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10 Dec, 5 tweets, 3 min read
For #NeurIPS2020, we spoke with @wojczarnecki about Spinning Tops, advice he wish he received as a student, and his goals for next year! #PeopleBehindThePapers Image
AI has been extremely successful in real world games (GO, DOTA, StarCraft) with results coming from relatively simple multi-agent algorithms. In this paper, we hypothesise that they share a common geometry - Spinning Tops. Learn more: bit.ly/3qI8RrD #NeurIPS2020 Image
I’ve always loved biology. During my masters I decided to take a handful of neurophysiology courses - which I found to be interesting. But eventually I realised that my true strengths were in mathematical sciences. A career in ML and AI became a natural way to combine the two. Image
I wish I knew that having a doctorate wasn’t essential for a successful career. Nowadays you can join an AI lab as a research engineer (or another similar role) which can provide you with the experience and opportunities needed to succeed! Image
I want to relearn some of the mathematics that I’ve forgotten. A year isn’t long enough to revisit the whole domain, so maybe I’ll start with set theory, topology and some functional analysis. There are also a few projects I'm working on - maybe something to share at #NeurIPS2021 Image

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More from @DeepMind

1 Dec
Yesterday we shared the news that #AlphaFold has been recognised as a solution to the ‘protein folding problem’ by #CASP14, the biennial Critical Assessment of Protein Structure Prediction. But what exactly is protein folding, and why is it important? A thread… (1/6)
Proteins are the building blocks of life - they underpin the biological processes in every living thing. If you could unravel a protein you would see that it’s like a string of beads made of a sequence of different chemicals known as amino acids. (2/6)
Interactions between these amino acids make the protein fold, as it finds its shape out of almost limitless possibilities. For decades, scientists have been trying to find a method to reliably determine a protein’s structure just from its sequence of amino acids. (3/6)
Read 6 tweets
9 Jun
We have research scientist @seb_ruder up next with more #AtHomeWithAI recommendations!

He suggests the Deep Learning Book from @mitpress for a comprehensive introduction to the fundamentals of DL: bit.ly/351qMzb (1/7)
Overwhelmed with the number of available machine learning courses? @seb_ruder recommends taking a look through @venturidb’s curated - and ranked - list available on @freeCodeCamp.

bit.ly/3erZEN4 #AtHomeWithAI
Do you have a technical background? Are you looking for an introduction to natural language processing?

Sebastian recommends the @fastdotai course, “A Code-First Introduction to Natural Language Processing”.

bit.ly/3esFtP8 #AtHomeWithAI
Read 7 tweets
27 May
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
Read 6 tweets
21 May
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
Read 7 tweets
18 May
We’re back with more researcher recommended resources available to use #AtHomeWithAI. Today is the turn of research engineer @KerenGu! (1/5) Image
Her first two recommendations come from @MITDeepLearning and @MITOCW . Both are intro courses - one for machine learning & one for deep learning. Find them here: bit.ly/2VSb4lJ & bit.ly/2Vx5ZQI (2/5)
Looking for something challenging and fun? @KerenGu suggests Project Euler, a series of complex mathematical/computer programming problems hosted in a fun and recreational context. Test your skills and play along here: bit.ly/3bxBmAj #AtHomeWithAI (3/5)
Read 5 tweets
15 May
Looking to learn more about AI? Our researchers are continuing to share their #AtHomeWithAI recommendations!

Today’s choices come from William Isaac (@wsisaac), a senior research scientist who specialises in ethics, bias and fairness. (1/5) Image
For an overview on fairness & how it applies to machine learning, William suggests diving into this freely available book [long read] by @s010n @mrtz and @random_walker!

See here: bit.ly/350VoAO #AtHomeWithAI
@random_walker also discusses the various definitions of fairness and the tradeoffs they present for society in the video tutorial “21 definitions of fairness and their politics”

Watch it here: bit.ly/2S21KuE #AtHomeWithAI
Read 5 tweets

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