Course Notes
http://web.stanford.edu/class/cs224n/
https://deepgenerativemodels.github.io/notes/
https://stanford-cs329s.github.io/syllabus.html
https://cmu-multicomp-lab.github.io/mmml-course/
Classic Book
DL: https://www.deeplearningbook.org/
CV: https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738
NLP: Speech and Language Processing by Dan Jurafsky and James Martin
CV
List of CV resources: https://github.com/kjw0612/awesome-deep-vision
NLP
List of NLP resources: https://github.com/keon/awesome-nlp
List of must-read NLP papers: https://github.com/amanchadha/100-nlp-papers
NLP progress: https://github.com/sebastianruder/NLP-progress
NLP Testing Tool: https://towardsdatascience.com/checklist-behavioral-testing-of-nlp-models-491cf11f0238
About decoding:
https://huggingface.co/blog/how-to-generate
Brain
Learn basics of deep learning for fMRI and EEG
EEG Datasets: https://github.com/meagmohit/EEG-Datasets
Electrophysiology Datasets: https://github.com/openlists/ElectrophysiologyData
fMRI: https://openfmri.org
Review of EEG ERP: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3016705/
EEG classic book - https://www.amazon.com/Niedermeyers-Electroencephalography-Principles-Clinical-Applications/dp/0781789427
Misc
Explainable AI
https://christophm.github.io/interpretable-ml-book/
State of the art AI/ML paper: https://paperswithcode.com/sota
AI conference deadlines: https://aideadlin.es/
Acceptance rates: https://github.com/amanchadha/Conference-Acceptance-Rate
Current state of industry AI: https://huyenchip.com/2020/06/22/mlops.html, https://huyenchip.com/2022/01/02/real-time-machine-learning-challenges-and-solutions.html, https://ai.googleblog.com/2022/01/google-research-themes-from-2021-and.html?m=1
Tool to track sota: https://huyenchip.com/2018/10/04/sotawhat.html
Fun Engaging Educational Tools
Conv: https://cs.stanford.edu/people/karpathy/convnetjs/index.html
Statistics: https://seeing-theory.brown.edu/
Pandas: https://pandastutor.com/
LInear Algebra: http://matrixmultiplication.xyz/
Step by Step DL learning with digit: https://cs.stanford.edu/people/karpathy/convnetjs/intro.html