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