Book Image

Mastering PyTorch - Second Edition

By : Ashish Ranjan Jha
4 (1)
Book Image

Mastering PyTorch - Second Edition

4 (1)
By: Ashish Ranjan Jha

Overview of this book

PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.
Table of Contents (21 chapters)
20
Index

Understanding deep Q-learning

Instead of creating a Q-values table, DQN uses a deep neural network (DNN) that outputs a Q-value for a given state-action pair. DQN is used with complex environments such as video games, where there are far too many states for them to be managed in a Q-values table. The current image frame of the video game is used to represent the current state and is fed as input to the underlying DNN model, together with the current action.

The DNN outputs a scalar Q-value for each such input. In practice, instead of just passing the current image frame, N number of neighboring image frames in a given time window are passed as input to the model.

We are using a DNN to solve an RL problem. This has an inherent concern. While working with DNNs, we have always worked with independent and identically distributed (iid) data samples. However, in RL, every current output impacts the next input. For example, in the case of Q-learning, the Bellman equation itself suggests that...