Presenting algorithms for causal reasoning in Probability Trees. PTs are causal models of generative processes that can represent context-specific causal dependencies, ideal for causal induction, RL and games: https://t.co/Ff8aHMKuY6
β DeepMind (@DeepMind) October 28, 2020
Colab tutorial: https://t.co/J0Y8foKCYi pic.twitter.com/UQ6rDwDQX5
Now available to take for free online: MIT's class on Machine Learning in Healthcare.
β MIT CSAIL (@MIT_CSAIL) October 26, 2020
Videos, slides, notes & more: https://t.co/USoIwqpQuM#MondayMotivation #MLforHC #HealthcareAI #aihealthcare pic.twitter.com/mXaeoq73KK
My @GlobAICommunity talk on @spacy_io v3.0 is now live! In it, I'm walking through some of the most exciting new features & the ideas behind them.
β Ines Montani γ°οΈ (@_inesmontani) October 26, 2020
πΊ Video: https://t.co/dRQbyZKlnq
π spaCy v3.0 nightly: https://t.co/cpUa14hZTr
π₯ Blog post: https://t.co/OfC6AYlJPU pic.twitter.com/ioYD1hfjvT
Lots of great details here about building important deep learning algorithms from scratch. https://t.co/ypJd7Vcwqu pic.twitter.com/kZyTQvUzHQ
β Jeremy Howard (@jeremyphoward) October 26, 2020
We just published a new video by @ClementDelangue who walks you through fine-tuning of a language model with #huggingtweets by @borisdayma & how to use the model hub, zero-shot classification & open-domain question answering! https://t.co/qPUdW94BZv
β Hugging Face (@huggingface) October 22, 2020
Behold, an updated, simplified, step-by-step guide (plus video!) for doing regression discontinuity with #rstats! https://t.co/r9q16QjGnU pic.twitter.com/BouekS7Bz2
β Andrew Heiss (@andrewheiss) October 20, 2020
Training a transformer model for text classification has never been easier. Pick a model checkpoint from the π€Transformers library, a dataset from the dataset library and fine-tune your model on the task with the built-in Trainer!https://t.co/ahz6sHc0i4
β Sylvain Gugger (@GuggerSylvain) October 19, 2020
Reinforcement Learning is at the forefront of AI research.
β Andrew Trask (@iamtrask) October 14, 2020
This is one of the first truly *introductory* tutorials of RL & Deep Learning I've seen.
My kind of tutorial... lots of toy code examples and simple analogies!!!
Excellent for #100DaysOfMLCodehttps://t.co/6TZ35619Dq pic.twitter.com/QVTM7vVoht
The robotics team @arrival, a zero-emission solutions company, demonstrates how to apply deep reinforcement learning (DRL) to solve your own robotic challenge in this tutorial. Learn how to solve the peg-in-hole insertion task using Catalyst/CatalystRL: https://t.co/SLNZaiHoDp
β PyTorch (@PyTorch) October 13, 2020
Ceyda Cinarel (@ceyda_cinarel) shared a neat demo of a custom inference widget for token classification models π·, built on top of the:
β Julien Chaumond (@julien_c) October 9, 2020
- @huggingface Inference API
- Displacy from @spacy_io
- @streamlit
π₯ Demo: https://t.co/Rlk5gb9rO0
π₯ Blogpost: https://t.co/rKwlrFeMj2
The ultimate guide to encoder-decoder models!
β Hugging Face (@huggingface) October 5, 2020
Today, we're releasing part one explaining how they work and why they have become indispensable for NLG tasks such as summarization and translation.
> https://t.co/MbIRQMVtzU
Subscribe for the full series: https://t.co/O2BN6nth3I pic.twitter.com/LxOC8qekbg
Optimize runtime memory πββοΈπ§
β TensorFlow (@TensorFlow) October 2, 2020
In this blog, youβll learn how TensorFlow Lite optimizes its memory footprint for neural net inference on resource-constrained devices.
Start optimizing β https://t.co/H7AxFTd8Py pic.twitter.com/JQWPIa18uQ
