🎉Part 2- Summary of 10 summaries on:
— AI Fast Track (60/60) (@ai_fast_track) December 6, 2021
Tips & Trick & Best Practices in training (not only) object detection models.
Don't miss any of those posts, follow @ai_fast_track to catch them in your feed.
🎁 Summary of summaries: ... pic.twitter.com/VLcWNkMaph
Minitorch🔥(https://t.co/41DIF6vAUl, v2021) Build-it-yourself deep learning.
— Sasha Rush (@srush_nlp) December 6, 2021
Learn about auto-diff, tensors, gpu's, and advanced nn models. Build everything from scratch in pure python with full unit test coverage and interactive visualizations. pic.twitter.com/DmSLwuzoZQ
Now free (formerly $99): Ari Lamstein's 2 online courses on using his {choroplethr} #rstats package to make maps with R.
— Sharon Machlis (@sharon000) December 2, 2021
Mapmaking in R with Choroplethr https://t.co/XDyzEQHvta
Shapefiles for R Programmers https://t.co/4tGzVvhjz4#rspatial #gis #dataviz pic.twitter.com/xWpfvERz0X
Differential Inference: A Criminally Underused Tool (https://t.co/zSwhvk806r)
— Sasha Rush (@srush_nlp) November 30, 2021
An annotated talk about elementary probability (coins&dice) in pytorch. Nothing new, just think we should mostly do discrete inference with auto-diff.
Slides: https://t.co/tJHQlLwNrp pic.twitter.com/rq37IuEuh6
Learning rate is one of the most important hyperparameters to adjust well during the ML model training.
— Jean de Nyandwi (@Jeande_d) November 12, 2021
A high learning rate can speed up the training, but it can cause the model to diverge. A low rate can slow the training.
Here are different learning rate curves pic.twitter.com/GsGf4phRaA
Thanks to all young Belgian PhD students who just attended my #TRAIL doctoral lecture on Deep Latent Variable Models! Slides and notes are available at https://t.co/vkGYuvqU0A @UniversiteLiege pic.twitter.com/YGCLae5iqv
— Gilles Louppe (@glouppe) November 5, 2021
This blog will examine why distributed training is important and how you can use PyTorch Lightning with Ray to enable multi-node training and automatic cluster configuration with minimal code changes. Read more below:https://t.co/xnpj3A98sv
— PyTorch (@PyTorch) November 2, 2021
FX-based feature extraction is a new TorchVision utility that allows access to intermediate transformations of an input during the forward pass of a PyTorch Module.
— PyTorch (@PyTorch) October 29, 2021
Read more below on what makes TorchVision's utility more versatile than existing methods.https://t.co/RRsowlXPwi
Just checking out Hummingbird again for a current project (https://t.co/cmDYE1pbER). It combines my two favorite libraries (scikit-learn & PyTorch) and lets you port over existing models (and leverage GPUs) without having to retrain. Amazing stuff! pic.twitter.com/mwl4Pe0Pmn
— Sebastian Raschka (@rasbt) October 29, 2021
Dirty data science: machine learning on non-curated data
— Gael Varoquaux (@GaelVaroquaux) October 26, 2021
These slides are a one-hour course, touching on:
• missing values
• non-normalized categorical entrieshttps://t.co/zzlcCFcRMy
The problem with p-hacking is not the “hacking,” it’s the “p” (or, Fisher is just fine on this one) https://t.co/Rc0DoYeMmj
— Andrew Gelman et al. (@StatModeling) September 30, 2021
Here it is! 🔥
— Matt Turck (@mattturck) September 28, 2021
After 100’s of hours of research and writing, excited to release the 2021 ***MAD*** landscape (Machine learning, Artificial Intelligence and Data) - with my colleague @john_d_wu
A crazy, intense, and fun year in the ecosystem
👇👇👇https://t.co/NQDQfaFUk7
