Hands-On Convolutional Neural Networks with TensorFlow
Solve computer vision problems with modeling in TensorFlow and Python
Published on 28. August 2018
Book
Paperback/Softback
272 pages
978-1-78913-033-1 (ISBN)
Description
Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges.
Key Features
Learn the fundamentals of Convolutional Neural Networks
Harness Python and Tensorflow to train CNNs
Build scalable deep learning models that can process millions of items
Book DescriptionConvolutional 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. What you will learn
Train machine learning models with TensorFlow
Create systems that can evolve and scale during their life cycle
Use CNNs in image recognition and classification
Use TensorFlow for building deep learning models
Train popular deep learning models
Fine-tune a neural network to improve the quality of results with transfer learning
Build TensorFlow models that can scale to large datasets and systems
Who this book is forThis book is for Software Engineers, Data Scientists, or Machine Learning practitioners who want to use CNNs for solving real-world problems. Knowledge of basic machine learning concepts, linear algebra and Python will help.
Key Features
Learn the fundamentals of Convolutional Neural Networks
Harness Python and Tensorflow to train CNNs
Build scalable deep learning models that can process millions of items
Book DescriptionConvolutional 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. What you will learn
Train machine learning models with TensorFlow
Create systems that can evolve and scale during their life cycle
Use CNNs in image recognition and classification
Use TensorFlow for building deep learning models
Train popular deep learning models
Fine-tune a neural network to improve the quality of results with transfer learning
Build TensorFlow models that can scale to large datasets and systems
Who this book is forThis book is for Software Engineers, Data Scientists, or Machine Learning practitioners who want to use CNNs for solving real-world problems. Knowledge of basic machine learning concepts, linear algebra and Python will help.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 15 mm
Weight
514 gr
ISBN-13
978-1-78913-033-1 (9781789130331)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Iffat Zafar | Giounona Tzanidou | Richard Burton
Hands-On Convolutional Neural Networks with TensorFlow
Solve computer vision problems with modeling in TensorFlow and Python
E-Book
09/2024
Packt Publishing
€27.99
Available for download
Persons
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 Leicesters 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.
Content
Table of Contents
Setup and introduction to TensorFlow
Deep Learning and Convolutional Neural Networks
Image Classification in Tensorflow
Object Detection and Segmentation
VGG, Inception Modules, Residuals, and MobileNets
Autoencoders, Variational Autoencoders, and Generative Adversarial Networks
Transfer Learning
Machine Learning Best Practices and Troubleshooting
Training at Scale
Setup and introduction to TensorFlow
Deep Learning and Convolutional Neural Networks
Image Classification in Tensorflow
Object Detection and Segmentation
VGG, Inception Modules, Residuals, and MobileNets
Autoencoders, Variational Autoencoders, and Generative Adversarial Networks
Transfer Learning
Machine Learning Best Practices and Troubleshooting
Training at Scale