It aims to summarize and reproduce the textbook "The Elements of Statistical Learning" 2/E by Hastie, Tibshirani, and Friedman.

Currently working the early chapters, I try to implement without frameworks like scikit-learn for showing the algorithms that the textbook introduces to me.

Also starting with the neural networks, I decided to use PyTorch which seems less magical (They say that torch.Tensor is numpy.ndarray with GPU support).

Use your favorite virtualenv system and install the below dependencies; quite standard ones.

• numpy
• scipy
• matplotlib
• pandas
• jupyter
• pytorch
• scikit-learn (optional, used in my own articles)

(esl) $ pip install ipython numpy scipy matplotlib pandas jupyter

# The command below installs pytorch for Python 3.6 without CUDA support.
# For other settings, consult with pytorch.org.
(esl) $ pip install http://download.pytorch.org/whl/cpu/torch-0.3.1-cp36-cp36m-linux_x86_64.whl

Just run jupyter notebook.