DEEP LEARNING

Deep Learning

These lecture materials for Deep Learning are openly available to everyone.

For students: You are encouraged to use these materials to support your studies.
For instructors: You are welcome to use, modify, and distribute these materials in your teaching.

No credit or reference to us is required.

Topics HTML Keras PyTorch Slides PowerPoints PS Solution

Colab Installation iNote#0_1, iNote#0_2

Introduction pdf#00

Optimization iNote#01 iKeras#01 iTorch#01 pdf#01 pptx#01 PS#01 Solution

Machine Learning iNote#02 iKeras#02 iTorch#02 pdf#02 pptx#02 PS#02 Solution

Gradient Descent iNote#03 iKeras#03 iTorch#03 pdf#03 pptx#03 PS#03 Solution

Overfitting iNote#04 iKeras#04 iTorch#04 pdf#04 pptx#04 PS#04 Solution

Convolutional Neural Networks (CNN) iNote#07 iKeras#07 iTorch#07 pdf#07 pptx#07 PS#07 Solution

Midterm Part 1 Part 2

Pre-trained CNNs and Transfer Learning iNote#09 iKeras#09 iTorch#09 pdf#09 pptx#09 PS#09 Solution

Self-supervised Learning iNote#17 iKeras#17 iTorch#17 pdf#17 pptx#17 PS#17 Solution

Contrastive Learning iNote# iKeras# iTorch# pdf# pptx# PS# Solution

FCN: Segmentation, SR, and Deblurring iNote#10 iKeras#10 iTorch#10 pdf#10 pptx#10 PS#10 Solution

Generative Adversarial Networks (GAN) iNote#12 iKeras#12 iTorch#12 pdf#12 pptx#12 PS#12 Solution

Diffusion Model iNote#18 iKeras#18 iTorch#18 pdf#18 pptx#18 PS#18 Solution

Transformer iNote# iKeras# iTorch# pdf# pptx# PS# Solution

Anomaly Detection iNote#14 iKeras#14 iTorch#14 pdf#14 pptx#14 PS#14 Solution

Physics-informed Neural Networks (PINN) iNote#15 iKeras#15 iTorch#15 pdf#15 pptx#15 PS#15 Solution

Deep Neural Operators iNote#16 iKeras#16 iTorch#16 pdf#16 pptx#16 PS#16 Solution

Final Exam Part 1 Part 2

Final Project Project

Others

XAI: Layer-wise Relevance Propagation (LRP)

Style Transfer

Graph Neural Networks (GNN) iNote#19 iKeras#19 iTorch#19 pdf#19 pptx#19 PS#19

Topics HTML Colab Slides

Fixed-Point Iteration iNote#31 iColab#31 pdf#31

Markov Decision Process (MDP) iNote#33 iColab#33 pdf#33

Reinforcement Learning (RL) iNote#34 iColab#34 pdf#34

Reinforcement Learning Lab pdf#34_lab

Deep Reinforcement Learning (DQN) iNote#35 iColab#35 pdf#35

Deep Reinforcement Learning Lab pdf#35_lab