Data Science at Home
Episodes
Tuesday Aug 17, 2021
Reinforcement Learning is all you need. Or is it? (Ep. 165)
Tuesday Aug 17, 2021
Tuesday Aug 17, 2021
Is reinforcement learning sufficient to build truly intelligent machines? Listen to this episode to find out.
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References
https://bdtechtalks.com/2021/06/07/deepmind-artificial-intelligence-reward-maximization/
https://pub.towardsai.net/building-reinforcement-learning-agents-that-learn-to-collaborate-and-compete-at-the-same-time-d081fea942d2
https://towardsdatascience.com/intro-to-reinforcement-learning-temporal-difference-learning-sarsa-vs-q-learning-8b4184bb4978
Monday Jun 29, 2020
Rust and machine learning #4: practical tools (Ep. 110)
Monday Jun 29, 2020
Monday Jun 29, 2020
In this episode I make a non exhaustive list of machine learning tools and frameworks, written in Rust. Not all of them are mature enough for production environments. I believe that community effort can change this very quickly.
To make a comparison with the Python ecosystem I will cover frameworks for linear algebra (numpy), dataframes (pandas), off-the-shelf machine learning (scikit-learn), deep learning (tensorflow) and reinforcement learning (openAI).
Rust is the language of the future.Happy coding!
Reference
BLAS linear algebra https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms
Rust dataframe https://github.com/nevi-me/rust-dataframe
Rustlearn https://github.com/maciejkula/rustlearn
Rusty machine https://github.com/AtheMathmo/rusty-machine
Tensorflow bindings https://lib.rs/crates/tensorflow
Juice (machine learning for hackers) https://lib.rs/crates/juice
Rust reinforcement learning https://lib.rs/crates/rsrl
Tuesday Oct 15, 2019
What is wrong with reinforcement learning? (Ep. 82)
Tuesday Oct 15, 2019
Tuesday Oct 15, 2019
Join the discussion on our Discord server
After reinforcement learning agents doing great at playing Atari video games, Alpha Go, doing financial trading, dealing with language modeling, let me tell you the real story here.In this episode I want to shine some light on reinforcement learning (RL) and the limitations that every practitioner should consider before taking certain directions. RL seems to work so well! What is wrong with it?
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References
Emergence of Locomotion Behaviours in Rich Environments https://arxiv.org/abs/1707.02286
Rainbow: Combining Improvements in Deep Reinforcement Learning https://arxiv.org/abs/1710.02298
AlphaGo Zero: Starting from scratch https://deepmind.com/blog/article/alphago-zero-starting-scratch