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Deep Learning with MXNet Cookbook

You're reading from Deep Learning with MXNet Cookbook Discover an extensive collection of recipes for creating and implementing AI models on MXNet

Product type Paperback

Published in Dec 2023

Publisher Packt

ISBN-13 9781800569607

Length 370 pages

Edition 1st Edition

Languages

Python

Tools

MXNet

Concepts

Deep Learning

Author (1):

Andrés P. Torres

View More author details

Table of Contents (12) Chapters

Preface

1. Chapter 1: Up and Running with MXNet FREE CHAPTER

2. Chapter 2: Working with MXNet and Visualizing Datasets – Gluon and DataLoader

3. Chapter 3: Solving Regression Problems

4. Chapter 4: Solving Classification Problems

5. Chapter 5: Analyzing Images with Computer Vision

6. Chapter 6: Understanding Text with Natural Language Processing

7. Chapter 7: Optimizing Models with Transfer Learning and Fine-Tuning

8. Chapter 8: Improving Training Performance with MXNet

9. Chapter 9: Improving Inference Performance with MXNet

10. Index

Why subscribe?

11. Other Books You May Enjoy

Who is this book 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 3.7+. A good theoretical understanding of mathematics for deep learning will be beneficial.

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Authors (1)

Andrés P. Torres

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.

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