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# Bandits from Logged Data
- M. Dudik J. Langford, and L. Li, "[Doubly Robust Policy Evaluation and Learning](https://arxiv.org/abs/1103.4601)" (2017).
- A. Swaminathan and T. Joachims, "[The Self-Normalized Estimator for Counterfactual Learning](https://www.microsoft.com/en-us/research/publication/self-normalized-es... | github_jupyter |
```
import numpy as np
import copy
import matplotlib.pyplot as plt
%matplotlib nbagg
from chickpea import Waveform, Element, Sequence, Segment
```
## Introducing: Segments
A segment is part of a 'waveform' which represents a time slice on one channel of waveform generator with up to two markers also specified. Many s... | github_jupyter |
# Семинар 10
# Оптимизация на множествах простой структуры
## На прошлом семинаре...
- Метод Ньютона
- Квазиньютоновские методы
## Методы решения каких задач уже известны или скоро будут известны
- Безусловная минимизация: функция достаточно гладкая, но ограничений на аргумент нет.
- Линейное программирование: лине... | github_jupyter |
### Thickness budget in temperature space
```
%load_ext autoreload
%autoreload 2
import xarray as xr
import numpy as np
from matplotlib import pyplot as plt
import budgetcalcs as bc
import calc_wmt as wmt
import datetime
#import cftime
rootdir = '/archive/gam/MOM6-examples/ice_ocean_SIS2/Baltic_OM4_025/1yr/'
averaging... | github_jupyter |
ML Course, Bogotá, Colombia (© Josh Bloom; June 2019)
```
%run ../talktools.py
```
## Generative Adversarial Networks
One of the downsides of VAEs is that the generated samples are interpolated between real samples as you walk through the latent space. This can lead to unrealistic looking images (what's half w... | github_jupyter |
# Data Preparation: MCI Patient Selection
ADNIMERGE patient selection according to Massi's R screening file.
This notebook is to serve as to get familiar with the ADNI dataset, the ADNIMERGE file, and select the MCI patients of interest for our models.
Massi used the RID variable to see which rows refers to the same ... | github_jupyter |
### Loading common data formats
[**Neal Caren**](mailto:neal.caren@gmail.com)
University of North Carolina, Chapel Hill
It is possible to turn many sorts of data into a Pandas dataframe for subsequent anaysis.
The most basic method is reading comma-delimited text files, or csv files. This is accomplished with t... | github_jupyter |
# Machine Learning API
> ICDSS Machine Learning Workshop Series: Coding Models on `scikit-learn`, `keras` & `fbprophet`
* [Pipeline](#pipeline)
* [Preprocessing](#pipe:preprocessing)
* [Estimation](#pipe:estimation)
* [Supervised Learning](#pipe:supervised-learning)
* [Unsupervised Learning](#... | github_jupyter |
This notebook contains an example of using `redbiom` through it's Python API to extract a subset of American Gut Project samples. These data are then loaded into QIIME 2 for a mini beta-diversity analysis using UniFrac. This assumes we're using a QIIME 2 2018.11 environment that additionally has `redbiom` 0.3.0 install... | github_jupyter |
# Update Vaccination Data in ArcGIS Online from GitHub
This script will help you automatically update the data within your ArcGIS Online account that we uploaded from the [Publishing Vaccination Data from GitHub to ArcGIS](Get to Publish World Vaccination Data.ipynb) tutorial.
The only thing you need to change **if ... | github_jupyter |
# Black Scholes Exercise 6: MPI implementation
Use MPI to parallelize and distribute the work
```
# Boilerplate for the example
import cProfile
import pstats
import numpy as np
try:
import numpy.random_intel as rnd
except:
import numpy.random as rnd
# make xrange available in python 3
try:
xrange
except... | github_jupyter |
```
import warnings
warnings.filterwarnings("ignore")
import torchaudio as ta
ta.set_audio_backend("sox_io")
import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.nn.functional as F
import torch.autograd.profiler as profiler
# import pytorch_lightning as pl
import numpy as np
import ... | github_jupyter |
```
%matplotlib inline
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import glob
import pickle
import operator
import matplotlib
import scipy.stats as stats
import statsmodels.stats.multitest as multi
from itertools import chain
from sklearn.preprocessing import ... | github_jupyter |
# Azure Monitor Log Analytics Workspace Summary
Get a birds-eye view of the utilization and cost of your Log Analytics workspaces.
## Parameters
**resource_filter**: Optional KQL where clause to limit Azure Monitor workspace resources in scope.
```
resource_filter = None
```
## Setup
```
from azmeta.access import... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
```
### Scatterplot Cost Model
```
from numpy import dtype
cardinality = {
'dummyfloat1': 1,
'dummyfloat2': 1,
'id': 48895,
'name': 47906,
'host_id': 37457,
'host_name': 11453,
'neighbourhood_group': 5,
'neighbourhood': 221,
'latitude... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
from fetch import get_onedrive_directlink, fetch_beijing_AQ_data, fetch_flue_gas_data
```
## Beijing Air Quality Dataset
https://archive.ics.uci.edu/ml/datasets/Beijing+Multi-Site+Air-Quality+Data... | github_jupyter |
# Display a circuit that runs a Teleport circuit
This sample visualizes the trace of a quantum program that runs a Teleport circuit.
First, import the widget:
```
from quantum_viz import Viewer
```
The below cell creates a trace for a Teleport circuit.
```
teleport = {
"qubits": [
{
"id": 0... | github_jupyter |
```
import tensorflow as tf
import numpy as np
import keras
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.utils import shuffle
import os
import cv2
import random
import keras.backend as K
import sklearn
%matplotlib inline
# train_no = int(len(train_data)*0.9)
# training_data = train_data[:train_no]
#... | github_jupyter |
# _*Vehicle Routing*_
## The Introduction
Logistics is a major industry, with some estimates valuing it at USD 8183 billion globally in 2015. Most service providers operate a number of vehicles (e.g., trucks and container ships), a number of depots, where the vehicles are based overnight, and serve a number of client... | github_jupyter |
# Residual Analysis
By Chris Fenaroli and Max Margenot
Part of the Quantopian Lecture Series:
* [www.quantopian.com/lectures](https://www.quantopian.com/lectures)
* [github.com/quantopian/research_public](https://github.com/quantopian/research_public)
---
## Linear Regression
Linear regression is one of our m... | github_jupyter |
```
import pandas as pd
import numpy as np
import scanpy as sc
import os
from sklearn.cluster import KMeans
from sklearn.cluster import AgglomerativeClustering
from sklearn.metrics.cluster import adjusted_rand_score
from sklearn.metrics.cluster import adjusted_mutual_info_score
from sklearn.metrics.cluster import homog... | github_jupyter |
```
import pandas as pd
import numpy as np
import os
import pickle
import platform
from sklearn.preprocessing import StandardScaler
from mabwiser.mab import MAB, LearningPolicy
from mabwiser.linear import _RidgeRegression, _Linear
class LinTSExample(_RidgeRegression):
def predict(self, x):
if self.scaler ... | github_jupyter |
```
import numpy as np
```
### Create an array from an iterable
Such as
- ```list```
- ```tuple```
- ```range``` iterator
Notice that not all iterables can be used to create a numpy array, such as ```set``` and ```dict```
```
arr = np.array([1,2,3,4,5])
print(arr)
arr = np.array((1,2,3,4,5))
print(arr)
arr = np.arra... | 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 |
<h1> Getting started with TensorFlow </h1>
In this notebook, you play around with the TensorFlow Python API.
```
import tensorflow as tf
import numpy as np
print tf.__version__
```
<h2> Adding two tensors </h2>
First, let's try doing this using numpy, the Python numeric package. numpy code is immediately evaluated... | 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 |
##### Copyright 2019 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 |
# KDD Cup 1999 Data
http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
```
import pandas as pd
import matplotlib.pyplot as pyplot
from sklearn import datasets
import sklearn.preprocessing as sp
from sklearn.externals import joblib
% matplotlib inline
```
|ファイル名|ファイル内容|
|---|---|
|kddcup.data|フルデータ|
|kddcup.dat... | github_jupyter |
```
import pandas as pd
import numpy as np
from google.colab import files
uploaded = files.upload()
data_pd = pd.read_csv('WA_Fn-UseC_-Telco-Customer-Churn.csv', index_col=False)
df = data_pd
for col_name in df.columns:
if(df[col_name].dtype == 'object'):
df[col_name]= df[col_name].astype('category')
... | github_jupyter |
<a href="https://colab.research.google.com/github/keivanipchihagh/Google-ML-Crash-Course/blob/master/NumPy_UltraQuick_Tutorial.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
#@title Copyright 2020 Google LLC. Double-click here for license infor... | github_jupyter |
[](https://colab.research.google.com/github/Rishit-dagli/TFUG-Mysuru-2020/blob/master/TFQuantum_starter.ipynb)
# Getting started with [TensorFlow Quantum](https://www.tensorflow.org/quantum)
In this notebook you will build your first hybrid qua... | github_jupyter |
# Matrix Factorization for Recommender Systems - Part 1
**Table of contents of this tutorial series on matrix factorization for recommender systems:**
- [Part 1 - Traditional Matrix Factorization methods for Recommender Systems](/examples/matrix-factorization-for-recommender-systems-part-1)
- [Part 2 - Factorization ... | github_jupyter |
# Preparing the data
```
# Importing the libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Loading the data
# https://archive.ics.uci.edu/ml/datasets/statlog+(australian+credit+approval)
df = pd.read_csv("Credit_Card_Applications.csv")
df.head()
# Split the data
X = df.iloc[:, :-1].va... | github_jupyter |
<img src="https://firebasestorage.googleapis.com/v0/b/deep-learning-crash-course.appspot.com/o/Logo.png?alt=media&token=06318ee3-d7a0-44a0-97ae-2c95f110e3ac" width="100" height="100" align="right"/>
## 4 Neural Networks in TensorFlow - Advanced Techniques
<img src="https://firebasestorage.googleapis.com/v0/b/deep-lea... | github_jupyter |
# Nettoyage du text
Etapes de néttoyage:
1. Mettre en minuscle le texte
2. Enlever les contractions
3. Enlever les espaces
4. Tokeniser le texte
5. Lemmatiser les mots
6. Enlever les éléments ininteréssant (chiffres, ponctuation, mots non anglais et lettres isolées)
```
import pickle
import re
import string
import wa... | github_jupyter |
# First steps with `funflow`
## Introduction
`funflow` is a Haskell library for defining and running _workflows_.
A workflow specifies a pipeline of _tasks_ structured in a Direct Acyclic Graph (DAG).
Workflows in `funflow` have the great property of being __composable__ which means that you can easily share and ... | github_jupyter |
# Create a month/day by Year view of the daily sea ice index data.
```
tmp_dir = "../data"
!mkdir -p ../data
!wget -P ../data -qN ftp://sidads.colorado.edu/pub/DATASETS/NOAA/G02135/north/daily/data/NH_seaice_extent_final.csv
!wget -P ../data -qN ftp://sidads.colorado.edu/pub/DATASETS/NOAA/G02135/north/daily/data/NH_se... | github_jupyter |
# Geodatenhandling 2
**Inhalt:** Geopandas für Fortgeschrittene
**Nötige Skills**
- Basic pandas skills
- Funktionen und pandas
- Erste Schritte mit Geopandas
- Geodatenhandling 1
**Lernziele**
- Punkte, Linien, Polygone revisited
- Eigenschaften von geometrischen Shapes
- Shapes modifizieren und kombinieren
- Geoda... | github_jupyter |
# 1. DATA TYPES
<a name='variables'></a>Variables
===
A variable holds a value.
Python automatically assign type to a variable based on the values
<a name='example'></a>Example
---
```
message = "Hello Python world!"
print(message)
type(message)
```
A variable holds a value. You... | github_jupyter |
##### Copyright 2020 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 |
# Basic Bayesian Linear Regression Implementation
```
# Pandas and numpy for data manipulation
import pandas as pd
import numpy as np
# Matplotlib and seaborn for visualization
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
# Linear Regression to verify implementation
from sklearn.linear_... | github_jupyter |
# Padim Example
#### Import dependencies
```
import os
import anodet
import numpy as np
import torch
import cv2
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
```
#
## Training
In this notebook the MVTec dataset will be used. It can be downloaded from: https://www.mvtec.com/company/resear... | github_jupyter |
# Flax Basics
This notebook will walk you through the following workflow:
* Instantiating a model from Flax built-in layers or third-party models.
* Initializing parameters of the model and manually written training.
* Using optimizers provided by Flax to ease training.
* Serialization of parameters and other... | github_jupyter |
```
# Install TensorFlow
# !pip install -q tensorflow-gpu==2.0.0-beta1
try:
%tensorflow_version 2.x # Colab only.
except Exception:
pass
import tensorflow as tf
print(tf.__version__)
# More imports
from tensorflow.keras.layers import Input, SimpleRNN, GRU, LSTM, Dense, Flatten
from tensorflow.keras.models import... | github_jupyter |
# Harmonizome ETL: CORUM
Created by: Charles Dai <br>
Credit to: Moshe Silverstein
Data Source: http://mips.helmholtz-muenchen.de/corum/#download
```
# appyter init
from appyter import magic
magic.init(lambda _=globals: _())
import sys
import os
from datetime import date
import numpy as np
import pandas as pd
impor... | github_jupyter |
# Spam Filtering Using The [Enron Dataset][1]
[1]: http://www.aueb.gr/users/ion/data/enron-spam/
```
from pymldb import Connection
mldb = Connection('http://localhost/')
```
Let's start by loading the dataset. We have already merged the different email files in a sensible manner into a .csv file, which we've made ava... | github_jupyter |
```
#for Manupulation
import numpy as np
import pandas as pd
#for visulaization
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
#For reading the csv file
df = pd.read_csv('TRAIN (2).csv')
df.head()
df.shape
#Creating copy of Train Data Set
data = df.copy()
df.isnull().sum()
df.describe().tra... | github_jupyter |
# Data Analysis
## Link to data: https://www.kaggle.com/fedesoriano/company-bankruptcy-prediction
```
# Import packages
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from pprint import pprint
from pickle import dump
from ran... | github_jupyter |
##### Copyright 2019 The TF-Agents 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 a... | github_jupyter |
```
# https://www.tensorflow.org/extend/estimators
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# tensorflow
import tensorflow as tf
import tensorflow.contrib.rnn as rnn
import tensorflow.contrib.learn as tflearn
import tensorflow.contrib.layers as tflaye... | github_jupyter |
Copyright © 2020, SAS Institute Inc., Cary, NC, USA. All Rights Reserved.
SPDX-License-Identifier: Apache-2.0
# Fleet Maintenance: Build and Import Trained Models into SAS Model Manager
This notebook provides an example of how to build and train a Python model and then import the model into SAS Model Manager using t... | github_jupyter |
# Quantum jump duration estimation from direct deconvolution of signal
```
import matplotlib.pyplot as plt
import numpy as np
from uncertainties import unumpy
from uncertainties import ufloat
%matplotlib inline
```
## Signal import and frequency analysis
```
data_file = 'data/raw_data/selected_g2/20170529_FWMg2_MP... | github_jupyter |
So far, we've only studied word embeddings, where each word is represented by a vector of numbers. For instance, the word cat might be represented as
```python
cat = [0.23, 0.10, -0.23, -0.01, 0.91, 1.2, 1.01, -0.92]
```
But how would you represent a **sentence**? There are many different ways to represent sentences... | github_jupyter |
```
# Makes print and division act like Python 3
from __future__ import print_function, division
# Import the usual libraries
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
# Enable inline plotting
%matplotlib inline
from IPython.display import display, Lat... | github_jupyter |
## Lesson 6 - Taking Input, Reading and Writing Files, Functions
### Readings
* Shaw: [Exercises 11-26](https://learnpythonthehardway.org/python3/ex11.html)
* Lutz: Chapters 9, 14-17
### Table of Contents
* [Taking Input](#input)
* [Reading Files](#reading)
* [Writing Files](#writing)
* [Functions](#functions)
<a ... | github_jupyter |
```
! git clone https://github.com/singhnaveen098/Hamoye_capstone_project_smote.git
train_path = 'Hamoye_capstone_project_smote/Data/train/'
val_path = 'Hamoye_capstone_project_smote/Data/val/'
test_path = 'Hamoye_capstone_project_smote/Data/test/'
import tensorflow as tf
import os
import matplotlib.pyplot as plt
impor... | github_jupyter |
# Building a Regression Model for a Financial Dataset
In this notebook, you will build a simple linear regression model to predict the closing AAPL stock price. The lab objectives are:
* Pull data from BigQuery into a Pandas dataframe
* Use Matplotlib to visualize data
* Use Scikit-Learn to build a regression model
`... | github_jupyter |
```
%matplotlib inline
```
# Comparing random forests and the multi-output meta estimator
An example to compare multi-output regression with random forest and
the `multioutput.MultiOutputRegressor <multiclass>` meta-estimator.
This example illustrates the use of the
`multioutput.MultiOutputRegressor <multiclass>` ... | github_jupyter |
## Overview of the dataframe
```
import pandas
#Use pandas to read data from csv file into dataframe
#Parse_dates tells df to read particular column as a datetime object column
df = pandas.read_csv("reviews.csv", parse_dates=["Timestamp"])
#Access the first 5 elements using .head()
df.head()
#Tells us shape of the dat... | github_jupyter |
# Update the image WCS and coordinates from file using Gaia
This notebook shows how to use <a href="https://docs.astropy.org/en/stable/wcs/index.html">astropy.wcs</a> and <a href="https://astroquery.readthedocs.io/en/latest/gaia/gaia.html">astroquery.gaia</a> to update an image WCS using Gaia DR3.<br>
The updated WCS ... | github_jupyter |
```
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pylab as plt
import matplotlib
from luescher_nd.database.utilities import DATA_FOLDER
matplotlib.use("pgf")
sns.set(
context="paper",
style="ticks",
font_scale=1/1.7,
rc={
# "mathtext.fontset": "cm",
... | github_jupyter |
# Computação vetorizada
## Objetivos
- Compreender as capacidades da computação vetorizada;
- Associar conceitos abstratos de Matemática a estruturas computacionais;
- Saber estruturar dados em arrays multidimensionais;
## Introdução
A computação científica é uma ciência interdisciplinar que procura resolver probl... | github_jupyter |
# Figure 3
Run the steps below to generate the data and plot of Figure 3.
**Lennart van Sluijs** // 2019 Jan 8 // Leiden Observatory // vansluijs@strw.leidenuniv.nl
```
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import numpy as np
from astropy.io import fits
from sts_class import SpectralTimeSeries
fr... | github_jupyter |
# CTC Language Model
<div class="alert alert-info">
This tutorial is available as an IPython notebook at [malaya-speech/example/ctc-language-model](https://github.com/huseinzol05/malaya-speech/tree/master/example/ctc-language-model).
</div>
<div class="alert alert-warning">
This module is not language independ... | github_jupyter |
# Convolutional Autoencoder
In this example we will demonstrate how you can create a convolutional autoencoder in Gluon
```
import random
import matplotlib.pyplot as plt
import mxnet as mx
from mxnet import autograd, gluon
```
## Data
We wi... | github_jupyter |
```
import open3d as o3d
import numpy as np
import copy
import os
import sys
# monkey patches visualization and provides helpers to load geometries
sys.path.append('..')
import open3d_tutorial as o3dtut
# change to True if you want to interact with the visualization windows
o3dtut.interactive = not "CI" in os.environ
... | github_jupyter |
# AutoKeras
**This code worked with the AutoKeras version in Nov 2019 and has since been depreciated. Please refer to the June 2020 version [code/chapter-5/5-autokeras.ipynb](https://github.com/PracticalDL/Practical-Deep-Learning-Book/blob/master/code/chapter-5/5-autokeras.ipynb)**
As AI is automating more and more ... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import torch
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import Dataset, DataLoader
fro... | github_jupyter |
# Further topics
We want to list some further topics of current interest in Scientific Computing that we did not cover in this module. The list can not be exhaustive and there will be things of importance that I am leaving out. But it should give some pointers for those who are interested in diving more into the resea... | github_jupyter |
# Notebook 3: Bayesian Statistics
[Bayesian Decision Analysis](https://allendowney.github.io/BayesianDecisionAnalysis/)
Copyright 2021 Allen B. Downey
License: [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/)
```
import numpy as np
import... | github_jupyter |
# <font color=blue>Assignments for "Data Exploration - Multivariate Analysis"</font>
In this assignment, you will work on the `Students Performance` ([dataset](https://www.kaggle.com/spscientist/students-performance-in-exams/home)). You can reach the explanations of this data from Kaggle again.
To complete this assig... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
# load voter info data
voter_info = pd.read_csv('voter_info.csv', sep='\t')
# bar graph of the mean number of times moved for each age group
age_groups = [18, 26, 33, 40, 50, 150];
ages = np.ones([5, 2])
mean_address_counts = np.zeros(5)
for i i... | github_jupyter |
Test runs for Task1 of the shared task
```
import pickle
import json
from collections import Counter
import pandas as pd
import pickle
import re
import numpy as np
from collections import Counter, defaultdict, OrderedDict
from nltk import word_tokenize, pos_tag
import editdistance
import csv
from sklearn.metrics ... | github_jupyter |
# Self-Driving Car Engineer Nanodegree
## Deep Learning
## Project: Build a Traffic Sign Recognition Classifier
In this notebook, a template is provided for you to implement your functionality in stages, which is required to successfully complete this project. If additional code is required that cannot be included i... | github_jupyter |
```
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import numpy.linalg as la
```
# Dimension Reduction
```
np.random.seed(123)
np.set_printoptions(3)
```
### PCA from scratch
Principal Components Analysis (PCA) basically means to find and rank all the eigenvalues and ... | github_jupyter |
# Latent Dirichlet Allocation Demo
## Import dependencies
```
import os
import time
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.datasets import fetch_20newsgroups
from sklearn.manifold import TSNE
import bokeh.plotting as bp
from bokeh.plotting import save
from bokeh.mo... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
import healpy as hp
import sys
sys.path.append('/Users/mehdi/github/LSSutils')
from LSSutils import dataviz as dv
from glob import glob
plt.rc('font', family='serif', size=15)
from LSSutils.catalogs.datarelease import cols_dr8_rand as labels
!ls -lt ../pk*zbin1*.tx... | github_jupyter |
# Operators
> API details.
```
#default_exp operators
#export
from smpr3d.kernels import *
import torch as th
import numpy as np
import math as m
import numba.cuda as cuda
#export
def calc_psi(r, t, z, out):
out[:] = 0
K = r.shape[0]
MY, MX = out.shape
gpu = cuda.get_current_device()
threadsperbl... | github_jupyter |
# Better ML Engineering with ML Metadata
## Learning Objectives
1. Download the dataset
2. Create an InteractiveContext
3. Construct the TFX Pipeline
4. Query the MLMD Database
## Introduction
Assume a scenario where you set up a production ML pipeline to classify penguins. The pipeline ingests your training da... | github_jupyter |
# Getting Started with Matplotlib
We need `matplotlib.pyplot` for plotting.
```
import matplotlib.pyplot as plt
import pandas as pd
```
## About the Data
In this notebook, we will be working with 2 datasets:
- Facebook's stock price throughout 2018 (obtained using the [`stock_analysis` package](https://github.com/ste... | github_jupyter |
# 机器学习工程师纳米学位
## 模型评价与验证
## 项目 1: 预测波士顿房价
欢迎来到机器学习工程师纳米学位的第一个项目！在此文件中，有些示例代码已经提供给你，但你还需要实现更多的功能来让项目成功运行。除非有明确要求，你无须修改任何已给出的代码。以**'练习'**开始的标题表示接下来的内容中有需要你必须实现的功能。每一部分都会有详细的指导，需要实现的部分也会在注释中以**'TODO'**标出。请仔细阅读所有的提示！
除了实现代码外，你还**必须**回答一些与项目和实现有关的问题。每一个需要你回答的问题都会以**'问题 X'**为标题。请仔细阅读每个问题，并且在问题后的**'回答'**文字框中写出完整的答案。你的项目将会根... | github_jupyter |
<a href="https://colab.research.google.com/github/PytorchLightning/pytorch-lightning/blob/master/notebooks/03-basic-gan.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# PyTorch Lightning Basic GAN Tutorial ⚡
How to train a GAN!
Main takeaways:
1.... | github_jupyter |
<a href="https://colab.research.google.com/github/LucasDatilioCarderelli/Maratona_BehindTheCode_IBM20/blob/main/Desafio%206/DF6_Lit_2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Install
```
!pip install tpot
import pandas as pd
import numpy a... | github_jupyter |
```
#hide
import ds4se.facade as facade
import pandas as pd
```
# ds4se
> Data Science for Software Engieering (ds4se) is an academic initiative to perform exploratory analysis on software engineering artifacts (e.g., requirements, issues, source code, or test cases) and metadata (e.g., repository logs, databases log... | github_jupyter |
# Develop Deep Learning Models for Natural Language in Python
## Chapter 10 - Project: Develop a Neural Bag-of-Words Model for Sentimental Analysis
```
import re
import os
import numpy as np
from random import shuffle
```
### 10.4 - Bag-of-Words ReprRepresentation
#### Load Data
```
# Most Data preperation was don... | github_jupyter |
# Tutorial Part 10: Exploring Quantum Chemistry with GDB1k
Most of the tutorials we've walked you through so far have focused on applications to the drug discovery realm, but DeepChem's tool suite works for molecular design problems generally. In this tutorial, we're going to walk through an example of how to train a ... | github_jupyter |
```
!apt-get install -y -qq software-properties-common python-software-properties module-init-tools
!add-apt-repository -y ppa:alessandro-strada/ppa 2>&1 > /dev/null
!apt-get update -qq 2>&1 > /dev/null
!apt-get -y install -qq google-drive-ocamlfuse fuse
from google.colab import auth
auth.authenticate_user()
from oauth... | github_jupyter |
# Parallel Recursive Filtering of Infinite Input Extensions
## All functions needed and defined by the paper are in this notebook
### It also includes original functions from previous papers
#### This an auxiliary notebook, it runs from other notebooks, it depends on the following imports: math; cmath; numpy as np; sci... | github_jupyter |
**Chapter 4 – Training Linear Models**
_This notebook contains all the sample code and solutions to the exercices in chapter 4._
# Setup
First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figur... | github_jupyter |
# NeMo voice swap demo
This notebook shows how to use NVIDIA NeMo (https://github.com/NVIDIA/NeMo) to construct a toy demo which will swap a voice in the audio fragment with a computer generated one.
At its core the demo does:
* Automatic speech recognition of what is said in the file. E.g. converting audio to text
... | github_jupyter |
<table align="left">
<td>
<a target="_blank" href="https://colab.research.google.com/github/ShopRunner/collie/blob/main/tutorials/02_matrix_factorization.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" /> Run in Google Colab</a>
</td>
<td>
<a target="_blank" href="https://github.co... | github_jupyter |
```
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from datetime import datetime
from datetime import timedelta
sns.set()
df = pd.read_csv('../dataset/GOOG-year.csv')
date_ori = pd.to_datetime(df.iloc[:,... | github_jupyter |
```
import sys
sys.path.append('../scripts/')
from kf import * #誤差楕円を描くのに利用
def make_ax(): #axisの準備
fig = plt.figure(figsize=(4,4))
ax = fig.add_subplot(111)
ax.set_aspect('equal')
ax.set_xlim(-5,5)
ax.set_ylim(-5,5)
ax.set_xlabel("X",fontsize=10)
ax.set_ylabel("Y",fon... | github_jupyter |
# An example of how to plot result with the result .csv file
## Importations and configurations
```
%matplotlib notebook
import matplotlib as plt
plt.interactive(True)
import numpy as np
import sys
sys.path.append("../")
from source import functions
func = functions.Comparison()
import datetime
import pandas as p... | github_jupyter |
```
%matplotlib inline
import numpy as np
import itertools
import tensorflow as tf
from six.moves import cPickle as pickle
from six.moves import range
import random
import matplotlib.pyplot as plt
from tensorflow.contrib.layers import flatten
from PIL import Image, ImageOps
from scipy.ndimage.interpolation import shift... | github_jupyter |
# Introduction
This notebook includes experiments on auxiliary learning. Please see the corresponding [repository](https://github.com/vivien000/auxiliary-learning) and the associated blog post.
```
# Set to True to save the experiments' results on Google Drive
google_drive = True
#@title Tensorboard launch and utili... | github_jupyter |
# Settings
```
EXP_NO = 19
SEED = 1
N_SPLITS = 5
TARGET = 'target'
GROUP = 'art_series_id'
REGRESSION = True
assert((TARGET, REGRESSION) in (('target', True), ('target', False), ('sorting_date', True)))
```
# Library
```
from collections import defaultdict
from functools import partial
import gc
import glob
import j... | github_jupyter |
```
import numpy as np
import scipy as sp
import scipy.stats
import itertools
import logging
import matplotlib.pyplot as plt
import pandas as pd
import torch.utils.data as utils
import math
import time
import tqdm
import torch
import torch.optim as optim
import torch.nn.functional as F
from argparse import ArgumentPar... | github_jupyter |
# Futures long-only performance summary
This notebook summarises long-only performance statistics for major futures contracts provided by various data sources. Daily returns are computed by rolling the front contract before either the first notice day or the last trade day to avoid deliveries. Concretely, a daily retu... | github_jupyter |
<a href="https://colab.research.google.com/github/hendradarwin/covid-19-prediction/blob/master/series-dnn_and_rnn/Forecast_3._rnn.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## Pediction New Death Cases Global Covid-19 Cases
## Load Data and Im... | github_jupyter |
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