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We’re back with the latest set of #AtHomeWithAI researcher recommended resources, this time from research scientist @AdamMarblestone! (1/7) Image
Adam suggests class materials from @Stanford if students are looking for ideas on computational models of the neocortex.

Follow along here: stanford.io/2XWiNlB #AtHomeWithAI
Need a resource that covers the essentials of linear algebra for AI? This online lecture by #gilbertstrang and @broadinstitute does just that.

Watch it here: bit.ly/3buHbi6 #AtHomeWithAI
Up next is a concise introduction to probabilistic graphical models. These notes are based on a @Stanford course and are written by @volkuleshov and @StefanoErmon - with help from students and course staff.

Read them all here: bit.ly/2xSxVWe #AtHomeWithAI
Consider yourself a newcomer to nonlinear dynamics and chaos?

Adam suggests this course of 25 lectures, filmed at @Cornell in 2014. The collection of videos closely follows @stevenstrogatz’s "Nonlinear Dynamics and Chaos”.

Watch here: bit.ly/3eFoied #AtHomeWithAI
Interested in keeping up with the progress towards the scientific goal of understanding human intelligence from a computational POV?

If so, @AdamMarblestone suggests the @MITOCW readings on “The Human Intelligence Enterprise”: bit.ly/3cRZePW #AtHomeWithAI
Completely comprehending the science behind the brain is no small feat. For those looking to learn as much as possible, Adam points to the @BrandeisU paper on the challenges of understanding the brain.

Read it here: bit.ly/2VPCIjh & let us know your #AtHomeWithAI recs!
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Looking for a few more favourite resources from the team? Today’s #AtHomeWithAI picks are from research scientist @TaylanCemgilML! (1/6)
His first recommendation is for those looking to learn about the basics of probabilistic reasoning and modelling.

He suggests “Bayesian Reasoning and Machine Learning” [longer read] by @davidobarber. Read it for free here: bit.ly/3cG99rS #AtHomeWithAI
Are you a beginner looking for a lesson on the Monte Carlo method?

Taylan’s own, “A Tutorial Introduction to Monte Carlo methods, Markov Chain Monte Carlo and Particle Filtering” is available here: bit.ly/3cAQ8XG #AtHomeWithAI
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@DeepMind
We’re back with more suggestions from our researchers for ways to expand your knowledge of AI.

Today’s #AtHomeWithAI recommendations are from research scientist Kimberly Stachenfeld (@neuro_kim) (1/7)
She recommends “The Scientist in the Crib” [longer listen] by @AlisonGopnik, Andrew Meltzoff, & Patricia K. Kuhl for those who are interested in what early learning tells us about the mind.

Listen along here: adbl.co/2Wwp5pE #AtHomeWithAI
Want to explore intelligence by using an approach that integrates cognitive science, neuroscience, computer science and AI?

Kimberly suggests the Brains, Minds & Machines Summer Course, offered & taught by @MBLScience and @MIT_CBMM here: bit.ly/351M6V5 #AtHomeWithAI
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@DeepMind
Looking for a few more favourite resources from the team? Today’s #AtHomeWithAI picks are from software engineer Julian Schrittwieser (@Mononofu), one of the team behind #AlphaZero!(1/6)
Many people are now moving code into Jax. To get up to speed, Julian suggests exploring the DeepMind Haiku library, designed to help you implement deep reinforcement learning algorithms. Explore it here: bit.ly/2zBIkpR #AtHomeWithAI
Fancy a long read? Though the field of DL moves very quickly, @Mononofu considers both good foundational resources:

Neural Networks and DL from @michael_nielsen bit.ly/3ePYa06

DL by @goodfellow_ian, Yoshua Bengio & @AaronCourville bit.ly/351qMzb #AtHomeWithAI
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