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Mastering Computer Vision with TensorFlow 2.x
Mastering Computer Vision with TensorFlow 2.x

Mastering Computer Vision with TensorFlow 2.x: Build advanced computer vision applications using machine learning and deep learning techniques

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Mastering Computer Vision with TensorFlow 2.x

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Key benefits

  • Gain a fundamental understanding of advanced computer vision and neural network models in use today
  • Cover tasks such as low-level vision, image classification, and object detection
  • Develop deep learning models on cloud platforms and optimize them using TensorFlow Lite and the OpenVINO toolkit

Description

Computer vision allows machines to gain human-level understanding to visualize, process, and analyze images and videos. This book focuses on using TensorFlow to help you learn advanced computer vision tasks such as image acquisition, processing, and analysis. You'll start with the key principles of computer vision and deep learning to build a solid foundation, before covering neural network architectures and understanding how they work rather than using them as a black box. Next, you'll explore architectures such as VGG, ResNet, Inception, R-CNN, SSD, YOLO, and MobileNet. As you advance, you'll learn to use visual search methods using transfer learning. You'll also cover advanced computer vision concepts such as semantic segmentation, image inpainting with GAN's, object tracking, video segmentation, and action recognition. Later, the book focuses on how machine learning and deep learning concepts can be used to perform tasks such as edge detection and face recognition. You'll then discover how to develop powerful neural network models on your PC and on various cloud platforms. Finally, you'll learn to perform model optimization methods to deploy models on edge devices for real-time inference. By the end of this book, you'll have a solid understanding of computer vision and be able to confidently develop models to automate tasks.

Who is this book for?

This book is for computer vision professionals, image processing professionals, machine learning engineers and AI developers who have some knowledge of machine learning and deep learning and want to build expert-level computer vision applications. In addition to familiarity with TensorFlow, Python knowledge will be required to get started with this book.

What you will learn

  • Explore methods of feature extraction and image retrieval and visualize different layers of the neural network model
  • Use TensorFlow for various visual search methods for real-world scenarios
  • Build neural networks or adjust parameters to optimize the performance of models
  • Understand TensorFlow DeepLab to perform semantic segmentation on images and DCGAN for image inpainting
  • Evaluate your model and optimize and integrate it into your application to operate at scale
  • Get up to speed with techniques for performing manual and automated image annotation

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : May 15, 2020
Length: 430 pages
Edition : 1st
Language : English
ISBN-13 : 9781838826932
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Google
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Product Details

Publication date : May 15, 2020
Length: 430 pages
Edition : 1st
Language : English
ISBN-13 : 9781838826932
Vendor :
Google
Category :
Languages :
Tools :

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Frequently bought together


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Total NZ$ 233.97
Modern Computer Vision with PyTorch
NZ$96.99
Advanced Deep Learning with TensorFlow 2 and Keras
NZ$64.99
Mastering Computer Vision with TensorFlow 2.x
NZ$71.99
Total NZ$ 233.97 Stars icon

Table of Contents

17 Chapters
Section 1: Introduction to Computer Vision and Neural Networks Chevron down icon Chevron up icon
Computer Vision and TensorFlow Fundamentals Chevron down icon Chevron up icon
Content Recognition Using Local Binary Patterns Chevron down icon Chevron up icon
Facial Detection Using OpenCV and CNN Chevron down icon Chevron up icon
Deep Learning on Images Chevron down icon Chevron up icon
Section 2: Advanced Concepts of Computer Vision with TensorFlow Chevron down icon Chevron up icon
Neural Network Architecture and Models Chevron down icon Chevron up icon
Visual Search Using Transfer Learning Chevron down icon Chevron up icon
Object Detection Using YOLO Chevron down icon Chevron up icon
Semantic Segmentation and Neural Style Transfer Chevron down icon Chevron up icon
Section 3: Advanced Implementation of Computer Vision with TensorFlow Chevron down icon Chevron up icon
Action Recognition Using Multitask Deep Learning Chevron down icon Chevron up icon
Object Detection Using R-CNN, SSD, and R-FCN Chevron down icon Chevron up icon
Section 4: TensorFlow Implementation at the Edge and on the Cloud Chevron down icon Chevron up icon
Deep Learning on Edge Devices with CPU/GPU Optimization Chevron down icon Chevron up icon
Cloud Computing Platform for Computer Vision Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.8
(4 Ratings)
5 star 50%
4 star 25%
3 star 0%
2 star 0%
1 star 25%
Kindle Customer Dec 29, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Good variety of examples to work through. The examples and concepts go beyond the typical introductory book. The code is generic enough to run locally or on AWS SageMaker. The author provides great support when problems were encountered. I'm glad I purchased this book.Would love to see another advanced book that covers TensorFlow model zoo, model/research, object detection API, TensorRT and deploying models to IoT edge devices (e.g. NVIDIA Jetsons)
Amazon Verified review Amazon
maninblack Jul 22, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is a good starting point for computer science professionals who are new to the field of Computer Vision. I personally have not worked with CV a lot, but have the general prerequisite knowledge of machine learning concepts, and found this book to be a good starting point. This book had a good balance between theoretical concepts and working code snippets that can be tried out. The GitHub repo linked in the book was very helpful to replicate the code snippets as well, and I have not come across many books with this kind of functionality. All the code snippets I tried out worked well without any problems. From a practical implementation point of view, the section on deploying the model on a phone was quite interesting and fun to explore. Each concept mentioned in the book was very well illustrated and supported with thorough mathematical explanations as well.On the downside, I felt the book was focused mostly on image applications and could have also included more applications with videos as input in addition to images. I would have also liked a little more intuition when explaining certain concepts, in addition to the mathematical formulations already mentioned in the book. That really helps readers grasp the concept well.Overall, this was a well-written and instructive book for people wanting to gain knowledge in the CV domain!
Amazon Verified review Amazon
MIke R Jul 04, 2020
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
There are many books out there / but this book stands out - very clear explanation of codes and contents, lots of detailed explanations for object detection, classification, visual search, matching and training in cloud. All the codes work and and their screen video has been really helpful. I find deployment of model in phone and Raspberry PI example has many practical usage. +4minus point: some sections like action recognition and semantic segmentation could be expanded more with training a custom model. -1bonus point: the author has been very helpful to connect and answer questions through LinkedIn +1
Amazon Verified review Amazon
c Jun 24, 2022
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Says it teaches image in painting but the linked notebook is just a GAN to generate MNIST digits
Amazon Verified review Amazon
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