Revised and expanded
| Topic | Slides | Teaching Guide | Code |
|---|---|---|---|
| Overview | - | - | |
| Supervised Learning | - | ||
| Datasets and Dataloaders | - | ||
| Multilayer Perceptron (MLP) | Notebook | ||
| Convolutional Neural Network (CNN) | Notebook | ||
| Recurrent Neural Network (RNN) | Notebook | ||
| Transformer | Notebook | ||
| Mamba | - | SimpleMamba Mamba2 |
|
| Optimization | - | - | |
| Regularization | - | - | |
| Detection | - | - | |
| Segmentation | - | SAM2 | |
| Autoencoder (AE) | - | AE & Denoising AE Colorization AE |
|
| Variational Autoencoder (VAE) | - | VAE and CVAE | |
| Generative Adversarial Network (GAN) | - | DCGAN and CGAN | |
| Normalizing Flows | - | Normalizing Flow | |
| Flow Matching and Diffusion Models | - | DM & Flow Match | |
| Intro to Large Language Models (LLMs) | GPT2-TS-train, GPT2-TS-val | ||
| LLM Data and Model | - | GPT2-TS-ft, GPT2-TS-ft-val Training & Validation Results |
|
| Agents | baby-onit |
| Topic | Note | Video | Code |
|---|---|---|---|
| Development Environment | - | - | |
| Python | - | - | |
| Numpy | - | - | |
| Einsum | - | Notebook | |
| Einops | - | Notebook | |
| PyTorch | - | - | |
| Gradio | - | Notebook Llama Chat |
|
| Efficiency | - | Code | |
| PyTorch Lightning | - | Notebook | |
| Model Packaging | - | ONNX Export ONNX Runtime TorchScript & TensorRT PyTriton Yolo Client PyTriton Yolo Server |
|
| Docker | = | - | |
| HuggingFcae | - | - |
Assuming you already have anaconda or venv, install the required python packages to run the experiments in this version.
pip install -r requirements.txt --upgrade
If you find this work useful, please give it a star, fork, or cite:
@misc{atienza2020dl,
title={Deep Learning Lecture Notes},
author={Atienza, Rowel},
year={2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/roatienza/Deep-Learning-Experiments}},
}