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Hands-On Computer Vision with TensorFlow 2
Hands-On Computer Vision with TensorFlow 2

Hands-On Computer Vision with TensorFlow 2: Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras

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Profile Icon Benjamin Planche Profile Icon Eliot Andres
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€29.99
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.3 (12 Ratings)
Paperback May 2019 372 pages 1st Edition
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Arrow left icon
Profile Icon Benjamin Planche Profile Icon Eliot Andres
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€29.99
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.3 (12 Ratings)
Paperback May 2019 372 pages 1st Edition
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€8.99 €23.99
Paperback
€29.99
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Renews at €18.99p/m
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Hands-On Computer Vision with TensorFlow 2

Section 1: TensorFlow 2 and Deep Learning Applied to Computer Vision

This section covers the fundamentals of computer vision and deep learning, with the help of concrete TensorFlow examples. Starting with a presentation of these technical domains, the first chapter will then walk you through the inner workings of neural networks. This section continues with an introduction to the instrumental features of TensorFlow 2 and Keras, and their key concepts and ecosystems. It ends with a description of machine learning techniques adopted by computer vision experts.

The following chapters will be covered in this section:

  • Chapter 1Computer Vision and Neural Networks
  • Chapter 2TensorFlow Basics and Training a Model
  • Chapter 3Modern Neural Networks
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Key benefits

  • Discover how to build, train, and serve your own deep neural networks with TensorFlow 2 and Keras
  • Apply modern solutions to a wide range of applications such as object detection and video analysis
  • Learn how to run your models on mobile devices and web pages and improve their performance

Description

Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. This book will help you explore TensorFlow 2, the brand new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. Hands-On Computer Vision with TensorFlow 2 starts with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the book demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts. By the end of the book, you will have both the theoretical understanding and practical skills to solve advanced computer vision problems with TensorFlow 2.0.

Who is this book for?

If you’re new to deep learning and have some background in Python programming and image processing, like reading/writing image files and editing pixels, this book is for you. Even if you’re an expert curious about the new TensorFlow 2 features, you’ll find this book useful. While some theoretical concepts require knowledge of algebra and calculus, the book covers concrete examples focused on practical applications such as visual recognition for self-driving cars and smartphone apps.

What you will learn

  • Create your own neural networks from scratch
  • Classify images with modern architectures including Inception and ResNet
  • Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net
  • Tackle problems faced when developing self-driving cars and facial emotion recognition systems
  • Boost your application's performance with transfer learning, GANs, and domain adaptation
  • Use recurrent neural networks (RNNs) for video analysis
  • Optimize and deploy your networks on mobile devices and in the browser
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Publication date, Length, Edition, Language, ISBN-13
Publication date : May 30, 2019
Length: 372 pages
Edition : 1st
Language : English
ISBN-13 : 9781788830645
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Table of Contents

15 Chapters
Section 1: TensorFlow 2 and Deep Learning Applied to Computer Vision Chevron down icon Chevron up icon
Computer Vision and Neural Networks Chevron down icon Chevron up icon
TensorFlow Basics and Training a Model Chevron down icon Chevron up icon
Modern Neural Networks Chevron down icon Chevron up icon
Section 2: State-of-the-Art Solutions for Classic Recognition Problems Chevron down icon Chevron up icon
Influential Classification Tools Chevron down icon Chevron up icon
Influential Classification Tools
Technical requirements
Understanding advanced CNN architectures
VGG – a standard CNN architecture
Overview of the VGG architecture
Motivation
Architecture
Contributions – standardizing CNN architectures
Replacing large convolutions with multiple smaller ones
Increasing the depth of the feature maps
Augmenting data with scale jittering
Replacing fully connected layers with convolutions
Implementations in TensorFlow and Keras
The TensorFlow model
The Keras model
GoogLeNet and the inception module
Overview of the GoogLeNet architecture
Motivation
Architecture
Contributions – popularizing larger blocks and bottlenecks
Capturing various details with inception modules
Using 1 x 1 convolutions as bottlenecks
Pooling instead of fully connecting
Fighting vanishing gradient with intermediary losses
Implementations in TensorFlow and Keras
Inception module with the Keras Functional API
TensorFlow model and TensorFlow Hub
The Keras model
ResNet – the residual network
Overview of the ResNet architecture
Motivation
Architecture
Contributions – forwarding the information more deeply
Estimating a residual function instead of a mapping
Going ultra-deep
Implementations in TensorFlow and Keras
Residual blocks with the Keras Functional API
The TensorFlow model and TensorFlow Hub
The Keras model
Leveraging transfer learning
Overview
Definition
Human inspiration
Motivation
Transferring CNN knowledge
Use cases
Similar tasks with limited training data
Similar tasks with abundant training data
Dissimilar tasks with abundant training data
Dissimilar tasks with limited training data
Transfer learning with TensorFlow and Keras
Model surgery
Removing layers
Grafting layers
Selective training
Restoring pretrained parameters
Freezing layers
Summary
Questions
Further reading
Object Detection Models Chevron down icon Chevron up icon
Enhancing and Segmenting Images Chevron down icon Chevron up icon
Section 3: Advanced Concepts and New Frontiers of Computer Vision Chevron down icon Chevron up icon
Training on Complex and Scarce Datasets Chevron down icon Chevron up icon
Training on Complex and Scarce Datasets
Technical requirements
Efficient data serving
Introducing the TensorFlow Data API
Intuition behind the TensorFlow Data API
Feeding fast and data-hungry models
Inspiration from lazy structures
Structure of TensorFlow data pipelines
Extract, Transform, Load
API interface
Setting up input pipelines
Extracting (from tensors, text files, TFRecord files, and more)
From NumPy and TensorFlow data
From files
From other inputs (generator, SQL database, range, and others)
Transforming the samples (parsing, augmenting, and more)
Parsing images and labels
Parsing TFRecord files
Editing samples
Transforming the datasets (shuffling, zipping, parallelizing, and more)
Structuring datasets
Merging datasets
Loading
Optimizing and monitoring input pipelines
Following best practices for optimization
Parallelizing and prefetching
Fusing operations
Passing options to ensure global properties
Monitoring and reusing datasets
Aggregating performance statistics
Caching and reusing datasets
How to deal with data scarcity
Augmenting datasets
Overview
Why augment datasets?
Considerations
Augmenting images with TensorFlow
TensorFlow Image module
Example – augmenting images for our autonomous driving application
Rendering synthetic datasets
Overview
Rise of 3D databases
Benefits of synthetic data
Generating synthetic images from 3D models
Rendering from 3D models
Post-processing synthetic images
Problem – realism gap
Leveraging domain adaptation and generative models (VAEs and GANs)
Training models to be robust to domain changes
Supervised domain adaptation
Unsupervised domain adaptation
Domain randomization
Generating larger or more realistic datasets with VAEs and GANs
Discriminative versus generative models
VAEs
GANs
Augmenting datasets with conditional GANs
Summary
Questions
Further reading
Video and Recurrent Neural Networks Chevron down icon Chevron up icon
Optimizing Models and Deploying on Mobile Devices Chevron down icon Chevron up icon
Migrating from TensorFlow 1 to TensorFlow 2 Chevron down icon Chevron up icon
Assessments Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.3
(12 Ratings)
5 star 33.3%
4 star 25%
3 star 8.3%
2 star 8.3%
1 star 25%
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AlfredO Nov 12, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Easy to understand.
Amazon Verified review Amazon
Cliente de Amazon Dec 06, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Being quite new in the field of Computer Vision, I have found this book to be a very good reference especially when it comes to translate the concepts of Deep Learning into actual code. It has certainly helped me to reduce the time I'd spend googling for examples and such. Plus, the explanations provided are very clear and I'd definitely recommend this book if you looking for a starting point to get into Computer Vision.
Amazon Verified review Amazon
Sergey Apr 07, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great read for both beginners and experienced enthusiasts! Authors provide an easy to follow introduction to deep learning and its mathematical foundations as well as the code and exercises to enhance your understanding. I, personally, used the book to get to know TF 2 (already having some experience with other frameworks) and it served me very well. Highly recommended!
Amazon Verified review Amazon
samuel Aug 01, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book provides a clear mathematical background for understanding neural networks. The theoretical explanations are illustrated by applications inspired from historical image processing riddles. This makes it quite interesting to follow the book as it is correlated to real-life problems and doesn't take shortcuts by oversimplifying things.I had no prior experience with Python, so it was quite challenging for me to get started. But even so I went trough the first chapter without any major issue.It worked best for me to juggle back and forth between the book (to get the theoretical understanding) and the Jupiter Notebook/online code (to apply the concepts and follow the program examples)
Amazon Verified review Amazon
Niveditha Kalavakonda Jan 13, 2020
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
The book is a light reference for those starting out in computer vision. The authors provide an overview of different problems and share code snippets for deep learning-based solutions. It is well written on the whole and has enough detail for software engineers and machine learning engineers to get an initial prototype up and running for their problems. Considering this is a book, the authors could have shared some additional insight into why certain types of convolutional blocks improved performance over others and why they work for specific problems.
Amazon Verified review Amazon
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