ML Notebooks

A series of 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 Introduction to Computational Graphs A basic tutorial to learn about computational graphs PyTorch Hello World! Build a simple neural network and train it A Gentle Introduction to PyTorch A detailed explanation introducing PyTorch concepts Logistic Regression from Scratch An implementation of logistic regression from scratch Concise Logistic Regression Concise implementation of logistic regression model for binary image classification. First Neural Network - Image Classifier Build a minimal image classifier using MNIST Neural Network from Scratch An implementation of simple neural network from scratch Introduction to GNNs Introduction to Graph Neural Networks. Applies basic GCN to Cora dataset for node classification. Emotion Classification with Fine-tuned BERT Emotion classification using fine-tuned BERT model Text Classification using Transformer An implementation of Attention Mechanism and Positional Embeddings on a text classification task Siamese Network An implementation of Siamese Network for finding Image Similarity Variational Auto Encoder An implementation of Variational Auto Encoder to generate Augmentations for MNIST Handwritten Digits 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 Object Detection using Selective Search A basic object detection implementation using selective search on top of an image classifer Neural Machine Translation using Transformer An implementation of Transformer to translate human readabke dates in any format to YYYY-MM-DD format. Feature Tokenizer Transformer An implementation of Feature Tokenizer Transformer on a classification task Counterfactual Explanations A basic tutorial to learn about counterfactual explanations for explainable AI

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}
}