The ICML2020 Tutorial on “Model-Based Methods in Reinforcement Learning” given by Igor Mordatch (@IMordatch) and Jessica Hamrick (@jhamrick) is a really nice survey of the literature.
— hardmaru (@hardmaru) July 17, 2020
The slides are packed with useful information about model-based RL:https://t.co/EHKE3ak334 pic.twitter.com/GixfXDurIJ
Faster than training from scratch - #Tutorial on how to fine-tune the English #GPT2 in any language with #Transformers, #Tokenizers & #fastai v2 (use case with Portuguese). Thanks to @huggingface, @jeremyphoward, @GuggerSylvain & @ailab_unb #NLP #DL #AI https://t.co/O21oSlW0OA
— Pierre Guillou (@pierre_guillou) July 15, 2020
New tutorial!🚀
— Adrian Rosebrock (@PyImageSearch) July 13, 2020
Implementing basic R-CNN object detector from scratch with #Keras and #TensorFlow 2:
- Train on your own datasets
- Detailed tutorial
- Includes #Python codehttps://t.co/vlxyGoQuO7 👍#DeepLearning #MachineLearning #ArtificialIntelligence #AI #DataScience pic.twitter.com/SFPER6aPqv
Oh no! Reached the end of the #SciPy2020 ML playlist! But it ended with a bang! Remember Ray (the distr. Pandas lib.)? It evolved into a whole ecosystem for workflows that are (now) overlapping (ML training, serving, etc). Great talk by @robertnishihara : https://t.co/oYEhU9d7x0 pic.twitter.com/9sujKBkmiu
— Sebastian Raschka (@rasbt) July 13, 2020
Explaining RNNs without neural networks. A summary of what I learned building RNNs with just linear algebra, starting from @karpathy's code then reading fastai book from @jeremyphoward / @GuggerSylvain plus @iamtrask's grokking DL book. Thanks guys! https://t.co/6wrW3cFz8J
— Terence Parr (@the_antlr_guy) July 10, 2020
Jirka, Lead contributor for Lightning, and Zach, ML Engineer at Google, discuss how PyTorch Lightning became the first ML framework to run continuous integration on TPUs in this blog post. https://t.co/zk78fnTF2s
— PyTorch (@PyTorch) July 10, 2020
Bootstrap https://t.co/eZ2bbpDzwV pic.twitter.com/F0kZUh6f4u
— Chris Albon (@chrisalbon) July 10, 2020
New tutorial on https://t.co/m6mT8SrKDD: speaker recognition from speech samples, using FFT & 1D CNN. By @fadibadine https://t.co/8pfJpUWwP2
— François Chollet (@fchollet) July 7, 2020
Short educational talks on various PyTorch topics such as the latest updates, Pruning, Torchaudio, PyTorch3D, PyTorch Mobile Runtime on iOS/Android and more are now available on the PyTorch YouTube Channel. https://t.co/VjTtdbvOnJ
— PyTorch (@PyTorch) July 6, 2020
The full version of the Deep Learning with PyTorch book from Luca Antiga, Eli Stevens, and Thomas Viehmann is now available! New chapters include in-depth real-world examples and production deployment. Grab a free digital copy on: https://t.co/RNUUKbAZTs
— PyTorch (@PyTorch) July 6, 2020
🔍 @Sicara_fr's TF Explain implements interpretability methods as @TensorFlow 2.x callbacks to help you understand the behavior and decision-making processes of your neural networks.
— 👩💻 DynamicWebPaige @ 127.0.0.1 🏠 (@DynamicWebPaige) July 6, 2020
Check it out in action in @TensorBoard in the tutorial below:
👉 https://t.co/6hTGLmY9gm pic.twitter.com/t5H77ZhVKX
Slides for my #WiNLP2020 keynote later today https://t.co/75yvXBcaMf pic.twitter.com/9GLU79iur3
— Rachael Tatman (@rctatman) July 5, 2020
