Understanding Convolutional Neural Networks

This repository is an archive of the course CS231n: Convolutional Neural Networks for Visual Recognition (Winter 2016). If you’re even vaguely interested in this topic, you should probably take this class. It is outstanding.

To use this repository, make a fork of it and then tick off the items in the following syllabus as you complete them. (You can tick off items by replacing [ ] with [x] in README.md.)

Happy learning!

Harish Narayanan, 2017

Course Syllabus

• [ ] Lecture 1: Intro to computer vision, historical context
  • [ ] Video
  • [ ] Slides
• [ ] Lecture 2: Image classification and the data-driven approach; k-nearest neighbors; Linear classification I
  • [ ] Video
  • [ ] Slides
  • [ ] Python/NumPy tutorial
  • [ ] Image classification notes
  • [ ] Linear classification notes
• [ ] Lecture 3: Linear classification II; Higher-level representations, image features; Optimization, stochastic gradient descent
  • [ ] Video
  • [ ] Slides
  • [ ] Linear classification notes
  • [ ] Optimization notes
• [ ] Lecture 4: Backpropagation; Introduction to neural networks
  • [ ] Video
  • [ ] Slides
  • [ ] Backprop notes
  • [ ] Related references
    • [ ] Efficient Backprop -- 3/44
    • [ ] Automatic differentiation survey
    • [ ] Calculus on Computational Graphs
    • [ ] Backpropagation Algorithm
    • [ ] Learning: Neural Nets, Back Propagation
• [ ] Assignment 1
  • [ ] k-Nearest Neighbor classifier
  • [ ] Training a Support Vector Machine
  • [ ] Implement a Softmax classifier
  • [ ] Two-Layer Neural Network
  • [ ] Higher Level Representations: Image Features
  • [ ] Cool Bonus: Do something extra!
• [ ] Lecture 5: Training Neural Networks Part 1; Activation functions, weight initialization, gradient flow, batch normalization; Babysitting the learning process, hyperparameter optimization
  • [ ] Video
  • [ ] Slides
  • [ ] Neural Nets notes 1
  • [ ] Neural Nets notes 2
  • [ ] Neural Nets notes 3
  • [ ] Related references
    • [ ] Tips/Tricks 1
    • [ ] Tips/Tricks 2
    • [ ] Tips/Tricks 3
    • [ ] Deep learning review article
• [ ] Lecture 6: Training Neural Networks Part 2: parameter updates, ensembles, dropout; Convolutional Neural Networks: intro
  • [ ] Video
  • [ ] Slides
  • [ ] Neural Nets notes 3
• [ ] Lecture 7: Convolutional Neural Networks: architectures, convolution / pooling layers; Case study of ImageNet challenge winning ConvNets
  • [ ] Video
  • [ ] Slides
  • [ ] ConvNet notes
• [ ] Lecture 8: ConvNets for spatial localization; Object detection
  • [ ] Video
  • [ ] Slides
• [ ] Lecture 9: Understanding and visualizing Convolutional Neural Networks; Backprop into image: Visualizations, deep dream, artistic style transfer; Adversarial fooling examples
  • [ ] Video
  • [ ] Slides
• [ ] Assignment 2
  • [ ] Fully-connected Neural Network
  • [ ] Batch Normalization
  • [ ] Dropout
  • [ ] ConvNet on CIFAR-10
  • [ ] Do something extra!
• [ ] Lecture 10: Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM); RNN language models; Image captioning
  • [ ] Video
  • [ ] Slides
  • [ ] Related references
    • [ ] Recurrent neural networks
    • [ ] Min Char RNN
    • [ ] Char RNN
    • [ ] NeuralTalk2
• [ ] Lecture 11: Training ConvNets in practice; Data augmentation, transfer learning; Distributed training, CPU/GPU bottlenecks; Efficient convolutions
  • [ ] Video
  • [ ] Slides
• [ ] Lecture 12: Overview of Caffe/Torch/Theano/TensorFlow
  • [ ] Video
  • [ ] Slides
• [ ] Assignment 3
  • [ ] Image Captioning with Vanilla RNNs
  • [ ] Image Captioning with LSTMs
  • [ ] Image Gradients: Saliency maps and Fooling Images
  • [ ] Image Generation: Classes, Inversion, DeepDream
  • [ ] Do something extra!
• [ ] Lecture 13: Segmentation; Soft attention models; Spatial transformer networks
  • [ ] Video
  • [ ] Slides
• [ ] Lecture 14: ConvNets for videos; Unsupervised learning
  • [ ] Video
  • [ ] Slides
• [ ] Invited Lecture: A sampling of deep learning at Google
  • [ ] Video
• [ ] Lecture 15: Conclusions
  • [ ] Slides