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 joeddav.github.io
Zero-Shot Learning in Modern NLP | Joe Davison Blog
| | explosion.ai
Embed, encode, attend, predict: The new deep learning formula for state-of-the-art NLP models ยท Explosion
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| | Over the last six months, a powerful new neural network playbook has come together for Natural Language Processing. The new approach can be summarised as a simple four-step formula: embed, encode, attend, predict. This post explains the components of this new approach, and shows how they're put together in two recent systems.
| | www.kdnuggets.com
How to Build a Text Classification Model with Hugging Face Transformers - KDnuggets
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| | Learn the key steps to train a transformer-based text classification model from scratch.
| | towardsml.wordpress.com
BERT Explained: A Complete Guide with Theory and Tutorial - Towards Machine Learning
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| | Unless you have been out of touch with the Deep Learning world, chances are that you have heard about BERT - it has been the talk of the town for the last one year. At the end of 2018 researchers at Google AI Language open-sourced a new technique for Natural Language Processing (NLP) called BERT...
| | wtfleming.github.io
Cats vs Dogs - Part 3 - 99.1% Accuracy - Binary Image Classification with PyTorch and an Ensemble of ResNet Models
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| [AI summary] This post discusses achieving 99.1% accuracy in binary image classification of cats and dogs using an ensemble of ResNet models with PyTorch.