English | ISBN: 9390684684 | 208 pages | EPUB | June 25, 2021 | 4.69 Mb
Conceptualizing deep learning in computer vision applications using PyTorch and Python libraries.
● Covers a variety of computer vision projects, including face recognition and object recognition such as Yolo, Faster R-CNN. ● Includes graphical representations and illustrations of neural networks and teaches how to program them. ● Includes deep learning techniques and architectures introduced by Microsoft, Google, and the University of Oxford.
Elements of Deep Learning for Computer Vision gives a thorough understanding of deep learning and provides highly accurate computer vision solutions while using libraries like PyTorch.
This book introduces you to Deep Learning and explains all the concepts required to understand the basic working, development, and tuning of a neural network using Pytorch. The book then addresses the field of computer vision using two libraries, including the Python wrapper/version of OpenCV and PIL. After establishing and understanding both the primary concepts, the book addresses them together by explaining Convolutional Neural Networks(CNNs). CNNs are further elaborated using top industry standards and research to explain how they provide complicated Object Detection in images and videos, while also explaining their evaluation. Towards the end, the book explains how to develop a fully functional object detection model, including its deployment over APIs.
By the end of this book, you are well-equipped with the role of deep learning in the field of computer vision along with a guided process to design deep learning solutions.
What you will learn
● Get to know the mechanism of deep learning and how neural networks operate.
● Learn to develop a highly accurate neural network model.
● Access to rich Python libraries to address computer vision challenges.
● Build deep learning models using PyTorch and learn how to deploy using the API.
● Learn to develop Object Detection and Face Recognition models along with their deployment.