What is notes

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

Objective

image

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

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.