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Practical Convolutional Neural Networks
Practical Convolutional Neural Networks

Practical Convolutional Neural Networks: Implement advanced deep learning models using Python

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Profile Icon Mohit Sewak Profile Icon Karim Profile Icon Pradeep Pujari
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€18.99 per month
Full star icon Half star icon Empty star icon Empty star icon Empty star icon 1.5 (6 Ratings)
Paperback Feb 2018 218 pages 1st Edition
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Arrow left icon
Profile Icon Mohit Sewak Profile Icon Karim Profile Icon Pradeep Pujari
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€18.99 per month
Full star icon Half star icon Empty star icon Empty star icon Empty star icon 1.5 (6 Ratings)
Paperback Feb 2018 218 pages 1st Edition
eBook
€15.99 €23.99
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Practical Convolutional Neural Networks

Introduction to Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are everywhere. In the last five years, we have seen a dramatic rise in the performance of visual recognition systems due to the introduction of deep architectures for feature learning and classification. CNNs have achieved good performance in a variety of areas, such as automatic speech understanding, computer vision, language translation, self-driving cars, and games such as Alpha Go. Thus, the applications of CNNs are almost limitless. DeepMind (from Google) recently published WaveNet, which uses a CNN to generate speech that mimics any human voice (https://deepmind.com/blog/wavenet-generative-model-raw-audio/).

In this chapter, we will cover the following topics:

  • History of CNNs
  • Overview of a CNN
  • Image augmentation

History of CNNs

There have been numerous attempts to recognize pictures by machines for decades. It is a challenge to mimic the visual recognition system of the human brain in a computer. Human vision is the hardest to mimic and most complex sensory cognitive system of the brain. We will not discuss biological neurons here, that is, the primary visual cortex, but rather focus on artificial neurons. Objects in the physical world are three dimensional, whereas pictures of those objects are two dimensional. In this book, we will introduce neural networks without appealing to brain analogies. In 1963, computer scientist Larry Roberts, who is also known as the father of computer vision, described the possibility of extracting 3D geometrical information from 2D perspective views of blocks in his research dissertation titled BLOCK WORLD. This was the first breakthrough...

Convolutional neural networks

CNNs, or ConvNets, are quite similar to regular neural networks. They are still made up of neurons with weights that can be learned from data. Each neuron receives some inputs and performs a dot product. They still have a loss function on the last fully connected layer. They can still use a nonlinearity function. All of the tips and techniques that we learned from the last chapter are still valid for CNN. As we saw in the previous chapter, a regular neural network receives input data as a single vector and passes through a series of hidden layers. Every hidden layer consists of a set of neurons, wherein every neuron is fully connected to all the other neurons in the previous layer. Within a single layer, each neuron is completely independent and they do not share any connections. The last fully connected layer, also called the output layer...

Practical example – image classification

The convolutional layer helps to detect regional patterns in an image. The max pooling layer, present after the convolutional layer, helps reduce dimensionality. Here is an example of image classification using all the principles we studied in the previous sections. One important notion is to first make all the images into a standard size before doing anything else. The first convolution layer requires an additional input.shape() parameter. In this section, we will train a CNN to classify images from the CIFAR-10 database. CIFAR-10 is a dataset of 60,000 color images of 32 x 32 size. These images are labeled into 10 categories with 6,000 images each. These categories are airplane, automobile, bird, cat, dog, deer, frog, horse, ship, and truck. Let's see how to do this with the following code:

import keras
import numpy...

Summary

We began this chapter by briefly looking into the history of CNNs. We introduced you to the implementation of visualizing images. 

We studied image classification with the help of a practical example, using all the principles we learned about in the chapter. Finally, we learned how image augmentation helps us avoid overfitting and studied the various other features provided by image augmentation.

In the next chapter, we will learn how to build a simple image classifier CNN model from scratch.

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Key benefits

  • Fast-paced guide with use cases and real-world examples to get well versed with CNN techniques
  • Implement CNN models on image classification, transfer learning, Object Detection, Instance Segmentation, GANs and more
  • Implement powerful use-cases like image captioning, reinforcement learning for hard attention, and recurrent attention models

Description

Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models. This book starts with an overview of deep neural networkswith the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images is available. Later we build upon the learning achieved to build advanced vision related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision. By the end of this book, you should be ready to implement advanced, effective and efficient CNN models at your professional project or personal initiatives by working on complex image and video datasets.

Who is this book for?

This book is for data scientists, machine learning and deep learning practitioners, Cognitive and Artificial Intelligence enthusiasts who want to move one step further in building Convolutional Neural Networks. Get hands-on experience with extreme datasets and different CNN architectures to build efficient and smart ConvNet models. Basic knowledge of deep learning concepts and Python programming language is expected.

What you will learn

  • From CNN basic building blocks to advanced concepts understand practical areas they can be applied to
  • Build an image classifier CNN model to understand how different components interact with each other, and then learn how to optimize it
  • Learn different algorithms that can be applied to Object Detection, and Instance Segmentation
  • Learn advanced concepts like attention mechanisms for CNN to improve prediction accuracy
  • Understand transfer learning and implement award-winning CNN architectures like AlexNet, VGG, GoogLeNet, ResNet and more
  • Understand the working of generative adversarial networks and how it can create new, unseen images

Product Details

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Publication date : Feb 27, 2018
Length: 218 pages
Edition : 1st
Language : English
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Publication date : Feb 27, 2018
Length: 218 pages
Edition : 1st
Language : English
ISBN-13 : 9781788392303
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Table of Contents

10 Chapters
Deep Neural Networks – Overview Chevron down icon Chevron up icon
Introduction to Convolutional Neural Networks Chevron down icon Chevron up icon
Build Your First CNN and Performance Optimization Chevron down icon Chevron up icon
Popular CNN Model Architectures Chevron down icon Chevron up icon
Transfer Learning Chevron down icon Chevron up icon
Autoencoders for CNN Chevron down icon Chevron up icon
Object Detection and Instance Segmentation with CNN Chevron down icon Chevron up icon
GAN: Generating New Images with CNN Chevron down icon Chevron up icon
Attention Mechanism for CNN and Visual Models Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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dr srinivas padmanabhuni Oct 04, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Very practical and example based
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WaverlyTN Oct 17, 2018
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Just glancing through, it was interesting to see that that they had blatantly stolen, word for word, whole paragraphs and even pages from other authors. For example, their page #80 is a verbatim copy of page #273 of the book "Hands-on Machine Learning with Scikit-Learn & TensorFlow".
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Robert P. Oct 20, 2018
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This book is a waste of time and money.In this book, convolutional neural networks are not explained. The authors give you an idea what these networks might be, but they remain extremely vague.For example, there is one instance, where they talk about settings for a convolutional layer, where they mentoin a variable and basically state, that this is another variable to be set. They do not even attempt to give the reader any idea, what this variable does.This book contains lots of flawed code. In the beginning, the reader will be eager to try and fix the bugs in the code, but at some point, he/she will give up, because the code is totally buggy.The authors have a github page, where they offer code from the book, which is slightly different than in the book, but also, this code does not run.
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AmazonCustomer Apr 11, 2019
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Currently on Chapter 1 and I've already come across several errors in the code snippets from the book including typos and codes that downright don't work. There's even a piece of code that the authors mentioned would throw an error but it ran perfectly fine. I've spent more time on Google and Tensorflow's website trying to figure out why the code isn't working according to the book. It's obvious the editors and the publishers of the book spent very little time on it.
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Gabriele Marchello Oct 16, 2019
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Maybe the worst book ever bought!Badly written, published in a rush without double-checking anything, not even the codes!Wrong pictures, no links for download reported, poorly designed neural networks.If I hadn't underlined a lot of things I would definitely return it.
Amazon Verified review Amazon
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