This repository is simply me working through CS231n: Convolutional Neural Networks for Visual Recognition (Winter 2016), a course that’s got rave reviews everywhere.
There is nothing to see here.
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
- Lecture 10:
- Lecture 11:
- Lecture 12:
- Lecture 13:
- Lecture 14:
- Lecture 15: