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Key benefits

Create scalable deep learning applications using MXNet products with step-by-step tutorials

Implement tasks such as transfer learning, transformers, and more with the required speed and scalability

Analyze model performance and fine-tune for accuracy, scalability, and speed

Purchase of the print or Kindle book includes a free PDF eBook

Description

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. You’ll work with MXNet/Gluon libraries to solve classification and regression problems and gain insights into their inner workings. Venturing further, you’ll 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. You'll 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, you’ll wield the MXNet and Gluon libraries to expertly create and train deep learning networks using GPUs and deploy them in different environments.

Who is this book for?

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.

What you will learn

Grasp the advantages of MXNet and Gluon libraries

Build and train network models from scratch using MXNet

Apply transfer learning for more complex, fine-tuned network architectures

Address modern Computer Vision and NLP problems using neural network techniques

Train state-of-the-art models with GPUs and leverage modern optimization techniques

Improve inference run-times and deploy models in production

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Colour book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Unfortunately, due to several restrictions, we are unable to ship to the following countries:

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Carlos Feb 23, 2024

I recently started this book and I can tell you it is great to learn about Computer Vision and NLP, with tons of code samples. I love "learn-by-building" books, instead of pure-theory ones. This one is a perfect balance.By the way, all these technologies and frameworks are similar among them, so this book is perfect to start on this AI field, as MXNet will not have any further updates. For me, this means a very stable environment, without constantly dealing with broken libs!

Amazon Verified review

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

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