Contains code examples for all sorts of machine learning tasks and applications. The notebooks are meant to be minimal and easily reusable and extendable. You are free to use them for educational and research purposes.
| Name | Description | Notebook |
|---|---|---|
| Text Classification using Transformer | An implementation of Attention Mechanism and Positional Embeddings on a text classification task |
Kaggle |
| Neural Machine Translation using Transformer | An implementation of Transformer to translate human readabke dates in any format to YYYY-MM-DD format. |
Kaggle |
| Feature Tokenizer Transformer | An implementation of Feature Tokenizer Transformer on a classification task |
Kaggle |
| Named Entity Recognition using Transformer | An implementation of Transformer to perform token classification and identify species in PubMed abstracts |
Kaggle |
| Extractive Question Answering using Transformer | An implementation of Transformer to perform extractive question answering |
Kaggle |
| Name | Description | Notebook |
|---|---|---|
| Siamese Network | An implementation of Siamese Network for finding Image Similarity |
Kaggle |
| Variational Auto Encoder | An implementation of Variational Auto Encoder to generate Augmentations for MNIST Handwritten Digits |
Kaggle |
| Object Detection using Sliding Window and Image Pyramid | A basic object detection implementation using sliding window and image pyramid on top of an image classifer |
Kaggle |
| Object Detection using Selective Search | A basic object detection implementation using selective search on top of an image classifer |
Kaggle |
| Name | Description | Notebook |
|---|---|---|
| Deep Convolutional GAN | An Implementation of Deep Convolutional GAN to generate MNIST digits |
Kaggle |
| Wasserstein GAN with Gradient Penalty | An Implementation of Wasserstein GAN with Gradient Penalty to generate MNIST digits |
Kaggle |
| Conditional GAN | An Implementation of Conditional GAN to generate MNIST digits |
Kaggle |
If you find any bugs or have any questions regarding these notebooks, please open an issue. We will address it as soon as we can.
Reach out on Twitter if you have any questions.
Please cite the following if you use the code examples in your research:
@misc{saravia2022ml,
title={ML Notebooks},
author={Saravia, Elvis and Rastogi, Ritvik},
journal={https://github.com/dair-ai/ML-Notebooks},
year={2022}
}