DongjunLee / notes

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

notes

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

Objective

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
• Documentation by Notion

Usage

• 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

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Knowledge Source

Math

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

Machine Learning

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

Deep 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.