This repository provides code and notebooks for three "LiveLessons" video series: "Deep Learning with TensorFlow," "Deep Learning for Natural Language Processing," and "Deep Reinforcement Learning and GANs." It's designed for individuals looking to learn and apply deep learning concepts, from foundational neural networks to advanced NLP and reinforcement learning techniques, using TensorFlow and Keras.

How It Works

The project offers a structured curriculum, progressing from basic neural network analogies and TensorFlow fundamentals to complex architectures like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs, GRUs, Generative Adversarial Networks (GANs), and Deep Q-Learning. It emphasizes practical implementation through Jupyter notebooks, covering theory, data preprocessing, model building, and evaluation using libraries like Keras and TensorFlow.

Quick Start & Requirements

• Installation: Step-by-step guides are available in the installation directory.
• Prerequisites: Familiarity with Unix command line basics and Python for data analysis (pandas, scikit-learn, matplotlib) is recommended.
• Resources: Links to DataQuest for Python basics and a command-line tutorial are provided.

Highlighted Details

• Covers foundational deep learning theory, including activation functions, cost functions, gradient descent, and backpropagation.
• Implements classic CNN architectures like LeNet-5, AlexNet, and VGGNet for image classification.
• Explores NLP techniques such as word vectors (word2vec, GloVe), sentiment analysis with dense, CNN, and RNN models.
• Details GANs for image generation and Deep Q-Learning for reinforcement learning tasks using OpenAI Gym.

Maintenance & Community

• The repository is associated with Jon Krohn, who has authored multiple LiveLessons series.
• A second edition of "Deep Learning with TensorFlow" is available, suggesting ongoing development and updates.

Licensing & Compatibility

• The repository itself does not explicitly state a license. The code within the notebooks likely relies on the licenses of the libraries used (TensorFlow, Keras, etc.), which are typically permissive for commercial use.

Limitations & Caveats

• The repository is primarily a companion to video content, and the notebooks may require specific versions of libraries or environments for optimal execution.
• While a second edition exists for one series, the maintenance status of this specific repository is not detailed.