Very nice article by M. Aminian in SIAM News ennoscriptd "The Connection Between Applied Mathematics and Deep Learning".

The article dissects and explains in very clear terms the gist of my plenary talk at the recent SIAM Mathematics of Data Science conference ennoscriptd "The Deep Learning - Applied Mathematics Connection".

It discusses the specifics of optimization as it pertains to DL (stochastic optimization in high-dimensional spaces), the surprising fact that widely overparamterized DL models work well, the use of DL in traditional applications of scientific computing: PDE solving, lattice quantum computation, graph convolutional nets and the connection with Fourier transform, and the rising topic of self-supervised learning.

The methods of Applied Mathematics could help explain some of the mysteries of deep learning

SIAM News paper: https://sinews.siam.org/Details-Page/the-connection-between-applied-mathematics-and-deep-learning

Video of my talk: https://youtu.be/9sqoe_krQ1E

Papers with Code partners with arXiv
Robert Stojnic : https://medium.com/paperswithcode/papers-with-code-partners-with-arxiv-ecc362883167

@ArtificialIntelligenceArticles
#MachineLearning #arXiv #Code

Deep Reinforcement Learning
CS 285 at UC Berkeley, Fall 2020
Levine et al.: https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc
#ArtificialIntelligence #DeepLearning #ReinforcementLearning

State of AI Report 2020
The State of AI Report analyses the most interesting developments in AI. We aim to trigger an informed conversation about the state of AI and its implication for the future.
The Report is produced by AI investors Nathan Benaich and Ian Hogarth.
https://www.stateof.ai/

https://www.confetti.ai/
Confetti AI | Ace Your Machine Learning Interviews

Gerat paper

https://arxiv.org/abs/1811.10959

Deep Learning is one of the most highly sought after skills in AI. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.
https://www.newworldai.com/cs230-deep-learning-stanford-engineering/

Speed-up the Python code by thousands of times using Cython
https://towardsdatascience.com/boosting-python-noscripts-with-cython-applied-on-raspberry-pi-5ea191292e68

Efficient Transformers: A Survey
Tay et al.: https://arxiv.org/abs/2009.06732
#Transformer #DeepLearning #ArtificialIntelligence

Nguyen et al., Super-Human Performance in Online Low-latency Recognition of Conversational Speech. arXiv:2010.03449 [cs.CV]. arxiv.org/abs/2010.03449

Fourier Neural Operator for Parametric Partial Differential Equations

https://arxiv.org/abs/2010.08895