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

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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

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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Sebastian Haubacher Aug 20, 2023

outdated no updates wrong code !!

Amazon Verified review

Gabriele Marchello Oct 16, 2019

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.

AmazonCustomer Apr 11, 2019

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.

Robert P. Oct 20, 2018

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.

WaverlyTN Oct 17, 2018

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".

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

What do you get with eBook?

Practical Convolutional Neural Networks

Introduction to Convolutional Neural Networks

History of CNNs

Convolutional neural networks

Practical example – image classification

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

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