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Deep Learning with MXNet Cookbook

You're reading from  Deep Learning with MXNet Cookbook

Product type Book
Published in Dec 2023
Publisher Packt
ISBN-13 9781800569607
Pages 370 pages
Edition 1st Edition
Languages
Author (1):
Andrés P. Torres Andrés P. Torres
Profile icon Andrés P. Torres

Table of Contents (12) Chapters

Preface 1. Chapter 1: Up and Running with MXNet 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 11. Other Books You May Enjoy

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “We will store the computation time in five dictionaries, one for each compute profile (timings_np, timings_mx_cpu, and timings_mx_gpu).”

A block of code is set as follows:

import mxnet
mxnet.__version__
features = mxnet.runtime.Features()
print(features)
print(features.is_enabled('CUDA'))
print(features.is_enabled('CUDNN'))
print(features.is_enabled('MKLDNN'))

Any command-line input or output is written as follows:

!python3 -m pip install gluoncv gluonnlp
!python3 -m pip install gluoncv gluonnlp

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “For this step, we will use the pyplot module from a library called Matplotlib, which will allow us to create charts easily.”

Tips or important notes

Appear like this.

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