https://github.com/jdwittenauer/ipython-notebooks

A collection of IPython notebooks covering various topics.
https://github.com/jdwittenauer/ipython-notebooks

Last synced: about 1 month ago
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A collection of IPython notebooks covering various topics.

• Host: GitHub
• URL: https://github.com/jdwittenauer/ipython-notebooks
• Owner: jdwittenauer
• Created: 2014-06-01T00:14:43.000Z (over 10 years ago)
• Default Branch: master
• Last Pushed: 2020-10-19T12:44:35.000Z (about 4 years ago)
• Last Synced: 2024-08-03T02:10:24.352Z (4 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 32.5 MB
• Stars: 2,608
• Watchers: 171
• Forks: 1,514
• Open Issues: 9
• Metadata Files:
  • Readme: README.md

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• awesome-starred - ipython-notebooks - A collection of IPython notebooks covering various topics. (Jupyter Notebook)

README

        
ipython-notebooks

========================



This repo contains various IPython notebooks I've created to experiment with libraries and work through exercises, and explore subjects that I find interesting.  I've included notebook viewer links below.  Click the link to see a live rendering of the notebook.

#### Language

These notebooks contain introductory content such as an overview of the language and a review of IPython's functionality.

Introduction To Python


IPython Magic Commands

#### Libraries

Examples using a variety of popular "data science" Python libraries.

NumPy


SciPy


Matplotlib


Pandas


Statsmodels


Scikit-learn


Seaborn


NetworkX


PyMC


NLTK


DEAP


Gensim

#### Machine Learning Exercises

Implementations of the exercises presented in Andrew Ng's "Machine Learning" class on Coursera.

Exercise 1 - Linear Regression


Exercise 2 - Logistic Regression


Exercise 3 - Multi-Class Classification


Exercise 4 - Neural Networks


Exercise 6 - Support Vector Machines


Exercise 7 - K-Means Clustering & PCA


Exercise 8 - Anomaly Detection & Recommendation Systems

#### Tensorflow Deep Learning Exercises

Implementations of the assignments from Google's Udacity course on deep learning.

Assignment 1 - Intro & Data Prep


Assignment 2 - Regression & Neural Nets


Assignment 3 - Regularization


Assignment 4 - Convolutions


Assignment 5 - Word Embeddings


Assignment 6 - Recurrent Nets

#### Spark Big Data Labs

Lab exercises for the original Spark classes on edX.

Lab 0 - Learning Apache Spark


Lab 1 - Building A Word Count Application


Lab 2 - Web Server Log Analysis


Lab 3 - Text Analysis & Entity Resolution


Lab 4 - Introduction To Machine Learning


ML Lab 3 - Linear Regression


ML Lab 4 - Click-Through Rate Prediction


ML Lab 5 - Principal Component Analysis

### Fast.ai Lessons

Notebooks from Jeremy Howard's fast.ai class.

Lesson 1 - Image Classification


Lesson 2 - Multi-label Classification


Lesson 3 - Structured And Time Series Data


Lesson 4 - Sentiment Classification


Lesson 5 - Recommendation Using Deep Learning


Lesson 6 - Language Modeling With RNNs


Lesson 7 - Convolutional Networks In Detail

### Deep Learning With Keras

Notebooks using Keras to implement deep learning models.

Part 1 - Structured And Time Series Data


Part 2 - Convolutional Networks


Part 3 - Recommender Systems


Part 4 - Recurrent Networks


Part 5 - Anomaly Detection


Part 6 - Generative Adversarial Networks

#### Misc

Notebooks covering various interesting topics!

Comparison Of Various Code Optimization Methods


A Simple Time Series Analysis of the S&P 500 Index


An Intro To Probablistic Programming


Language Exploration Using Vector Space Models


Solving Problems With Dynamic Programming


Time Series Forecasting With Prophet


Markov Chains From Scratch


A Sampling Of Monte Carlo Methods