This repository is simply me working through CS231n: Convolutional Neural Networks for Visual Recognition (Winter 2016).
There is nothing to see here.
But if you’re even vaguely interested in this topic, you should probably take this class. It is outstanding.
Harish Narayanan, 2016
- Lecture 1: Intro to computer vision, historical context
- Lecture 2: Image classification and the data-driven approach; k-nearest neighbors; Linear classification I
- Lecture 3: Linear classification II; Higher-level representations, image features; Optimization, stochastic gradient descent
- Lecture 4: Backpropagation; Introduction to neural networks
- 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
- Lecture 6: Training Neural Networks Part 2: parameter updates, ensembles, dropout; Convolutional Neural Networks: intro
- Lecture 7: Convolutional Neural Networks: architectures, convolution / pooling layers; Case study of ImageNet challenge winning ConvNets
- Lecture 8: ConvNets for spatial localization; Object detection
- Lecture 9: Understanding and visualizing Convolutional Neural Networks; Backprop into image: Visualizations, deep dream, artistic style transfer; Adversarial fooling examples
- 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
- Lecture 11: Training ConvNets in practice; Data augmentation, transfer learning; Distributed training, CPU/GPU bottlenecks; Efficient convolutions
- Lecture 12: Overview of Caffe/Torch/Theano/TensorFlow
- 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
- Lecture 14: ConvNets for videos; Unsupervised learning
- Invited Lecture: A sampling of deep learning at Google
- Lecture 15: Conclusions