DeepMind’s Suggestions for Learning #AtHomeWithAI
DeepMind has been sharing resources for learning AI at home on their Twitter account. Check out a few of these suggestions here, and keep your eye on the #AtHomeWithAI hashtag for more.
Stuck at home? Looking to take advantage of this reality and expand your knowledge of AI while in quarantine or lockdown?
DeepMind has recently been sharing such resources via their Twitter account @DeepMind with the hashtag #AtHomeWithAI with the goal of helping you accomplish this very task. The campaign was introduced on Twitter last week:
For students and others interested in expanding their knowledge of AI during this period, we thought it might be helpful to ask our researchers what they consider to be the most impactful and insightful resources available to use #AtHomeWithAI
At the time of this article's publication, 3 Twitter threads of such resource suggestions have been shared by 3 DeepMind researchers and engineers; Feryal Behbahani (@feryalmp), Julian Schrittwieser (@Mononofu), and Kimberly Stachenfeld (@neuro_kim). The threads are linked below, and a selection of the suggested resources are highlighted.
I encourage you to check out some of these listed resources, the entire threads, follow @DeepMind on Twitter, and keep your eyes open for additional #AtHomeWithAI hashtags in the coming days and weeks (though, to be honest, I'm not sure how long the campaign is expected to continue).
Feryal Behbahani (@feryalmp)
- Reinforcement Learning
This is a collection of Emma Brunskill’s online video lecture series from Stanford's CS234.
- Information Theory, Pattern Recognition, and Neural Networks
For a basis in information theory, pattern recognition, and neural networks, Feryal recommends David MacKay's lectures from University of Cambridge.
- Practical Deep Learning slides and notebooks
From Khipu 2019, an event held at the Universidad de la República in Montevideo, this is a repository of slides and Colab notebooks on a variety of deep learning techniques.
View the entire thread of Feryal's suggestions here.
Julian Schrittwieser (@Mononofu)
- DeepMind's Haiku
For those moving code to JAX, or those interested in a new tool for building neural networks, DeepMind's JAX-based neural network library is worth checking out and playing around with.
- Neural Networks and Deep Learning
This online book by Michael Nielsen has become a staple in neural networks learning resources since its publication, and is freely available to read online.
- Introduction to Reinforcement Learning
These are DeepMind's David Silver's lectures for reinforcement learning, and are one of the premier go-to video collections for an introduction to the topic.
View the entire thread of Julian's suggestions here.
Kimberly Stachenfeld (@neuro_kim)
- Brains, Minds & Machines Summer Course
This MIT course explores intelligence using an approach integrating cognitive science, neuroscience, computer science and AI.
- The Appeal of Parallel Distributed Processing
Kimberly calls this (freely available) book, written by James McClelland, the late David Rumelhart, & Geoffrey Hinton, "a classic for anyone who wants to understand the roots of DL."
- Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
This book, by Peter Dayan & L.F. Abbott, is described by Kimberly as a must read for those intent on an introduction to the topic of neuroscience.
View the entire thread of Kimberly's suggestions here.
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