This repository contains course materials for "T81-558: Applications of Deep Neural Networks" at Washington University in St. Louis, taught by Jeff Heaton. It provides a comprehensive curriculum for students to learn deep learning concepts and their practical applications using PyTorch, targeting individuals with some programming background seeking to understand and implement modern neural network architectures.

How It Works

The course material is structured into modules covering Python fundamentals for machine learning, PyTorch basics, and various deep learning architectures like CNNs, LSTMs, GRUs, GANs, and Transformers. It emphasizes practical application across computer vision, NLP, time series analysis, and generative models, with a focus on using PyTorch for implementation and exploring high-performance computing aspects with GPUs.

Quick Start & Requirements

• Installation: Primarily involves setting up a Python environment with PyTorch. Specific instructions for installing dependencies are not detailed in the README, but typical requirements include Python 3.x, pip, and PyTorch.
• Prerequisites: Familiarity with at least one programming language is assumed. Access to GPUs is beneficial for performance but not explicitly stated as mandatory.
• Resources: Links to datasets are provided. The course is delivered in a hybrid format.

Highlighted Details

• Covers a broad range of deep learning architectures and applications, including CNNs, LSTMs, GANs, Transformers, and Reinforcement Learning.
• Focuses on practical implementation using PyTorch and explores topics like prompt engineering, fine-tuning models (e.g., Dreambooth), and deployment.
• Includes modules on leveraging libraries like Pandas, RAPIDS, Hugging Face, and Stable Baselines.
• Features a Kaggle competition and project-based learning, culminating in presentations.

Maintenance & Community

The repository is associated with Washington University in St. Louis and instructor Jeff Heaton. No specific community channels or active maintenance signals are mentioned in the README.

Licensing & Compatibility

The repository's licensing is not specified in the provided README.

Limitations & Caveats

The README does not detail specific version requirements for Python or PyTorch, nor does it provide explicit installation instructions beyond the general framework. The hybrid delivery format might require specific logistical considerations for remote participants.