Deep Learning (ENGG-6600) - University of Guelph

This is the page for the course Deep Learning (ENGG-6600-07), offered in Fall 2023 at the University of Guelph.

Videos of Lectures:

See videos on Youtube

Lectures

• Course introduction:
  • Course outline: (outline)
  • Course introduction: (slides), (annotated slides)
  • Preliminaries: (slides), (annotated slides)
• One neuron (Perceptron, ADALINE, logistic regression): (slides), (annotated slides)
• One-layer networks (radial basis function, self-organizing map): (slides), (annotated slides)
• Fully connected network: (slides), (annotated slides)
• Backpropagation, stochastic gradient descent, AdaGrad, RMSProp, Adam: (slides), (annotated slides), (code as PDF), (code as ipynb)
• Convolutional neural network: (slides), (annotated slides)
• Important convolutional neural networks: (slides), (annotated slides)
• Regularization in deep learning: (slides), (annotated slides)
• Recurrent neural network (RNN) and long short term memory (LSTM) network: (slides), (annotated slides)
• Attention, transformer, BERT, and GPT: (slides), (annotated slides)
• Deep metric learning: (slides), (annotated slides)
• Deep Reinforcement learning (DRL): (slides), (annotated slides)
• Generative adversarial network (GAN): (slides), (annotated slides)
• Knowledge Distillation (KD): (slides), (annotated slides)