Practical Deep Learning for Coders, 2019 edition, is now available. With a shiny new video player with searchable transcripts.
— Jeremy Howard (@jeremyphoward) January 25, 2019
This course is 100% new material, including some new techniques and results never previously published.https://t.co/upLaVgfwSg pic.twitter.com/XnxcsTO5WC
"How to visualize convolutional features in 40 lines of code" by Fabio M. Graetz
— Jeremy Howard (@jeremyphoward) January 24, 2019
Amazingly cool (and simple!) approach to visualizing convolutional features using fastai and @PyTorch (it uses the older fastai 0.7 and pytorch 0.4).https://t.co/QfAtBtzuIP pic.twitter.com/AeGge1AUkj
SequentialEx and Sequential models are also self-documenting (since their string representation shows exactly how they are calculated) and easily modified for fine-tuning (you can remove and replace layers)
— Jeremy Howard (@jeremyphoward) January 24, 2019
A little trick that you may not have come across yet in fastai and @PyTorch: with `SequentialEx` and `MergeLayer` you can simplify many kinds of models with skip connections. Here's an example:https://t.co/1yuhZb46EV pic.twitter.com/6OwPoOHXHT
— Jeremy Howard (@jeremyphoward) January 24, 2019
TorchSeg - Fast, modular reference implementation and easy training of Semantic Segmentation algorithms in PyTorch. https://t.co/qg81kjkux6
— Python Trending (@pythontrending) January 23, 2019
Understanding Convolutional Neural Networks through Visualizations in PyTorch by Vihar Kurama https://t.co/wLlA2AlZnY #pytorch #deeplearning #machinelearning #ml #ai #neuralnetworks #datascience
— PyTorch Best Practices (@PyTorchPractice) January 19, 2019
Gradually-Warmup Learning Rate Scheduler https://t.co/viBsz55NoD #pytorch #deeplearning #machinelearning #ml #ai #neuralnetworks #datascience
— PyTorch Best Practices (@PyTorchPractice) January 17, 2019
Really proud to share "What is torch.nn, really?", which takes you from a neural net written from scratch, refactored step by step using all the key concepts in `torch.nn`.
— Jeremy Howard (@jeremyphoward) January 16, 2019
If you want to really understand how neural nets work in @PyTorch, start here!https://t.co/qJgsZPQnTL
Anomaly detector in PyTorch: https://t.co/9D82gvOvUg
— PyTorch (@PyTorch) December 18, 2018
For reference, here's a regular Convolutional Variational Autoencoder: https://t.co/Lj3sgWeOCf
— Sebastian Raschka (@rasbt) December 16, 2018
and a regular Conditional Variational Autoencoder: https://t.co/cfmyfsNsVp
PyText from @facebookai : A set of NLP models that are highly configurable, that have been shipping at scale, along with efficient inference engine that runs them. Aimed at product developers rather than researchers.https://t.co/9xatvwTd8V pic.twitter.com/n5NiZ6z5QS
— PyTorch (@PyTorch) December 14, 2018
PyTorch BERT models are now 4x faster, thanks to @nvidia https://t.co/enSldGFAAC
— PyTorch (@PyTorch) December 14, 2018
