Textbook in PDF format

Computer vision is constantly evolving, and this book has been updated to reflect new topics that have emerged in the field since the first edition’s publication. All code used in the book has also been fully updated.
This second edition features new material covering image manipulation practices, image segmentation, feature extraction, and object identification using real-life scenarios to help reinforce each concept. These topics are essential for building advanced computer vision applications, and you’ll gain a thorough understanding of them. The book’s source code has been updated from TensorFlow 1.x to 2.x, and includes step-by-step examples using both OpenCV and TensorFlow with Python.
In this book, my aim is to offer a structured and methodical approach to learning computer visions systems. You will learn essential concepts first and then build on those concepts by working through practical code examples that pertain to real-world computer vision systems. This approach will help you connect the dots as you progress through the chapters. I’ve structured this book to be as hands-on and practical as possible to assist you effectively.
This book also covers the basic concepts of Machine Learning and then gradually moves into more complex concepts like artificial neural networks and Deep Learning. Every concept is reinforced with one or more practical code examples that show how the concept is applied in practice. Machine learning–related concepts are illustrated via code examples in TensorFlow with Python.
Upon completing this book, you’ll have the knowledge and skills to build your own computer vision applications using neural networks
What You Will Learn:
Understand image processing, manipulation techniques, and feature extraction methods
Work with convolutional neural networks (CNN), single-shot detector (SSD), and YOLO
Utilize large scale model development and cloud infrastructure deployment
Gain an overview of FaceNet neural network architecture and develop a facial recognition system
Who This Book Is For:
Those who possess a solid understanding of Python programming and wish to gain an understanding of computer vision and machine learning. It will prove beneficial to data scientists, deep learning experts, and students