deep learning mxnet cookbook di torres andrés (13 risultati)

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Paperback. Condizione: new. Paperback. An in-depth analysis into the world of deep learning using Apache MXNet for flexible and efficient research prototyping and deployment to productionKey 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 DescriptionMXNet 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 NLP, CV, RL, 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, GANs, 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 distributed computing.What you will learnUnderstand MXNet and Gluon libraries and their advantagesBuild and train network models from scratch using MXNetApply ready-to-use pre-trained models and learn to fine-tune and apply transfer learning for increased accuracyTrain and evaluate models using GPUs and distributed computingSolve modern computer vision, NLP, RL, and GANs problems using neural network techniquesLearn how vector embeddings can be used for Recommender Systems with a Collaborative Filtering approachLearn about Deep Convolutional GANs architectures and how to apply them to generate photo-realistic synthetic facesWho this book is forThis book is ideal for data scientists, machine learning engineers, and AWS 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. The book helps machine learning engineers and scientists build and train AI models using MXNet/Gluon, covering practical examples to build deep learning models, and apply optimal training and inference techniques. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

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Paperback / softback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 526.

Taschenbuch. Condizione: Neu. 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.

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