Discover and read the best of Twitter Threads about #AtHomeWithAI
Most recents (10)
~ New Post ~
During this quarantine time, I binge-watched @Stanford #CS330 lectures taught by the brilliant @chelseabfinn. This blog post is a summary of the key takeaways on #Bayesian Meta-Learning that I’ve learned. #AtHomeWithAI
medium.com/cracking-the-d…
(1/7) 👇
During this quarantine time, I binge-watched @Stanford #CS330 lectures taught by the brilliant @chelseabfinn. This blog post is a summary of the key takeaways on #Bayesian Meta-Learning that I’ve learned. #AtHomeWithAI
medium.com/cracking-the-d…
(1/7) 👇
Bayesian meta-learning generates hypotheses about the underlying function, samples from the data distribution, and reasons about model uncertainty. It is suitable for problems in safety-critical domains, exploration strategies for meta-RL, and active learning.
(2/7) 👇
(2/7) 👇
There are various Bayesian Meta-Learners pre-neural-nets:
- @drfeifei, @rob_fergus, and @PietroPerona: vision.stanford.edu/documents/Fei-…
- @rsalakhu, @wise_tbaum, and Torralba: proceedings.mlr.press/v27/salakhutdi…
- @LakeBrenden, @rsalakhu, and @wise_tbaum: papers.nips.cc/paper/5128-one…
(3/7) 👇
- @drfeifei, @rob_fergus, and @PietroPerona: vision.stanford.edu/documents/Fei-…
- @rsalakhu, @wise_tbaum, and Torralba: proceedings.mlr.press/v27/salakhutdi…
- @LakeBrenden, @rsalakhu, and @wise_tbaum: papers.nips.cc/paper/5128-one…
(3/7) 👇
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)
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
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
Sebastian recommends the @fastdotai course, “A Code-First Introduction to Natural Language Processing”.
bit.ly/3esFtP8 #AtHomeWithAI
We’re back with more #AtHomeWithAI researcher recommendations. Next up is research scientist @csilviavr with suggestions for resources to learn about causal inference! (1/5)
Her first suggestion is “The Book of Why” by @yudapearl & Dana Mackenzie.
According to Silvia, this is best for those looking for an introduction to the topic: bit.ly/30isGej #AtHomeWithAI
According to Silvia, this is best for those looking for an introduction to the topic: bit.ly/30isGej #AtHomeWithAI
Need a more in-depth look at causal inference? Silvia suggests reading through “Causal Inference in Statistics: A Primer” by @yudapearl, @MadelynTheRose & @NP_Jewell.
bit.ly/36xdvza #AtHomeWithAI
bit.ly/36xdvza #AtHomeWithAI
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
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
Taylan’s own, “A Tutorial Introduction to Monte Carlo methods, Markov Chain Monte Carlo and Particle Filtering” is available here: bit.ly/3cAQ8XG #AtHomeWithAI
We’re back with the latest set of #AtHomeWithAI researcher recommended resources, this time from research scientist @AdamMarblestone! (1/7)
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
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
Watch it here: bit.ly/3buHbi6 #AtHomeWithAI
We’re back with more researcher recommended resources available to use #AtHomeWithAI. Today is the turn of research engineer @KerenGu! (1/5)
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)
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)
Today’s choices come from William Isaac (@wsisaac), a senior research scientist who specialises in ethics, bias and fairness. (1/5)
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
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
Watch it here: bit.ly/2S21KuE #AtHomeWithAI
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)
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
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
Kimberly suggests the Brains, Minds & Machines Summer Course, offered & taught by @MBLScience and @MIT_CBMM here: bit.ly/351M6V5 #AtHomeWithAI
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
Neural Networks and DL from @michael_nielsen bit.ly/3ePYa06
DL by @goodfellow_ian, Yoshua Bengio & @AaronCourville bit.ly/351qMzb #AtHomeWithAI
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 (1/9)
Want to know more about reinforcement learning? She recommends @EmmaBrunskill’s’ online lecture series from @StanfordEng. Watch them here: bit.ly/3eAArB9 #AtHomeWithAI
