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

Dive into Deep Learning with this machine learning course taught by industry veterans.

You'll learn about Random Forests, Gradient Descent, Recurrent Neural Networks, and other key coding concepts. All you need to get started with this course is some Python knowledge and a little high school math. And if you need to brush up on those, freeCodeCamp has you covered. (15 hour YouTube course):

#deeplearning

https://www.freecodecamp.org/news/learn-deep-learning-from-the-president-of-kaggle/

Interested in Learning for Safety-Critical Control?

Check talk at the "Physics ∩ ML" Seminar
http://www.physicsmeetsml.org/

Title: Learning for Safety-Critical Control in Dynamical Systems

Abstract:
This talk describes ongoing research at Caltech on integrating learning into the design of safety-critical controllers for dynamical systems. To achieve control-theoretic guarantees while using powerful function classes such as neural networks, we must carefully integrate conventional control principles with learning into unified frameworks. I will present two paradigms: integration in dynamics modeling and in policy/controller design. Special emphasis on methods that both admit relevant safety guarantees and are practical to deploy.

Featuring work by Caltech students/postdocs/alumni:
Hoang Le (http://hoangle.info/)
Guanya Shi (http://gshi.me/)
Anqi Liu (https://anqiliu-ai.github.io/)
Richard Cheng (https://rcheng805.github.io/)
Jialin Song (https://jialin-song.github.