ml-notebooks's Introduction

🐙 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.

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}
}

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