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  • Format: ePub

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
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Produktbeschreibung
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.


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Autorenporträt
Andrés P. Torres, is the Head of Perception at Oxa, a global leader in industrial autonomous vehicles, leading the design and development of State-Of The-Art algorithms for autonomous driving. Before, Andrés had a stint as an advisor and Head of AI at an early-stage content generation startup, Maekersuite, where he developed several AI-based algorithms for mobile phones and the web. Prior to this, Andrés was a Software Development Manager at Amazon Prime Air, developing software to optimize operations for autonomous drones. I want to especially thank Marta M. Civera for most of the illustrations in Chapter 5, another example of her wonderful skills, apart from being a fantastic Architect and, above all, partner.