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Andrés P.
Deep Learning with MXNet Cookbook
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4365858Deep Learning with MXNet Cookbookhttps://www.gandhi.com.mx/deep-learning-with-mxnet-cookbook-9781800562905/phttps://gandhi.vtexassets.com/arquivos/ids/4036791/image.jpg?v=638417538659930000739821MXNPackt PublishingInStock/Ebooks/4294784Deep Learning with MXNet Cookbook739821https://www.gandhi.com.mx/deep-learning-with-mxnet-cookbook-9781800562905/phttps://gandhi.vtexassets.com/arquivos/ids/4036791/image.jpg?v=638417538659930000InStockMXN99999DIEbook20239781800562905_W3siaWQiOiI4MDMwNjNmNC1hNGRlLTRlY2YtYTY2NC0yOGU2MzEwOWFhMzEiLCJsaXN0UHJpY2UiOjgyMSwiZGlzY291bnQiOjgyLCJzZWxsaW5nUHJpY2UiOjczOSwiaW5jbHVkZXNUYXgiOnRydWUsInByaWNlVHlwZSI6Ildob2xlc2FsZSIsImN1cnJlbmN5IjoiTVhOIiwiZnJvbSI6IjIwMjQtMDQtMDhUMTY6MDA6MDBaIiwicmVnaW9uIjoiTVgiLCJpc1ByZW9yZGVyIjpmYWxzZX1d9781800562905_<p><b>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.</b></p><h2>Key Features</h2><ul><li>A step-by-step tutorial towards using MXNet products to create scalable deep learning applications</li><li>Implement tasks such as transfer learning, transformers, and more with the required speed and scalability</li><li>Analyze the performance of models and fine-tune them for accuracy, scalability, and speed</li></ul><h2>Book Description</h2>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. Youll also learn to build and train deep-learning neural network architectures from scratch, before moving on to complex concepts like transfer learning. Youll 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.<h2>What you will learn</h2><ul><li>Understand MXNet and Gluon libraries and their advantages</li><li>Build and train network models from scratch using MXNet</li><li>Apply transfer learning for more complex, fine-tuned network architectures</li><li>Solve modern Computer Vision and NLP problems using neural network techniques</li><li>Train and evaluate models using GPUs and learn how to deploy them</li><li>Explore state-of-the-art models with GPUs and leveraging modern optimization techniques</li><li>Improve inference run-times and deploy models in production</li></ul><h2>Who this book is for</h2><p>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.</p>...(*_*)9781800562905_<p><b>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</b></p><h2>Key Features</h2><ul><li>Create scalable deep learning applications using MXNet products with step-by-step tutorials</li><li>Implement tasks such as transfer learning, transformers, and more with the required speed and scalability</li><li>Analyze model performance and fine-tune for accuracy, scalability, and speed</li><li>Purchase of the print or Kindle book includes a free PDF eBook</li></ul><h2>Book Description</h2>Explore the capabilities of the open-source deep learning framework MXNet to train and deploy neural network models and implement state-of-the-art (SOTA) architectures in Computer Vision, natural language processing, and more. The Deep Learning with MXNet Cookbook is your gateway to constructing fast and scalable deep learning solutions using Apache MXNet. Starting with the different versions of MXNet, this book helps you choose the optimal version for your use and install your library. Youll work with MXNet/Gluon libraries to solve classification and regression problems and gain insights into their inner workings. Venturing further, youll use MXNet to analyze toy datasets in the areas of numerical regression, data classification, picture classification, and text classification. From building and training deep-learning neural network architectures from scratch to delving into advanced concepts such as transfer learning, this book covers it all. Youll master the construction and deployment of neural network architectures, including CNN, RNN, LSTMs, and Transformers, and integrate these models into your applications. By the end of this deep learning book, youll wield the MXNet and Gluon libraries to expertly create and train deep learning networks using GPUs and deploy them in different environments.<h2>What you will learn</h2><ul><li>Grasp the advantages of MXNet and Gluon libraries</li><li>Build and train network models from scratch using MXNet</li><li>Apply transfer learning for more complex, fine-tuned network architectures</li><li>Address modern Computer Vision and NLP problems using neural network techniques</li><li>Train state-of-the-art models with GPUs and leverage modern optimization techniques</li><li>Improve inference run-times and deploy models in production</li></ul><h2>Who this book is for</h2><p>This book is for data scientists, machine learning engineers, and developers who want to work with Apache MXNet for building fast and scalable deep learning solutions. Python programming knowledge and access to a working coding environment with Python 3.6 is necessary to get started. Although not a prerequisite, a solid theoretical understanding of mathematics for deep learning will be beneficial.</p>...9781800562905_Packt Publishinglibro_electonico_9781800562905_9781800562905Andrés P.InglésMéxicohttps://getbook.kobo.com/koboid-prod-public/packt-epub-a1e0c7aa-37ad-4985-90e6-1bb09462aa86.epub2023-12-29T00:00:00+00:00Packt Publishing