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

Andrés P. Torres

ISBN 10: 1800569602 / ISBN 13: 9781800569607

Editore: Packt Publishing, 2023

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nach der Bestellung gedruckt Neuware - Printed after ordering - Gain practical, recipe-based insights into the world of deep learning using Apache MXNet for flexible and efficient research prototyping, training, and deployment to production.Key Features:A 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 Learn:Understand 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.

Codice articolo 9781800569607

Dati bibliografici

• Titolo: Deep Learning with MXNet Cookbook : Discover an extensive collection of recipes for creating and implementing AI models on MXNet
• Editore: Packt Publishing
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 1800569602
• ISBN 13: 9781800569607
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 191 A x 20 P (millimetri) 235 millimetri di larghezza per 191 millimetri di altezza per 20 millimetri di profondità
• Peso: 690 grammi

Descrizione articolo

Riassunto

Gain practical, recipe-based insights into the world of deep learning using Apache MXNet for flexible and efficient research prototyping, training, and deployment to production.

Key Features:

• A step-by-step tutorial towards using MXNet products to create scalable deep learning applications
• Implement tasks such as transfer learning, transformers, and more with the required speed and scalability
• Analyze the performance of models and fine-tune them for accuracy, scalability, and speed

Book 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 Learn:

• Understand MXNet and Gluon libraries and their advantages
• Build and train network models from scratch using MXNet
• Apply transfer learning for more complex, fine-tuned network architectures
• Solve modern Computer Vision and NLP problems using neural network techniques
• Train and evaluate models using GPUs and learn how to deploy them
• Explore state-of-the-art models with GPUs and leveraging modern optimization techniques
• Improve inference run-times and deploy models in production

Who 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.

Informazioni sull?autore

Andrés Pérez Torres is a Data Scientist with a specialization in Computer Vision, Geometry, and Deep Learning SOTA models. He has a strong background in Mathematics and Physics. His professional experience includes serving as a Software Development Manager at Amazon Primer Air, leading UK Engineering activities and specializing in CV algorithm design with a full data life-cycle from data acquisition, annotation to model evaluation. He has extensive experience in Amazon training programs and Udacity MOOCs in Deep Learning and Computer Vision.