Deep Learning
| Unit | Topic | Materials |
|---|
| 01 | Overview, Linear Algebra, and NDArray | Slides |
| 03 | Derivatives, Backpropagation, and Complexity | Slides |
| 04 | Linear Methods and Optimization | Slides |
| 05 | Maximum Likelihood and Logistic Regression | Slides |
| 06 | Multilayer Perceptrons | Slides |
| 08 | Numerical Stability, Activations, and Hardware | Slides |
| 11 | Convolution and Pooling | Slides |
| 12 | LeNet, AlexNet, VGG, and NiN | Slides |
| 13 | Inception, Batch Normalization, and Residual Networks | Slides |
| 15 | Image Augmentation, Fine-Tuning, and Style Transfer | Slides |
| 16 | Object Detection and Training Techniques | Slides |
| 18 | Sequence Models | Slides |
| 19 | Recurrent Neural Networks | Slides |
| 20 | Advanced Recurrent Neural Networks | Slides |
| 24 | Attention and Vision Transformers | Slides
· ViT |
| 25 | Optimization | Slides |
Language Models
| Unit | Topic | Materials |
|---|
| 00 | Language Model Overview | Slides |
| 01 | Text Preprocessing | Slides |
| 02 | Statistical Language Models | Slides |
| 03 | Word Embeddings | Slides |
| 04 | Sequence-to-Sequence Models | Slides |
| 05 | Pretrained Models | Slides |
3D Vision
Covered topics include ray tracing of volume densities, EWA volume splatting, NeRF, and 3D Gaussian Splatting.