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# # Project I: Parameter estimation for a toy model of... |
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import warnings
warnings.filterwarnings('ignore')
# 통... |
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# + [markdown] id="view-in-github" colab_type=... |
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# # Logistic Regression
# ## Generate data
# +
impor... |
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# #### Models used from https://github.com/fonnesbeck/... |
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# + [markdown] papermill={"duration": 0.0, "end_time":... |
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# + [markdown] id="lWUi9G6FKonD"
# # Kolmo... |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... |
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# Tarea 4
# Con base a los métodos vistos ... |
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# # WeatherPy
# -... |
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# #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"... |
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y = [5, 5.5, 5, 5.5, 6, 6.5, 6, 6.5, 7, 5.5, 5.25, 6, ... |
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# +
# %matplotlib inline
from glob import glob
impor... |
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from ast import literal_eval
from os import listdi... |
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# # Sample with isets
# +
# %matplotlib inline
import... |
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# ## Optimized texture calculation
#
# Noteboo... |
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# + [markdown] id="J-D0IcTiUmIP"
# Item-based collaborative filtering with ra... |
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run "C:\Users\<NAME>\Google Drive\school\Graduate\FPF ... |
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from projectq.ops... |
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# **Consumer Theory for Cheap Information**
#
# My job... |
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# # Practice with Datasets
import numpy a... |
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# # Realestate Analysis
#
# **A deep look into the rea... |
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# + This notebook is part of lecture 24 *Markov matric... |
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#THIS NOTEBOOK CONTAINS ESTIMATES ON THE NUMBER OF... |
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# # Introductory junk on root finding
# Root finding ... |
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# # Distribuições
#
# A estatística descritiva é limit... |
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# # uWell. VLP
#
# Vertical lift perfomance - vertical... |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... |
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# # Homework 2 - Soccer analytics
#
# ### Libraries
#... |
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# + slideshow={"slide_type": ""}
#pip inst... |
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import pandas as pd
from sklearn.linear_mo... |
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#mapping some polygons -
import pandas as pd
import g... |
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# + [markdown] nbsphinx="hidden"
# # Characterization ... |
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# ### Modules we will use today: nume... |
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# # Results - CIViC smMIPs panel rescues clinically re... |
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# + [markdown] id="b57ba0a3"
# # LSTM - Random Paramet... |
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# # Sine Wave Mixing
#
# Adding sine ... |
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# # The Graph Data Access
#
# In this notebook, we rea... |
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# %pylab inline
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#
# <a id='multi-hyper-v7'></a>
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# ## Quantum Graph Reccurent Neural Network in Pennyla... |
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# + [markdown] id="view-in-github" colab_type="text"
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# + deletable=false editable=false nbgrader={"cell_typ... |
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# + [markdown] _uuid="d21ae5d68a0b39c29977b8fb1db12916... |
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# Sveučilište u Zagrebu
# Fakultet elektrotehnike i ... |
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# + [markdown] colab_type="text" id="8z1JzXCtxhXb"
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# # Basic 1-dimensional ODE solution
# This is a stan... |
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import torch
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# +
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import csv
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import os, sys
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# # CS109A Project Group 21
# # Baseline Model
#
# ***... |
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# + id="wxS5hmyWsin0" colab_type="code" colab={"base_u... |
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# # 微積分の計算について N0.4 定積分の内容－3 ＜定積分の部分積分＞
# ### 学籍番号... |
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# + [markdown] id="6NcOpFighczd"
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# + id="MopELn5rXoWq"
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# # FEATURE ENGINEERING
# ## ON NUMERICAL DATA
# Var... |
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# # HW 1 - Getting started with Python
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# # Taller 5
# #### Reading data of power consumption... |
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# ## Create 2D spectrogram dataset from 1D raw data
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# # Lab 7 Exercises for COMP 432 Machine Learning
#
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import numpy as np
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# <script async src="https://www.googletag... |
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# # The Whole Game
#
# Some defs:
#
# | What | Formula... |
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# # Danish fire insurance dataset
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# # B-splines FEM solver for Poisson equation (... |
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# # Human Brain samples - MS Nature 2019 Rowitch datas... |
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import pandas as pd
import numpy as np
from numpy impo... |
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# https://nbviewer.jupyter.org/github/entiretydotai/Meetup-Content/blob/master/Neural_Network/7_Optimizers.ipynb
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import scipy.sparse
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# # Data Science Mathematics
# # Simple Linear Regress... |
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import sympy
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# # CD4 CROP-seq Analysis
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import pandas as pd
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import torch
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# # "Differentiation and Integration with SymPy"
# > "... |
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# # Three-sat atmuso on Swarm
# <NAME> (Institute for ... |
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# # Differential Gene Expression
# +
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# # Analysis of Anscombes Quartet
# In this Jupyter ... |
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# # BPM ESTIMATION ON REAL VIDEO
# Explanation of th... |
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# + [markdown] id="aXhLRq5rpJtk"
# # Mount Drive
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Compute acreage per cultivar for double peak d... |
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# # Fit models for strong enhancer vs. silencer
# This... |
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