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Deep Learning with MXNet Cookbook
Deep Learning with MXNet Cookbook

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

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Profile Icon Andrés P. Torres
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Full star icon Full star icon Full star icon Full star icon Full star icon 5 (1 Ratings)
Paperback Dec 2023 370 pages 1st Edition
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Arrow left icon
Profile Icon Andrés P. Torres
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Full star icon Full star icon Full star icon Full star icon Full star icon 5 (1 Ratings)
Paperback Dec 2023 370 pages 1st Edition
eBook
S$36.99 S$53.99
Paperback
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eBook
S$36.99 S$53.99
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Deep Learning with MXNet Cookbook

Working with MXNet and Visualizing Datasets – Gluon and DataLoader

In the previous chapter, we learned how to set up MXNet. We also verified how MXNet could leverage our hardware to provide maximum performance. Before applying deep learning (DL) to solve specific problems, we need to understand how to load, manage, and visualize the datasets we will be working with. In this chapter, we will start using MXNet to analyze some toy datasets in the domains of numerical regression, data classification, image classification, and text classification. To manage those tasks efficiently, we will see new MXNet libraries and functions such as Gluon (an API for DL) and DataLoader.

In this chapter, we will cover the following topics:

  • Understanding regression datasets – loading, managing, and visualizing the House Sales dataset
  • Understanding classification datasets – loading, managing, and visualizing the Iris dataset
  • Understanding image datasets – loading...

Technical requirements

Apart from the technical requirements specified in the Preface, no other requirements apply to this chapter.

The code for this chapter can be found at the following GitHub URL: https://github.com/PacktPublishing/Deep-Learning-with-MXNet-Cookbook/tree/main/ch02

Furthermore, you can directly access each recipe from Google Colab; for example, for the first recipe of this chapter, visit https://colab.research.google.com/github/PacktPublishing/Deep-Learning-with-MXNet-Cookbook/blob/main/ch02/2_1_Toy_Dataset_for_Regression_Load_Manage_and_Visualize_House_Sales_Dataset.ipynb.

Understanding regression datasets – loading, managing, and visualizing the House Sales dataset

The training process of machine learning (ML) models can be divided into three main sub-groups:

  • Supervised learning (SL): The expected outputs are known for at least some data
  • Unsupervised learning (UL): The expected outputs are not known but the data has some features that could help with understanding its internal distribution
  • Reinforcement learning (RL): An agent explores the environment and makes decisions based on the inputs acquired from the environment

There is also an approach that falls in between the first two sub-groups called weakly SL, where there are not enough known outputs to follow an SL approach for one of the following reasons:

  • The outputs are inaccurate
  • Only some of the output features are known (incomplete)
  • They are not exactly the expected outputs but are connected/related to the task we intend to achieve (inexact)
...

Understanding classification datasets – loading, managing, and visualizing the Iris dataset

In the previous recipe, we studied one of the most common problem types in SL: regression. In this recipe, we will take a closer look at another of these problem types: classification.

In classification problems, we want to estimate a categorial output, a class, from a set of given classes, using a variable number of input features. In this recipe, we will analyze a toy classification dataset from Kaggle: the Iris dataset, one of the most renowned classification datasets.

The Iris dataset presents the problem of estimating the iris class of the flower of plants, from three classes (iris setosa, iris versicolor, and iris virginica) with the help of the following four features:

  • Sepal length (in cm)
  • Sepal width (in cm)
  • Petal length (in cm)
  • Petal width (in cm)

These data features are provided for 150 flowers, with 50 instances for each of the 3 classes (making...

Understanding image datasets – loading, managing, and visualizing the Fashion-MNIST dataset

One of the fields that has grown considerably in DL in the last years has been computer vision (CV). Since the AlexNet revolution in 2012, CV has expanded from lab research to surpassing human performance in real-world datasets (known as “in the wild”).

In this recipe, we will explore the simplest CV task: image classification. Given a set of images, our task is to correctly classify that image among a given set of labels (classes).

One of the most classic image classification datasets is the MNIST (which stands for the Modified National Institute of Standards and Technology) database. Similarly sized, but more suited for current CV analysis, is the Fashion-MNIST dataset. This dataset is a multi-label image classification dataset, with a training set of 60k examples and a test set of 10k examples, with each example belonging to 1 of these 10 categories (starting with...

Understanding text datasets – loading, managing, and visualizing the Enron Email dataset

Another field that has grown considerably in DL in recent years is natural language processing (NLP). Similarly to CV, this field aims to surpass human performance in real-world datasets.

In this recipe, we will explore one of the simplest NLP tasks: text classification. Given a set of sentences and paragraphs, our task is to correctly classify that text among a given set of labels (classes).

One of the most classic text classification tasks is to distinguish whether received email is spam or not (ham). These datasets are binary text classification datasets (only two labels to assign, 0 and 1, or ham and spam).

In our specific scenario, we will use a real-world email dataset. This set of emails was made public during the investigation of the Enron scandal in the early 2000s by the US Government. This dataset was first published in 2004 and is composed of emails from ~150 users,...

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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

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Length: 370 pages
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ISBN-13 : 9781800569607
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Table of Contents

11 Chapters
Chapter 1: Up and Running with MXNet Chevron down icon Chevron up icon
Chapter 2: Working with MXNet and Visualizing Datasets – Gluon and DataLoader Chevron down icon Chevron up icon
Chapter 3: Solving Regression Problems Chevron down icon Chevron up icon
Chapter 4: Solving Classification Problems Chevron down icon Chevron up icon
Chapter 5: Analyzing Images with Computer Vision Chevron down icon Chevron up icon
Chapter 6: Understanding Text with Natural Language Processing Chevron down icon Chevron up icon
Chapter 7: Optimizing Models with Transfer Learning and Fine-Tuning Chevron down icon Chevron up icon
Chapter 8: Improving Training Performance with MXNet Chevron down icon Chevron up icon
Chapter 9: Improving Inference Performance with MXNet Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Carlos Feb 23, 2024
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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!
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