This repository provides a comprehensive collection of TensorFlow 2.x tutorials and practical examples, targeting developers and researchers looking to learn and implement various deep learning models. It covers a wide range of architectures including CNNs, RNNs, GANs, Auto-Encoders, Faster RCNN, GPT, and BERT, offering hands-on code for TensorFlow 2.0 adoption.

How It Works

The project offers practical, runnable code examples for a broad spectrum of deep learning tasks. It leverages TensorFlow 2.x's eager execution and high-level APIs, making it easier to understand and experiment with complex models. The inclusion of diverse architectures like GANs (DCGAN, CycleGAN, WGAN) and advanced models (Faster RCNN, GPT, BERT) demonstrates practical applications of TensorFlow 2.0.

Quick Start & Requirements

• Install: pip install tensorflow -U (CPU) or pip install tensorflow-gpu -U (GPU).
• Prerequisites: Python 3.x, CUDA 10.0+ and cuDNN for GPU support.
• Resources: Requires TensorFlow installation. GPU setup involves CUDA/cuDNN configuration.
• Links: TensorFlow 2.0 Video Tutorial

Highlighted Details

• Winner of the #PoweredByTF 2.0 Challenge.
• Covers foundational to advanced models: Linear Regression, MNIST, CIFAR10, VGG16, Inception, ResNet, Auto-Encoders, GANs, Faster RCNN, GPT, BERT, GCN.
• Demonstrates TensorFlow 2.0 features like eager execution.
• Includes examples for image generation (DCGAN, CycleGAN, WGAN, Pixel2Pixel) and object detection (Faster RCNN).

Maintenance & Community

The repository encourages community contributions via Pull Requests. It acknowledges several referenced repositories, indicating community engagement and building upon existing work.

Licensing & Compatibility

The repository's license is not explicitly stated in the README. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The README does not specify a license, which may impact commercial use. Some examples might require specific dataset downloads or configurations not detailed in the README. The project is presented as a collection of examples rather than a cohesive library, potentially requiring integration effort.