Hands-On Convolutional Neural Networks with TensorFlow: Solve computer vision problems with modeling in TensorFlow and Python

by Iffat Zafar, Giounona Tzanidou, Richard Burton, Nimesh Patel, Leonardo Araujo

Overview

Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time!

We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation.

After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks.

Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images.

Product Details

ISBN-13:	9781789132823
Publisher:	Packt Publishing
Publication date:	08/28/2018
Sold by:	Barnes & Noble
Format:	eBook
Pages:	272
File size:	29 MB
Note:	This product may take a few minutes to download.

About the Author

Iffat Zafar was born in Pakistan. She received her Ph.D. from the Loughborough University in Computer Vision and Machine Learning in 2008. After her Ph.D. in 2008, she worked as research associate at the Department of Computer Science, Loughborough University, for about 4 years. She currently works in the industry as an AI engineer, researching and developing algorithms using Machine Learning and Deep Learning for object detection and general Deep Learning tasks for edge and cloud-based applications. Giounona Tzanidou is a PhD in computer vision from Loughborough University, UK, where she developed algorithms for runtime surveillance video analytics. Then, she worked as a research fellow at Kingston University, London, on a project aiming at prediction detection and understanding of terrorist interest through intelligent video surveillance. She was also engaged in teaching computer vision and embedded systems modules at Loughborough University. Now an engineer, she investigates the application of deep learning techniques for object detection and recognition in videos. Richard Burton graduated from the University of Leicester with a master's degree in mathematics. After graduating, he worked as a research engineer at the University of Leicester for a number of years, where he developed deep learning object detection models for their industrial partners. Now, he is working as a software engineer in the industry, where he continues to research the applications of deep learning in computer vision. Nimesh Patel graduated from the University of Leicester with an MSc in applied computation and numerical modeling. During this time, a project collaboration with one of University of Leicester’s partners was undertaken, dealing with Machine Learning for Hand Gesture recognition. Since then, he has worked in the industry, researching Machine Learning for Computer Vision related tasks, such as Depth Estimation. Leonardo Araujo is just the regular, Brazilian, curious engineer, who has worked in the industry for the past 19 years (yes, in Brazil, people work before graduation), doing HW/SW development and research on the topics of control engineering and computer vision. For the past 6 years, he has focused more on Machine Learning methods. His passions are too many to put on the book.

Table of Contents

Table of Contents

1. Setup and introduction to TensorFlow
2. Deep Learning and Convolutional Neural Networks
3. Image Classification in Tensorflow
4. Object Detection and Segmentation
5. VGG, Inception Modules, Residuals, and MobileNets
6. Autoencoders, Variational Autoencoders, and Generative Adversarial Networks
7. Transfer Learning
8. Machine Learning Best Practices and Troubleshooting
9. Training at Scale