Lecture Notes

Template

Additional Notes

Matrix Calculus

Computational Graphs

Back-propagation and Vector Derivatives (UMich)

Module 1: Introduction to Neural Networks

Linear Classifiers and Gradient Descent

Neural Networks

Optimization of Deep Neural Networks

Data Wrangling

Module 2: Convolutional Neural Networks

Convolution and Pooling Layers

Convolutional Neural Network Architectures

Visualization of Neural Networks

Advanced CV Architectures

Module 3: Structured Neural Representations

Structures and Structured Representations

Language Models

Neural Attention Models