🤔 Curious about getting started with sentiment extraction? Check out this awesome notebook on Tweet Sentiment Extraction with TF2/SpanBERT using TPUs from Kaggler @asuzsoy || https://t.co/AJUREUXPoY pic.twitter.com/wPb5d55gX1
— Kaggle (@kaggle) June 30, 2020
Interested in learning how to build OCR models? Here's a great code example from @A_K_Nain showing a model that can break Captchas. https://t.co/dmXDCNEppI pic.twitter.com/Rh3Py2CBqp
— François Chollet (@fchollet) June 26, 2020
Automate the tuning of hyperparameters with PyTorch Ignite using Bayesian Optimisation in Optuna. Learn more: https://t.co/VxOgKGxXbh
— PyTorch (@PyTorch) June 25, 2020
Nice example of #UNet + #EfficientNet using @TensorFlow 2.2 and tf.keras: https://t.co/BxSb0v0IcE pic.twitter.com/qwZPMatvTN
— 👩‍💻 DynamicWebPaige @ 127.0.0.1 🏠(@DynamicWebPaige) June 24, 2020
Check out this tutorial from the Catalyst team on how to distillate BERT models. Distilling BERT models can minimize accuracy loss, reduce model sizes, and speed up inferences.https://t.co/uBvba5NObZ
— PyTorch (@PyTorch) June 24, 2020
Kaggler @DavidMezzetti walks you through how to build a fully-automated data analysis pipeline on Kaggle in this 🔥 blog post || [READ] https://t.co/fwpwBRy8u0
— Kaggle (@kaggle) June 22, 2020
Categorical preprocessing layers are a new feature in Keras (available in tf-nightly & TF 2.3). They enable you to do string/int feature encoding directly as part of your model -- one-hot, multi-hot, indexing, hashing, etc. Check out this simple example: https://t.co/DLoUobJHYF
— François Chollet (@fchollet) June 22, 2020
Ever wondered what Bayesians are so excited about? Here's a walkthrough of a couple of mostly-correct examples for the newbie.https://t.co/LdMfV1l02U
— Brandon Rohrer (@_brohrer_) June 22, 2020
Here is the code for the above screen-cap: https://t.co/2DQFxELrx9
— Thomas Wolf (@Thom_Wolf) June 15, 2020
Cool demo by @A_K_Nain: building an OCR model with TF/Keras to read captcha https://t.co/R1EgCrWbkc
— François Chollet (@fchollet) June 14, 2020
Catalyst is a PyTorch framework designed for reproducibility, faster experimentation and code/ideas reusing.
— PyTorch (@PyTorch) June 11, 2020
This blog walks through MINST classifier tutorial and compare PyTorch and Catalyst code side-by-side: https://t.co/VQ3VufvAeB
A demonstration of metric learning for image similarity search, by @mat_kelcey https://t.co/mtZHOz7VM8
— François Chollet (@fchollet) June 10, 2020
