Applied Machine Learning & NLP

Probably useful code for doing Data Science or so.

Under (permanent) construction

Intuition/Fundamentals

1. Machine Learning is fun! - A really nice machine learning intro, a topic that actually needs an intro. By Adam Geitgey.
2. Intuition for Simulated Annealing - Shake!. By Robb Seaton.
3. Everything You Wanted to Know about the Kernel Trick (But Were Too Afraid to Ask). By Eric Kim.
4. Principal Component Analysis (PCA) vs Ordinary Least Squares (OLS): A Visual Explanation - By J.D. Long
5. Deep Learning, NLP, and Representations - By C. Olah
6. Markov Chains - A visual explanation. By Lewis Lehe.
7. Neural Networks and Deep Learning - By Micheal Nielsen. A great online book on neural networks.
8. A Beginner’s Guide to Eigenvectors, PCA, Covariance and Entropy - by Skymind. The most intuitive introduction to Eigenvectors and Eigenvalues I've found so far.
9. Visual Information Theory - by C. Olah. Entropy, Cross-entropy, and KL-divergence visually explained...
10. Calculus on computational graphs: backpropagation - by C. Olah. Backpropagation explained as calculus on computational graphs
11. Understanding LSTM Networks - by C.Olah
12. The Unreasonable Effectiveness of Recurrent Neural Networks - by A. Karpathy. An introduction to RNN and charater-level language models.
13. The Matrix Calculus You Need For Deep Learning - by Terrence Parr and Jeremy Howard.

Programming Machine Learning

1. Introduction to NumPy - By Sebastian Raschka (Appendix F)
2. An introduction to NumPy and SciPy - By M. Scott Shell

Advanced

1. Deep Reinforcement Learning Doesn't Work Yet - By Alex Irpan.

Visualization

1. Visual Vocabulary (.png) - By ft.com - How to visualize your data, depending on what you want to emphasize.
2. Visualizing the uncertainty in data - By Nathan Yau
3. Fundamentals of Data Visualization - By Claus Wilke - "The book is meant as a guide to making visualizations that accurately reflect the data, tell a story, and look professional."

My tutorials

1. Yet Another Python Encoding Tutorial

2. Matrices for Data Scientists

3. Natural Language Parsing with Python

4. Ciencia de Datos: lo mínimo que hay que saber (in Spanish)

• 4.1 NumPy
• 4.2 Pandas
• 4.3 Matplotlib y Seaborn

Beautiful links

1. Movie Recommendations with k-Nearest Neighbors and Cosine Similarity - By Nicole White.
2. How to make beautiful data visualizations in Python with matplotlib - By Randal Olson
3. Logs, Tails, Long Tails - By Ryan Moulton. Why log probabilities are useful. Why long tails matter.
4. Sentiment Analysis on Movie Reviews - By Rafael Carrascosa. Sentiment Analysis using Random Forests.
5. Tiny Data, Approximate Bayesian Computation and the Socks of Karl Broman - By Rasmus Bååth.
6. Seeing Theory By Daniel Kunin. A visual introduction to Probability and Statistics

Random Amusements

1. Figuritas (In Spanish)
2. Mentiras, malditas mentiras, y encuestas (In Spanish)
3. Mi "predicción" para las elecciones 2014 en Uruguay (In Spanish)
4. Python, Machine Learning y el Titanic - Material for a talk at the Tech Meetup Uruguay 2014 (In Spanish)
5. Seminario Ciencia de Datos - Slides for a 8-hour seminar on Data Science. Facultad de Ciencias Económicas - Universidad de la República - Uruguay