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
storyor example - Draw an
illustration - If possible, append a
code
Documentation by Gitbook- Documentation by Notion
- 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
- 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.