Lecture Slides [2019]
- 01 – Introduction
- 02 – Linear Classification
- 03 – Gradient and Back Propagation
- 04 – Deep Neural Networks
- 05 – Convolutional Neural Network
- 06 – Advanced Optimizations
- 07 – Architectures and Case Studies
- 08 – Visualization and Understanding
- 09 – Generative Models
- 10 – Localization and Detection
- 11 – Recurrent Neural Networks
- 12 – Segmentation
- 13 – Deep Learning in 2019
Exercises [2019]
- 00 – NumPy Exercise
- 01 – Linear Classification
- 02 – Neural Networks
- 03 – Deep Neural Network
- 04 – Layer-wise Pretraining
- 05 – Convolutional Neural Network
- 06 – Advanced Optimization
- 07 – Keras and TensorFlow
- 08 – PyTorch
- 09 – CNN Architectures
- 10 – Keras Model (TensorFlow)
- 11 – Transfer Learning
- 12 – Augmentation and Acceleration
- 13 – Network Visualization
- 14 – Neural Style Transfer (PyTorch)
- 15 – Variational AutoEncoder (TensorFlow)
- 16 – Generative Adversarial Network (PyTorch)
- 17 – YOLO Detection
- 18 – Recurrent Neural Network
- 19 – Long Short-term Memory