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Hands-On Deep Learning for Images with TensorFlow
Hands-On Deep Learning for Images with TensorFlow

Hands-On Deep Learning for Images with TensorFlow: Build intelligent computer vision applications using TensorFlow and Keras

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Hands-On Deep Learning for Images with TensorFlow

Image Data

In the previous chapter, we prepared our Machine Learning Toolkit, where we set up Keras and Docker in order to allow us to run Jupyter Notebooks to process machine learning.

In this chapter, we're going to look into preparing image data for use with machine learning and the steps that are involved in hooking that into Keras. We're going to start by learning about the MNIST digits. These are handwritten characters in the form of images that we're effectively going to perform Optical Character Recognition (OCR) on with machine learning. Then, we're going to talk about tensors. Tensors sounds like a math word, and it is really, but as a programmer, you've seen multidimensional arrays, so you've actually already been using tensors, and I'll show you the equivalency. Afterward, we're going to turn images into tensors. Images, as you...

MNIST digits

Now, let's learn about MNIST digits. In this section, we'll look at the ImageData notebook that I've prepared to help us understand how to deal with image data; downloading and getting the MNIST digits; looking at images as raw numbers; and then finally, visualizing the actual images based on this numeric data.

The code we're going to be working with is contained in an IPython Notebook. This is the way we've set up our container, so you're going to be running your container like we mentioned at the end of the setting up your Machine Learning Toolkit. I've also prepared an ImageData IPython Notebook that we're going to be working with. We will start off by importing all the necessary packages, and we're going to, turn on Matplotlib in order to automatically plot. This means that when we show an image, we don't have...

Tensors – multidimensional arrays

Now that we've learned a bit about MNIST digits, we're going to take the time to look at a tensor, and what a tensor is. We're going to be looking at a NumPy of multidimensional arrays. Multidimensional arrays are also called tensors. The math vocabulary can be mildly overwhelming, but we're going to show you that it's a lot simpler than you might think. Then, we'll look at tensor shape. Tensor shape is really the number of dimensions, or, in terms of arrays, the number of different indices that you would use to access them. And then finally, we're going to look at datatypes. The tensors, or multidimensional arrays, can hold a wide array of different datatypes, and we'll explain some of the differences.

Let's start with the basics. The most basic tensor you can imagine is a one tensor, which...

Turning images into tensors

In the previous section, we learned a bit about what a tensor is. Now, we're going to use that knowledge to prepare image data as tensors for machine learning. First, we'll ask a question: why are we working with data in floating points? Then, we will learn the difference between samples and the data points at the end of them. Finally, we will normalize the data for use in machine learning.

So, why a floating point? Well, the real reason is that machine learning is fundamentally a math optimization problem, and when we're working with floating points, the computer is trying to optimize a series of mathematical relationships to find learned functions that can then predict outputs. So, preparing our data for machine learning does involve reformatting normal binary data, such as an image, into a series of floating point numbers, which isn...

Turning categories into tensors

In the previous section, we looked at turning images into tensors for machine learning, and in this section, we will look at turning the output values, the categories, into tensors for machine learning.

We will cover output classes, what it means to make a discrete prediction, the concept of one-hot encoding; and then we'll visualize what one-hot encoding looks like as an image, and then we'll recap with a data preparation cookbook, which you should use to be able to deal with all kinds of image data for machine learning.

But for now, let's talk about output. When we're talking about digits, there's 0 through 9, so there's ten different classes, and not classes in the object-oriented sense, but classes in the label sense. Now, with these labels being from 0 to 9 as individual digits, the predictions we want to make...

Summary

In this chapter, we learned about the MNIST digits, and how to acquire them; how tensors are really just multidimensional arrays; how we can encode image data as a tensor; how we can encode categorical or classification data as a tensor; and then we had a quick review and a cookbook approach to think about dimensions and tensors to get data prepared for machine learning.

Now that we've learned how to set up our input and output data for machine learning, we're going to move on to the next chapter, where we will create a Classical Neural Network (CNN).

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Key benefits

  • Discover image processing for machine vision
  • Build an effective image classification system using the power of CNNs
  • Leverage TensorFlow’s capabilities to perform efficient deep learning

Description

TensorFlow is Google’s popular offering for machine learning and deep learning, quickly becoming a favorite tool for performing fast, efficient, and accurate deep learning tasks. Hands-On Deep Learning for Images with TensorFlow shows you the practical implementations of real-world projects, teaching you how to leverage TensorFlow’s capabilities to perform efficient image processing using the power of deep learning. With the help of this book, you will get to grips with the different paradigms of performing deep learning such as deep neural nets and convolutional neural networks, followed by understanding how they can be implemented using TensorFlow. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow and Keras.

Who is this book for?

Hands-On Deep Learning for Images with TensorFlow is for you if you are an application developer, data scientist, or machine learning practitioner looking to integrate machine learning into application software and master deep learning by implementing practical projects in TensorFlow. Knowledge of Python programming and basics of deep learning are required to get the best out of this book.

What you will learn

  • Build machine learning models particularly focused on the MNIST digits
  • Work with Docker and Keras to build an image classifier
  • Understand natural language models to process text and images
  • Prepare your dataset for machine learning
  • Create classical, convolutional, and deep neural networks
  • Create a RESTful image classification server
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Length: 96 pages
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Language : English
ISBN-13 : 9781789538670
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Table of Contents

6 Chapters
Machine Learning Toolkit Chevron down icon Chevron up icon
Image Data Chevron down icon Chevron up icon
Classical Neural Network Chevron down icon Chevron up icon
A Convolutional Neural Network Chevron down icon Chevron up icon
An Image Classification Server Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Jesse Lethe Jun 12, 2019
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Book has an unrealized potential. There are too many inconsistencies. For example, Chapter 1 author demonstrate how to install Docker, which by the way is completely off-topic and unnecessary. Author then mentions that it will install docker on Windows and shows how to get the Windows install. However, lot of the information in Chapter 1 is actually a mess of explanations on Windows and Ubuntu (a distribution of Linux). Is is unreasonable to waste so much time on such a thin book with Docker and present mixing information on Docker, Windows, Linux, and GPUs.The rest of the book is very simplistic and presents the whole subject in a "look-at-my-code" style.
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Gurpreet Chawla Nov 18, 2018
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content which is availBle in internet .No new content
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