Chapter 1. Deep Learning Walkthrough and PyTorch Introduction
At this point in time, there are dozens of deep learning frameworks out there that are capable of solving any sort of deep learning problem on GPU, so why do we need one more? This book is the answer to that million-dollar question. PyTorch came to the deep learning family with the promise of being NumPy on GPU. Ever since its entry, the community has been trying hard to keep that promise. As the official documentation says, PyTorch is an optimized tensor library for deep learning using GPUs and CPUs. While all the prominent frameworks offer the same thing, PyTorch has certain advantages over almost all of them.
The chapters in this book provide a step-by-step guide for developers who want to benefit from the power of PyTorch to process and interpret data. You'll learn how to implement a simple neural network, before exploring the different stages of a deep learning workflow. We'll dive into basic convolutional networks and generative adversarial networks, followed by a hands-on tutorial on how to train a model with OpenAI's Gym library. By the final chapter, you'll be ready to productionize PyTorch models.
In this first chapter, we will go through the theory behind PyTorch and explain why PyTorch gained the upper hand over other frameworks for certain use cases. Before that, we will take a glimpse into the history of PyTorch and learn why PyTorch is a need rather than an option. We'll also cover the NumPy-PyTorch bridge and PyTorch internals in the last section, which will give us a head start for the upcoming code-intensive chapters.
