Profile picture
, 6 tweets, 6 min read
My Authors
Read all threads
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
For practical, interactive coding exercises on a range of topics, dive into deep learning here: d2l.ai from @astonzhangAZ, @zacharylipton, Mu LI and @smolix #AtHomeWithAI
Finally, for an introduction to reinforcement learning, @Mononofu suggests starting with these @UCL video lectures from his team mate, co-creator of #AlphaZero #AlphaStar & recent #ACMPrize winner, David Silver: bit.ly/2Kt8w8l #AtHomeWithAI
Have your own suggestions? Let us know! #AtHomeWithAI
Missing some Tweet in this thread? You can try to force a refresh.

Enjoying this thread?

Keep Current with DeepMind

Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

Twitter may remove this content at anytime, convert it as a PDF, save and print for later use!

Try unrolling a thread yourself!

how to unroll video

1) Follow Thread Reader App on Twitter so you can easily mention us!

2) Go to a Twitter thread (series of Tweets by the same owner) and mention us with a keyword "unroll" @threadreaderapp unroll

You can practice here first or read more on our help page!

Related hashtags

#AtHomeWithAI #AlphaZero #AlphaStar #ACMPrize

More from @DeepMind see all

Profile picture
DeepMind
@DeepMind
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)
Today’s suggestions are from research scientist Feryal Behbahani @feryalmp! #AtHomeWithAI
Want to know more about reinforcement learning? She recommends @EmmaBrunskill’s’ online lecture series from @StanfordEng. Watch them here: bit.ly/3eAArB9 #AtHomeWithAI
Read 9 tweets
Profile picture
DeepMind
@DeepMind
Memory is a crucial feature of intelligence. Our new blog post overviews the use of memory in deep learning, and how modelling language may be an ideal task for developing better memory architectures:
deepmind.com/blog/article/A…
In order to understand a book, a reader must maintain memories of events and characters over many chapters. We introduce the Compressive Transformer, a neuroscience-inspired memory model that achieves state-of-the-art performance in language modelling:
arxiv.org/abs/1911.05507
as well as a benchmark dataset for book-length texts, over double the size of the Billion Word benchmark:
github.com/deepmind/pg19
Read 3 tweets
Profile picture
DeepMind
@DeepMindAI
Our new paper, “Reinforcement Learning, Fast and Slow”, reviews recent techniques in deep RL that narrow the gap in learning speed between humans and agents, & demonstrate an interplay between fast and slow learning w/ parallels in animal/human cognition:

cell.com/trends/cogniti…
When episodic memory is used in reinforcement learning, an explicit record of past events is maintained for making decisions about the current situation. The action chosen is the one associated with the highest value, based on the outcomes of similar past situations.
Meta-reinforcement learning quickly adapts to new tasks by learning strong inductive biases. This is done via a slower outer learning loop training on a distribution of tasks, leading to an inner loop that rapidly adapts by maintaining a history of past actions and observations.
Read 5 tweets

Embed code for your website

×
Follow Us on Twitter!

Did Thread Reader help you today?

Support us! We are indie developers!

This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3.00/month or $30.00/year) and get exclusive features!

Become Premium

Too expensive? Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal Become our Patreon

Thank you for your support!