You're reading from Deep Learning with MXNet Cookbook

Product typeBook

Published inDec 2023

Reading LevelBeginner

PublisherPackt

ISBN-139781800569607

Edition1st Edition

Languages

Python

Tools

MXNet

Concepts

Machine Learning

Author (1)

Andrés P. Torres

Technical requirements

Apart from the technical requirements specified in the Preface, the following technical requirements apply:

Ensure that you have completed the first recipe, Installing MXNet, Gluon, GluonCV and GluonNLP, from Chapter 1, Up and Running with MXNet.
Ensure that you have completed the second recipe, Toy dataset for classification – Loading, Managing, and Visualizing Iris Dataset, from Chapter 2, Working with MXNet and Visualizing Datasets: Gluon and DataLoader.
Most of the concepts for the model, the loss and evaluation functions, and the training were introduced in Chapter 3, Solving Regression Problems. Furthermore, as we will see in this chapter, classification can be seen as a special case of regression. Therefore, it is strongly recommended to complete Chapter 3 first.

The code for this chapter can be found at the following GitHub URL: https://github.com/PacktPublishing/Deep-Learning-with-MXNet-Cookbook/tree/main/ch04.

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Deep Learning with MXNet Cookbook

Published in: Dec 2023Publisher: PacktISBN-13: 9781800569607

Author (1)

Andrés P. Torres

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.
Read more about Andrés P. Torres

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