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PyTorch Transformers Tutorials
A set of annotated Jupyter notebooks, that give user a template to fine-tune transformers model to downstream NLP tasks such as classification, NER etc.
transformers text-classification text-summarization named-entity-recognition
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 production
Image Segmentation Using Keras and W&B
This report explores semantic segmentation with a UNET like architecture in Keras and interactively visualizes the model's prediction in Weights & Biases.
semantic-segmentation kera wandb visualization
Simple Transformers: Transformers Made Easy
Simple Transformers removes complexity and lets you get down to what matters – model training and experimenting with the Transformer model architectures.
transformers huggingface simple-transformers text-classification
GitHub Actions for Machine Learning
This presentation discusses the use of GitHub Actions to automate certain steps of a toy ML project.
github mlops scikit-learn wandb
Modern Data Augmentation Techniques for Computer Vision
A bunch of modern data augmentation techniques for computer vision covering cutout, mixup, cutmix and augmix.
data-augmentation cutout mixup cutmix
HuggingTweets
Tweet Generation with Huggingface.
text-generation huggingface transformers wandb
Simple Ways to Tackle Class Imbalance
Various methods used to counter class imbalance in image classification problems – class weighting, oversampling, under sampling, and two-phase learning.
tensorflow class-imbalance keras wandb
Understanding the Effectivity of Ensembles in Deep Learning
The report explores the ideas presented in Deep Ensembles: A Loss Landscape Perspective by Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan.
computer-vision deep-learning neural-networks ensembles
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