Deep Learning for Computer Vision

Shanmugamani, Rajalingappaa

ISBN 10: 1788295625 / ISBN 13: 9781788295628

Edité par Packt Publishing, 2018

A propos de cet article

Unread book in perfect condition.

N° de réf. du vendeur 30709914

Détails bibliographiques

• Titre: Deep Learning for Computer Vision
• Éditeur: Packt Publishing
• Date de parution: 2018
• Langue: anglais
• ISBN à 10 chiffres: 1788295625
• ISBN à 13 chiffres: 9781788295628
• Reliure: Couverture souple
• État de l'article: As New

A propos de ce titre

Synopsis

Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks

Key Features

• Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision
• Combine the power of Python, Keras, and Tensorflow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more
• Includes tips on optimizing and improving the performance of your models under various constraints

Book Description

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. You will also explore their applications using popular Python libraries such as Tensorflow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation.

What you will learn

• Set up an environment for deep learning with Python, Tensorflow, and Keras
• Define and train a model for image and video classification
• Use features from a pre-trained Convolutional Neural Network model for image retrieval
• Understand and implement object detection using the real-world Pedestrian Detection scenario
• Learn about various problems in image captioning and how to overcome them by training images and text together
• Implement similarity matching and train a model for face recognition
• Understand the concept of generative models and use them for image generation
• Deploy your deep learning models and optimize them for high performance

À propos de l?auteur

Rajalingappaa Shanmugamani is currently working as an Engineering Manager for a Deep learning team at Kairos. Previously, he worked as a Senior Machine Learning Developer at SAP, Singapore and worked at various startups in developing machine learning products. He has a Masters from Indian Institute of Technology-Madras. He has published articles in peer-reviewed journals and conferences and submitted applications for several patents in the area of machine learning. In his spare time, he coaches programming and machine learning to school students and engineers.