This repository documents a deep learning study group for professionals and academics, offering a structured curriculum and practical implementation examples. It serves as a knowledge-sharing platform for understanding neural networks from theory to application, with a focus on Python-based frameworks like TensorFlow and PyTorch.

How It Works

The group follows a three-pronged approach: theoretical study of foundational mathematics and machine learning concepts, practical application through coding tutorials and real-world problem-solving, and knowledge sharing via member presentations on projects and expertise. This blended methodology aims to foster a deep and practical understanding of deep learning.

Quick Start & Requirements

• Installation: Not applicable; this is a documentation and knowledge-sharing repository.
• Prerequisites: Python, NumPy, TensorFlow, PyTorch (for application examples).
• Resources: Access to the session notes and presentation materials within the repository.

Highlighted Details

• Comprehensive coverage of core deep learning topics including Neural Networks, Computer Vision (CS231n), Natural Language Processing (CS224N), and Reinforcement Learning.
• Practical implementation examples using Python libraries like NumPy, TensorFlow, and PyTorch.
• Detailed session notes and summaries available for each meeting.
• Insights into industry applications and investment trends in deep learning.

Maintenance & Community

• The study group is organized by Jon Krohn.
• Meetings were hosted and subsidized by untapt.
• The group has reached capacity, with a waiting list available via contact with the organizer.

Licensing & Compatibility

• The repository content is not explicitly licensed in the provided README.
• Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The repository primarily serves as a historical record of study group sessions and does not provide a direct, runnable codebase for deep learning tasks. The group has reached capacity, limiting new direct participation.