv('leak2d.csv')
new = new[new.Questionable != 1]
new.reset_index(inplace=True)
print(new.head())
print(new.shape)
ens = pd.read_csv('sub/'+prefix+midx+'.csv').fillna('')
ens = en... | github_jupyter |
# ORF MLP
Trying to fix bugs.
NEURONS=128 and K={1,2,3}.
```
import time
def show_time():
t = time.time()
print(time.strftime('%Y-%m-%d %H:%M:%S %Z', time.localtime(t)))
show_time()
PC_TRAINS=8000
NC_TRAINS=8000
PC_TESTS=8000
NC_TESTS=8000
RNA_LEN=1000
MAX_K = 3
INPUT_SHAPE=(None,84) # 4^3 + 4^2 + 4... | github_jupyter |
```
import xarray as xr
import pandas as pd
import pickle as pk
import re
import requests
import os
import gc
# Sensor E: url = 'https://opendap.oceanobservatories.org/thredds/catalog/ooi/dax.soule@qc.cuny.edu/20181104T104012-RS03ECAL-MJ03E-06-BOTPTA302-streamed-botpt_nano_sample/catalog.html'
# Sensor B url = 'https:/... | github_jupyter |
```
import google.datalab.bigquery as bq
import numpy as np
from sklearn.metrics import mean_squared_error, mean_absolute_error
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
#training price data
training = bq.Query('''
Select date_utc,price
from Energy.MarketPT
where date_utc between '2015... | github_jupyter |
# Inter-annotator agreement between the first 10 annotators of WS-353
Measured in Kappa and Rho:
- against the gold standard which is the mean of all annotators, as described in Hill et al 2014 (footnote 6)
- against each other
Using Kohen's kappa, which is binary, so I average across pairs of annotators.
```
%cd... | github_jupyter |
# **Applied Deep Learning Tutorial**
## **Transfer Learning for Object Classification**
### **Imports**
Import the necessary libraries and load the Dogs vs Cats dataset from Kaggle.
```
from __future__ import absolute_import, division, print_function, unicode_literals
import os
try:
# Use the %tensorflow_versi... | github_jupyter |
# General Funds
```
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
import mwdsbe
import schuylkill as skool
import time
```
## Data
```
registry = mwdsbe.load_registry() # geopandas df
gf = pd.read_excel(r'C:\Users\dabinlee\Documents\GitHub\mwdsbe_binny\MWDSBE\mwdsbe\data\cwedp_37_rep... | github_jupyter |
# Introduรงรฃo
```
import os
import re
import time
import json
import folium
import random
import requests
import numpy as np
import pandas as pd
import seaborn as sns
import geopandas as gpd
from folium import plugins
from osgeo import gdal, osr
from bs4 import BeautifulSoup
from tqdm.notebook import trange, tqdm
```
... | github_jupyter |
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