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Deep Learning for Computer Vision

Deep Learning for Computer Vision
  • Author : Rajalingappaa Shanmugamani
  • Publsiher : Packt Publishing Ltd
  • Release : 23 January 2018
  • ISBN : 1788293355
  • Pages : 310 pages
  • Rating : 4/5 from 21 ratings
GET THIS BOOKDeep Learning for Computer Vision

Summary:
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 Who this book is for 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 for Computer Vision

Deep Learning for Computer Vision
  • Author : Rajalingappaa Shanmugamani
  • Publisher : Packt Publishing Ltd
  • Release : 23 January 2018
GET THIS BOOKDeep Learning for Computer Vision

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


Machine Learning for OpenCV

Machine Learning for OpenCV
  • Author : Michael Beyeler
  • Publisher : Packt Publishing Ltd
  • Release : 14 July 2017
GET THIS BOOKMachine Learning for OpenCV

Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide. About This Book Load, store, edit, and visualize data using OpenCV and Python Grasp the fundamental concepts of classification, regression, and clustering Understand, perform, and experiment with machine learning techniques using this easy-to-follow guide Evaluate, compare, and choose the right algorithm for any task Who This Book Is For This book targets Python programmers who are already familiar with OpenCV; this book will give


Modern Deep Learning and Advanced Computer Vision

Modern Deep Learning and Advanced Computer Vision
  • Author : J. Nedumaan,Prof Thomas Binford,J. Lepika,J. Tisa,J. Ruby,P. S. Jagadeesh Kumar
  • Publisher : Anonim
  • Release : 08 December 2019
GET THIS BOOKModern Deep Learning and Advanced Computer Vision

Computer vision has enormous progress in modern times. Deep learning has driven and inferred a range of computer vision problems, such as object detection and recognition, face detection and recognition, motion tracking and estimation, transfer learning, action recognition, image segmentation, semantic segmentation, robotic vision. The chapters in this book are persuaded towards the applications of advanced computer vision using modern deep learning techniques. The authors trust in making the readers with more interesting illustrations in understanding the concepts of deep


Deep Learning in Computer Vision

Deep Learning in Computer Vision
  • Author : Mahmoud Hassaballah,Ali Ismail Awad
  • Publisher : CRC Press
  • Release : 23 March 2020
GET THIS BOOKDeep Learning in Computer Vision

Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed. This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community. The book covers a broad scope of topics in deep learning concepts and applications such


Mastering Computer Vision with TensorFlow 2.x

Mastering Computer Vision with TensorFlow 2.x
  • Author : Krishnendu Kar
  • Publisher : Packt Publishing Ltd
  • Release : 15 May 2020
GET THIS BOOKMastering Computer Vision with TensorFlow 2.x

Apply neural network architectures to build state-of-the-art computer vision applications using the Python programming language Key Features 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 Book Description Computer vision allows machines to gain human-level understanding to visualize, process, and analyze images and videos. This book



Advanced Deep Learning with Python

Advanced Deep Learning with Python
  • Author : Ivan Vasilev
  • Publisher : Packt Publishing Ltd
  • Release : 12 December 2019
GET THIS BOOKAdvanced Deep Learning with Python

Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem Key Features Get to grips with building faster and more robust deep learning architectures Investigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorch Apply deep neural networks (DNNs) to computer vision problems, NLP, and GANs Book Description In order to build robust deep learning systems, you’ll need to


Deep Learning for Vision Systems

Deep Learning for Vision Systems
  • Author : Mohamed Elgendy
  • Publisher : Manning Publications
  • Release : 10 November 2020
GET THIS BOOKDeep Learning for Vision Systems

How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition. Summary Computer vision is central to many leading-edge innovations, including self-driving cars, drones, augmented reality, facial recognition, and much, much more. Amazing new computer


Advanced Deep Learning with Keras

Advanced Deep Learning with Keras
  • Author : Rowel Atienza
  • Publisher : Packt Publishing Ltd
  • Release : 31 October 2018
GET THIS BOOKAdvanced Deep Learning with Keras

A comprehensive guide to advanced deep learning techniques, including Autoencoders, GANs, VAEs, and Deep Reinforcement Learning, that drive today's most impressive AI results Key Features Explore the most advanced deep learning techniques that drive modern AI results Implement Deep Neural Networks, Autoencoders, GANs, VAEs, and Deep Reinforcement Learning A wide study of GANs, including Improved GANs, Cross-Domain GANs and Disentangled Representation GANs Book Description Recent developments in deep learning, including GANs, Variational Autoencoders, and Deep Reinforcement Learning, are creating impressive


Applied Deep Learning and Computer Vision for Self-Driving Cars

Applied Deep Learning and Computer Vision for Self-Driving Cars
  • Author : Sumit Ranjan,Dr. S. Senthamilarasu
  • Publisher : Packt Publishing Ltd
  • Release : 14 August 2020
GET THIS BOOKApplied Deep Learning and Computer Vision for Self-Driving Cars

This book teaches you the different techniques and methodologies associated while implementing deep learning solutions in self-driving cars. You will use real-world examples to implement various neural network architectures to develop your own autonomous and automated vehicle using the Python environment.


Advanced Deep Learning with R

Advanced Deep Learning with R
  • Author : Bharatendra Rai
  • Publisher : Packt Publishing Ltd
  • Release : 17 December 2019
GET THIS BOOKAdvanced Deep Learning with R

Discover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R libraries Key Features Implement deep learning algorithms to build AI models with the help of tips and tricks Understand how deep learning models operate using expert techniques Apply reinforcement learning, computer vision, GANs, and NLP using a range of datasets Book Description Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data.


Machine Learning for OpenCV 4

Machine Learning for OpenCV 4
  • Author : Aditya Sharma,Vishwesh Ravi Shrimali,Michael Beyeler
  • Publisher : Packt Publishing Ltd
  • Release : 06 September 2019
GET THIS BOOKMachine Learning for OpenCV 4

A practical guide to understanding the core machine learning and deep learning algorithms, and implementing them to create intelligent image processing systems using OpenCV 4 Key Features Gain insights into machine learning algorithms, and implement them using OpenCV 4 and scikit-learn Get up to speed with Intel OpenVINO and its integration with OpenCV 4 Implement high-performance machine learning models with helpful tips and best practices Book Description OpenCV is an opensource library for building computer vision apps. The latest release, OpenCV 4, offers a


Deep Learning with Python

Deep Learning with Python
  • Author : Francois Chollet
  • Publisher : Manning Publications
  • Release : 28 October 2017
GET THIS BOOKDeep Learning with Python

Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher Fran�ois Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition,


Hands-On Java Deep Learning for Computer Vision

Hands-On Java Deep Learning for Computer Vision
  • Author : Klevis Ramo
  • Publisher : Packt Publishing Ltd
  • Release : 21 February 2019
GET THIS BOOKHands-On Java Deep Learning for Computer Vision

Leverage the power of Java and deep learning to build production-grade Computer Vision applications Key Features Build real-world Computer Vision applications using the power of neural networks Implement image classification, object detection, and face recognition Know best practices on effectively building and deploying deep learning models in Java Book Description Although machine learning is an exciting world to explore, you may feel confused by all of its theoretical aspects. As a Java developer, you will be used to telling the


Advanced Topics in Computer Vision

Advanced Topics in Computer Vision
  • Author : Giovanni Maria Farinella,Sebastiano Battiato,Roberto Cipolla
  • Publisher : Springer Science & Business Media
  • Release : 24 September 2013
GET THIS BOOKAdvanced Topics in Computer Vision

This book presents a broad selection of cutting-edge research, covering both theoretical and practical aspects of reconstruction, registration, and recognition. The text provides an overview of challenging areas and descriptions of novel algorithms. Features: investigates visual features, trajectory features, and stereo matching; reviews the main challenges of semi-supervised object recognition, and a novel method for human action categorization; presents a framework for the visual localization of MAVs, and for the use of moment constraints in convex shape optimization; examines solutions