Lecture Slides
- 00 – Preparation
- 01 – Introduction
- 02 – Linear Classification
- 03 – Gradient and Backpropagation
- 04 – Artificial Neural Networks
- 05 – Advanced Optimization
- 06 – Convolutional Neural Network
- 07 – CNN Case Studies
- 08 – CNN in Practice
- 09 – Deep Learning Libraries
- 10 – Visualization and Understanding
- 11 – Generative Adversarial Network
- 12 – Localization and Detection
- 13 – Recurrent Neural Net
- 14 – Segmentation
Exercises [2018]
- 00 – Introduction Exercises.ipynb
- 00 – Introduction.ipynb
- 01 – Linear CLassification.ipynb
- 02 – Neural Network.ipynb
- 03 – Deep Neural Network.ipynb
- 04 – Convolutional Neural Network.ipynb
- 05 – Advanced Optimization.ipynb
- 06 – PyTorch.ipynb
- 07 – Network Visualization (PyTorch).ipynb
- 07 – Network Visualization (Keras).ipynb
- 08 – Neural Style Transfer.ipynb
- 09 – Generative Adversarial Network.ipynb
- 10 – Recurrent Neural Net.ipynb
- 11 – Long Short Term Memory.ipynb
Class Exercises [2018]
- 01 – [Class Exercise] Linear Classification.ipynb
- 01 – [Class Exercise] Nearest Neighbor.ipynb
- 02 – [Class Exercise] Gradient Descent and Neural Network.ipynb
- 03 – [Class Exercise] Batch Normalization.ipynb
- 03 – [Class Exercise] Neural Network Problems.ipynb
- 03 – [Class Exercise] Neural Network using Library.ipynb
- 05 – [Class Exercise] CNN Case Studies.ipynb
- 06 – [Class Exercise] Optimization Schemas.ipynb
- 07 – [Class Exercise] Transfer Learning.ipynb
- 08 – [Class Exercise] AutoEncoder.ipynb
- 09 – [Class Exercise] Network Visualization.ipynb
- 10 – [Class Exercise] Generative Adversarial Network.ipynb