This repository provides code and Jupyter notebooks for a comprehensive video tutorial series on deep learning, covering TensorFlow, Keras, and PyTorch. It's designed for individuals looking to gain hands-on experience with various deep learning architectures and applications, including machine vision, natural language processing, and reinforcement learning.

How It Works

The project offers over 18 hours of video instruction, mirroring the content of the "Deep Learning Illustrated" book. It breaks down deep learning concepts mathematically and through practical code examples using popular libraries. The tutorials progress from foundational concepts like neural network architectures and training to specialized areas like CNNs for vision, RNNs/LSTMs for NLP, GANs for creativity, and deep reinforcement learning.

Quick Start & Requirements

• Installation instructions are available in the installation directory.
• Requires Python and deep learning libraries (TensorFlow, Keras, PyTorch).
• Notebooks are provided in the notebooks directory.
• Links to free YouTube videos for specific lessons are included.

Highlighted Details

• Covers TensorFlow 2, PyTorch, Keras, CNNs, RNNs, LSTMs, GRUs, GANs, and Deep Reinforcement Learning.
• Includes advanced topics like BERT, ELMo, GPT-3, image segmentation, and object detection.
• Offers interactive examples for training and testing models.
• Updated code demos for TensorFlow/Keras.

Maintenance & Community

• The repository is associated with Jon Krohn's video series and book.
• No specific community links (Discord, Slack) or active maintenance signals are provided in the README.

Licensing & Compatibility

• The repository's license is not explicitly stated in the provided README.

Limitations & Caveats

The README does not specify the license, making commercial use or integration into closed-source projects uncertain. The content is based on tutorials released in early 2020, so some library versions or best practices might be dated.