This deep dive is all about neural networks - training them using best practices, debugging them and maximizing their performance using cutting edge research.
“This guide is designated to anybody with basic programming knowledge or a computer science background interested in becoming a Research Scientist with on Deep Learning and NLP”.
IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz - GitHub - dynamicslab/databook_python: IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz
J. Berner, P. Grohs, G. Kutyniok, and P. Petersen. (2021)cite arxiv:2105.04026Comment: This review paper will appear as a book chapter in the book "Theory of Deep Learning" by Cambridge University Press.
Y. Burda, H. Edwards, D. Pathak, A. Storkey, T. Darrell, and A. Efros. (2018)cite arxiv:1808.04355Comment: First three authors contributed equally and ordered alphabetically. Website at https://pathak22.github.io/large-scale-curiosity/.