#Tensorboard

This repository offers a PyTorch implementation of FastSpeech 2 using NVIDIA's Tacotron 2 preprocessing and MelGAN vocoder for enhanced audio synthesis. It leverages Espnet's framework for FastSpeech 2 replication with room for performance adjustments. Key features include phoneme-based synthesis and TorchScript export. Contributions are invited to extend its high-quality audio promise. Essential documentation and samples are provided for further exploration.

The project provides modular implementations of essential deep reinforcement learning algorithms using Keras suitable for discrete and continuous action spaces. It features Actor-Critic approaches like A2C and A3C and Deep Q-Learning variations such as DDQN with prioritized experience replay and dueling networks. Requiring Keras 2.1.6 and OpenAI Gym, it facilitates efficient and scalable setups, with tools for visualization and monitoring through TensorBoard and Plotly, focusing on stability and exploration in complex environments.

Delve into the second edition official codebase of O'Reilly's Generative Deep Learning book to discover methods for automating creative processes in machines. Learn about key models such as Variational Autoencoders and Generative Adversarial Networks and implement them using practical tutorials with Kaggle datasets and Docker. The guide features integration with Tensorboard for real-time monitoring and utilizes Keras resources, adaptable for local or cloud computing environments.

TensorFlow Ranking is an in-depth library for Learning-to-Rank (LTR) on the TensorFlow platform. It includes key loss functions and metrics like NDCG, supports innovative scoring methods and LambdaLoss optimization. With interactive demos and a variety of installation options, it simplifies the development of cutting-edge ranking models.