Deep Learning Lecture Notes and Experiments
2023-2024 Version
The "Deep Learning Lecture Notes and Experiments" is an expansive project tailored for enthusiasts of artificial intelligence and deep learning. This project is organized to demystify various concepts and practical elements involved in deep learning. The latest version, revised and expanded for 2023-2024, includes both theoretical insights and practical experiments, providing a comprehensive guide to deep learning methodologies.
Theory Section
The theory section provides a structured learning path through vital topics in deep learning. Each topic is supported with detailed notes, and some come with interactive code demonstrations:
- Overview establishes foundational knowledge, accessible via a downloadable PDF.
- Supervised Learning dives into the method of training models on labeled data.
- Multilayer Perceptron (MLP) introduces neural networks with multiple layers, supported by a hands-on notebook that uses the popular MNIST dataset for digit recognition.
- Convolutional Neural Network (CNN) explains the workings of these essential models for image processing tasks.
- Recurrent Neural Network (RNN) unpacks models that excel in sequence prediction.
- Transformer covers state-of-the-art deep learning models originally designed for language but applicable across various fields.
- Mamba offers groundbreaking subjects with accessible materials for novel models such as SimpleMamba and Mamba2.
- Optimization and Regularization provide insight into model training refinement for improved accuracy and generalization.
- Autoencoder (AE) and Variational Autoencoder (VAE) delve into models that compress and reconstruct data, with variations for creative tasks like colorizing images.
- Generative Adversarial Network (GAN) highlights methods for generating realistic data samples.
- Introduction to Large Language Models provides insights into the curiously evolving field of large-scale, contextual language understanding.
Practice Section
This section focuses on the practical skills needed for deep learning projects, covering a wide range of tools and methods:
- Development Environment provides guidance on setting up efficient coding environments.
- Python and Numpy are fundamental for numerical computation tasks in deep learning.
- Einsum and Einops are tools that simplify complex tensor manipulations in neural networks.
- PyTorch is highlighted as a leading framework for implementing and testing deep learning models.
- Gradio introduces web-based interfaces for machine learning applications.
- Efficiency, PyTorch Lightning, and Model Packaging & Serving equip developers with techniques to streamline production workflows.
- Docker, HuggingFace, and related tools offer additional resources for model deployment and sharing.
Experiments with LLMs, LVMs, and Agents
This cutting-edge section focuses on advanced experiments:
- Large Language Models (LLMs) such as LangChain, techniques for fine-tuning, and practical tools like Dolly.
- Large Vision Models (LVMs) introduce cutting-edge tools like the Segment Anything Model (SAM) and OpenCLIP.
- Agents and MultiModal Models include experiments with HuggingGPT and ImageBind, enhancing multi-faceted AI interaction capabilities.
- Stable Diffusion experiments show the potential of diffusion models in creating high-quality data samples under controlled settings.
Installation
To execute these experiments, users are directed to install the necessary Python packages with a simple command, assuming the presence of Anaconda or a virtual environment:
pip install -r requirements.txt --upgrade
2022 Version
The 2022 version also offers a trove of learning modules from basic AI tools and datasets to practical applications in vision, speech, and NLP, complete with tutorials and video content for more engaged learning.
Community and Contribution
The project is open for contributions, and learners or professionals finding the content beneficial are encouraged to star, fork, or cite this project using the provided citation format:
@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}},
}
Overall, this project is a valuable resource for anyone looking to deepen their understanding of deep learning and AI, offering both in-depth theoretical knowledge and practical skills.