https://cs231n.github.io/

CS231n Convolutional Neural Networks for Visual Recognition Course
Website
These notes accompany the Stanford CS class CS231n: Convolutional
Neural Networks for Visual Recognition. For questions/concerns/bug
reports, please submit a pull request directly to our git repo.
Spring 2024 Assignments
Assignment #1: Image Classification, kNN, SVM, Softmax, Fully
Connected Neural Network
Assignment #2: Fully Connected and Convolutional Nets, Batch
Normalization, Dropout, Pytorch & Network Visualization
Assignment #3: Network Visualization, Image Captioning with RNNs and
Transformers, Generative Adversarial Networks, Self-Supervised
Contrastive Learning
Module 0: Preparation
Software Setup
Python / Numpy Tutorial (with Jupyter and Colab)
Module 1: Neural Networks
Image Classification: Data-driven Approach, k-Nearest Neighbor, train
/val/test splits
L1/L2 distances, hyperparameter search, cross-validation
Linear classification: Support Vector Machine, Softmax
parameteric approach, bias trick, hinge loss, cross-entropy loss, L2
regularization, web demo
Optimization: Stochastic Gradient Descent
optimization landscapes, local search, learning rate, analytic/
numerical gradient
Backpropagation, Intuitions
chain rule interpretation, real-valued circuits, patterns in gradient
flow
Neural Networks Part 1: Setting up the Architecture
model of a biological neuron, activation functions, neural net
architecture, representational power
Neural Networks Part 2: Setting up the Data and the Loss
preprocessing, weight initialization, batch normalization,
regularization (L2/dropout), loss functions
Neural Networks Part 3: Learning and Evaluation
gradient checks, sanity checks, babysitting the learning process,
momentum (+nesterov), second-order methods, Adagrad/RMSprop,
hyperparameter optimization, model ensembles
Putting it together: Minimal Neural Network Case Study
minimal 2D toy data example
Module 2: Convolutional Neural Networks
Convolutional Neural Networks: Architectures, Convolution / Pooling
Layers
layers, spatial arrangement, layer patterns, layer sizing patterns,
AlexNet/ZFNet/VGGNet case studies, computational considerations
Understanding and Visualizing Convolutional Neural Networks
tSNE embeddings, deconvnets, data gradients, fooling ConvNets, human
comparisons
Transfer Learning and Fine-tuning Convolutional Neural Networks
Student-Contributed Posts
Taking a Course Project to Publication Recurrent Neural Networks

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