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Machine Learning Basics
A practical set of notebooks on machine learning basics, implemented in both TF2.0 + Keras and PyTorch.
deep-learning natural-language-processing tensorflow pytorch
Tips for Successfully Training Transformers on Small Datasets
It turns out that you can easily train transformers on small datasets when you use tricks (and have the patience to train a very long time).
transformers small-datasets training ptb
U^2-Net
The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."
object-detection salient-object-detection image-segmentation unet
Creating an End-to-End Machine Learning Application
A complete, end-to-end ML application, implemented in both TensorFlow 2.0 and PyTorch.
api tensorflow python systems-design
Self Supervised Representation Learning in NLP
An overview of self-supervised pretext tasks in Natural Language Processing
self-supervised-learning natural-language-processing tutorial
How to Steal Modern NLP Systems with Gibberish?
It’s possible to steal BERT-based models without any real training data, even using gibberish word sequences.
bert adversarial-attacks computer-security adversarial-learning
The Illustrated FixMatch for Semi-Supervised Learning
Learn how to leverage unlabeled data using FixMatch for semi-supervised learning
semi-supervised-learning computer-vision pytorch illustrated
T5 fine-tuning
A colab notebook to showcase how to fine-tune T5 model on various NLP tasks (especially non text-2-text tasks with text-2-text approach)
natural-language-processing transformers text-2-text t5
GitHub Actions & Machine Learning Workflows with Hamel Husain
In this talk, Hamel will provide a brief tutorial on GitHub Actions, and will show you how you can use this new tool to automate your ML workflows.
github-actions machine-learning workflows tutorial
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