Preface

MXNet is an open-source deep learning framework that allows you to train and deploy neural network models and implement state-of-the-art (SOTA) architectures in Computer Vision, Natural Language Processing, and more. With this cookbook, you will be able to construct fast, scalable deep learning solutions using Apache MXNet.

This book will start by showing you the different versions of MXNet and what version to choose before installing your library. You will learn to start using MXNet/Gluon libraries to solve classification and regression problems and get an idea on the inner workings of these libraries. This book will also show how to use MXNet to analyze toy datasets in the areas of numerical regression, data classification, image classification, and text classification. You’ll also learn to build and train deep-learning neural network architectures from scratch, before moving on to complex concepts like transfer learning. You’ll learn to construct and deploy neural network architectures including CNN, RNN, Transformers, and integrate these models into your applications. You will also learn to analyze the performance of these models, and fine-tune them for increased accuracy, scalability, and speed.

By the end of the book, you will be able to utilize the MXNet and Gluon libraries to create and train deep learning networks using GPUs and learn how to deploy them efficiently in different environments.