Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

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

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

Last synced: 7 days ago
JSON representation

A collection of IPython notebooks covering various topics.

Lists

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