Deep Learning Lecture Notes and Experiments

2026 Version

Revised and expanded

Theory

Topic	Slides	Teaching Guide	Code
Overview	PDF	-	-
Supervised Learning	PDF	PDF	-
Datasets and Dataloaders	PDF	PDF	-
Multilayer Perceptron (MLP)	PDF	PDF	Notebook
Convolutional Neural Network (CNN)	PDF	PDF	Notebook
Recurrent Neural Network (RNN)	PDF	PDF	Notebook
Transformer	PDF	PDF	Notebook
Mamba	PDF	-	SimpleMamba Mamba2
Optimization	PDF	-	-
Regularization	PDF	-	-
Detection	PDF	-	-
Segmentation	PDF	-	SAM2
Autoencoder (AE)	PDF	-	AE & Denoising AE Colorization AE
Variational Autoencoder (VAE)	PDF	-	VAE and CVAE
Generative Adversarial Network (GAN)	PDF	-	DCGAN and CGAN
Normalizing Flows	PDF	-	Normalizing Flow
Flow Matching and Diffusion Models	PDF	-	DM & Flow Match
Intro to Large Language Models (LLMs)	PDF	PDF	GPT2-TS-train, GPT2-TS-val
LLM Data and Model	PDF	-	GPT2-TS-ft, GPT2-TS-ft-val Training & Validation Results
Agents	PDF	PDF	baby-onit

Practice

Topic	Note	Video	Code
Development Environment	PDF	-	-
Python	PDF	-	-
Numpy	PDF	-	-
Einsum	PDF	-	Notebook
Einops	PDF	-	Notebook
PyTorch	PDF	-	-
Gradio	PDF	-	Notebook Llama Chat
Efficiency	PDF	-	Code
PyTorch Lightning	PDF	-	Notebook
Model Packaging	PDF	-	ONNX Export ONNX Runtime TorchScript & TensorRT PyTriton Yolo Client PyTriton Yolo Server
Docker	PDF	=	-
HuggingFcae	PDF	-	-

Install

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

Star, Fork, Cite

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}},
}