- Computer Vision
- Natural Language Processing
- Sequence Classification
- Question Answering
- MLOps
- ML Monitoring
Experiments using fastai, transformers, evidently for ml-monitoring
- Baseline using fastai defaults with Resnet34
- Fit OneCycle using Resnet34 with LearningRate(LR) Finder, Discriminative LR and Mixed Precision
- MixUp, a type of DataAugmentation
- Labelsmoothing as regularization
- Test Time Augmentation
- ranger as an optimizer, a mix of LookAhead and RAdam instead of the default AdamW
- Native Mixed Precision
- Resnet from scratch
- Baseline
- Small models
- ULMFiT Approach using Spacy with Gradual Unfreezing, Weighted Cross Entropy + Label Smoothing for handling imbalanced data
- Demonstrates how to use Cleanlab to detect and remove noisy labels and train a classifier using ULMFiT Approach (Accuracy change : 0.79 -> 0.85)
- Demonstrates how to use blurr, library that integrates HF transformers with fastai.
- Another example demonstrating how to use blurr MidLevel API and DistilRoberta for Sequence Classification.
- Data Drift & Model Classification Performance for Iris dataset