Deep Learning for Computer Vision [Book]PyImageSearch, New York: Jason Brownlee. Linear algebra is a pillar of machine learning. You cannot develop a deep understanding and application of machine learning without it. O'Reilly, Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.
Learn Computer Vision
8 Books for Getting Started With Computer Vision
Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks. This book is targeted at data scientists and Computer Vision practitioners who wish to apply the concepts of Deep Learning to overcome any problem related to Computer Vision. A basic knowledge of programming in Python—and some understanding of machine learning concepts—is required to get the best out of this book. Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more.
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Last Updated on July 5, Computer vision is a subfield of artificial intelligence concerned with understanding the content of digital images, such as photographs and videos. Deep learning has made impressive inroads on challenging computer vision tasks and makes the promise of further advances. Before diving into the application of deep learning techniques to computer vision , it may be helpful to develop a foundation in computer vision more broadly. This may include the foundational and classical techniques, theory, and even basic data handling with standard APIs. Discover how to build models for photo classification, object detection, face recognition, and more in my new computer vision book , with 30 step-by-step tutorials and full source code. Textbooks are those books written by experts, often academics, and are designed to be used as a reference for students and practitioners.
To learn more about this book and how it can help you on your deep learning journey, just keep reading. Are you just getting started in deep learning? Don't worry, you won't get bogged down by tons of theory and complex equations. We'll start off with the basics of machine learning and neural networks. You'll learn in a fun, practical way with lots of code.