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.
Feel 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 Attention Mechanism and Positional Embeddings | 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 |
|
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}
}