The notes for Math, Machine Learning, Deep Learning and Research papers.

Illustration by David Somerville based on the original by Hugh McLeod

• Let's make wisdom from knowledge.
• Define concepts to be intuitively understandable.
  • Simply summary (You can check the details on Wiki)
  • With story or example
  • Draw an illustration
  • If possible, append a code
• Documentation by Gitbook

• Sync papers (* recommend path like Google Drive's sync folder)

python scripts/sync_papers.py {SYNC_PATH}

• Make SUMMARY.md

python scripts/make_summary.py

• Course & Video
  • Statistics 110: Probability - Projects at Harvard
  • Mathematics for Machine Learning: Linear Algebra by David Dye

• Course & Video
  • Stanford University - Machine Learning by Andrew Ng.
  • Stanford University - Probabilistic Graphical Models by Daphne Koller
  • OXFORD University - Machine Learning

• Book

  • Deep Learning by Ian Goodfellow Yoshua Bengio and Aaron Courville, 2016

• Course & Video

  • Stanford University - CS231n: Convolutional Neural Networks for Visual Recognition by Fei-Fei Li, Andrej Karpathy, Justin Johnson
  • Udacity - Deep Learning by Vincent Vanhoucke, Arpan Chakraborty
  • Toronto University - Neural Networks for Machine Learning by Geoffrey Hinton
  • CS224d: Deep Learning for Natural Language Processing by Richard Socher
  • Deep Learning School (bayareadlschool) September 24-25, 2016 Stanford, CA
  • Oxford Deep NLP 2017 by Phil Blunsom and delivered in partnership with the DeepMind Natural Language Research Group.