This repo collects exercises and provides code for deep learning papers and algorithms. Implementations are loosely organized by topic and grouped into folders. In addition to implementations, each folder contains a README with learning goals and a list of exercises. Both folders and exercises are arranged in increasing order of complexity.
All code is written in Python 3 and implementations are in either TensorFlow or PyTorch.
- 🙇 Libraries: numpy, PyTorch, TensorFlow
- 🎯 Machine learning: linear algebra, non-deep classifiers
- 🔑 Neural net components: backprop, sigmoid, softmax, batchnorm, dropout
- 📚 Natural language processing, word2vec + subwords, NER, neural machine translation, attention
- 🎨 Image classification, convolutional networks, image segmentation, generative models
- 💬 Combined feature representations, VQA, captioning, saliency maps
- Vanilla GAN [code]
- VGG [code]
- Char-level RNN [code]
- Word2Vec [code]
- Simple two-layer neural net [code]
- Numerical gradient checker [code]
- Sigmoid [code]
- Softmax [code]
- Pytorch Exercises [notebook]
- Kyubyong's numpy exercises [notebook]
- fast.ai 1: Practical Deep Learning For Coders
- fast.ai 2: Cutting Edge Deep Learning For Coders
- fast.ai linalg: Computational Linear Algebra for Coders
- CS224d: Deep Learning for Natural Language Processing
- CS231n: Convolutional Neural Networks for Visual Recognition
- https://github.com/tensorflow/models
- https://github.com/dennybritz/models
- http://carpedm20.github.io
Format inspired by Denny Britz (my chief innovation on his format is that I added emojis).