Deep Learning with MXNet Cookbook Discover an extensive collection of recipes for creating and implementing AI

Key FeaturesA step-by-step tutorial towards using MXNet products to create scalable deep learning applicationsImplement tasks such as transfer learning, transformers, and more with the required speed and scalabilityAnalyze the performance of models and fine-tune them for accuracy, scalability, and speedBook Description
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 CV, NLP, 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, picture 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, LSTMs, Transformers, and integrate these models into your applications.
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.
What you will learnUnderstand MXNet and Gluon libraries and their advantagesBuild and train network models from scratch using MXNetApply transfer learning for more complex, fine-tuned network architecturesSolve modern Computer Vision and NLP problems using neural network techniquesTrain and evaluate models using GPUs and learn how to deploy themExplore state-of-the-art models with GPUs and leveraging modern optimization techniquesImprove inference run-times and deploy models in productionWho this book is for
This book is ideal for Data scientists, machine learning engineers, and developers who want to work with Apache MXNet for building fast, scalable deep learning solutions. The reader is expected to have a good understanding of Python programming and a working environment with Python 3.6+. A good theoretical understanding of mathematics for deep learning will be beneficial.
Table of ContentsUp and Running with MXNetWorking with MXNet and Visualizing Datasets - Gluon and DataLoaderSolving Regression ProblemsSolving Classification ProblemsAnalyzing images with Computer VisionUnderstanding text with Natural Language ProcessingOptimizing Models with Transfer Learning and Fine-TuningImproving Training Performance with MXNetImproving Inference Performance with MXNet

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