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Domain Adaptation in Computer Vision with Deep Learning

This book provides a survey of deep learning approaches to domain adaptation in computer vision. It gives the reader an overview of the;

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Deep Learning in Mining of Visual Content

in Digital form is constantly growing in volume. In such active domains as Computer Vision and Robotics visual information understanding is based on;

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Learn Computer Vision Using Opencv: With Deep Learning Cnns and Rnns

Build practical applications of computer vision using the OpenCV library with Python. This book discusses different facets of computer;

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Deep Learning for Vision Systems

, and much, much more. Amazing new computer vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL;

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Machine Learning for Speaker Recognition

and domain adaptation. This useful toolkit enables readers to apply machine learning techniques to address practical issues, such as;

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Synthetic Data for Deep Learning

to train computer vision models. There are many more facets of synthetic data to consider. In the section on basic computer vision, the book;

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Synthetic Data for Deep Learning

to train computer vision models. There are many more facets of synthetic data to consider. In the section on basic computer vision, the book;

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Hands-On Computer Vision with TensorFlow 2

. Hands-On Computer Vision with TensorFlow 2 starts with the fundamentals of computer vision and deep learning, teaching you how to build a neural;

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Mastering OpenCV 4 with Python

Create computer vision and deep learning web applications Who this book is forThis book is designed for computer vision developers, engineers;

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Deep Learning for Computer Vision

kinds of deep learning model from scratch to solve specific problems in Computer Vision Combine the power of Python, Keras, and TensorFlow to;

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Deep Learning in Computer Vision

Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions;

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Computer Vision Metrics

-of-the-art survey for fundamental computer vision methods and deep learning. With over 800 essential references, as well as chapter-by-chapter;

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Practical Computer Vision Applications Using Deep Learning with CNNs

Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the;

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Domain Adaptation for Visual Understanding

This unique volume reviews the latest advances in domain adaptation in the training of machine learning algorithms for visual understanding;

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Domain Adaptation for Visual Understanding

This unique volume reviews the latest advances in domain adaptation in the training of machine learning algorithms for visual understanding;

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Intelligent Learning for Computer Vision

industry. It covers selected papers in the area of computer vision. This book covers new tools and technologies in some of the important areas;

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Synthetic Data for Deep Learning

and development of synthetic data before delving into the role it plays in machine learning and computer vision. Yoüll gain insight into how;

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Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

This book constitutes the refereed proceedings of the First MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019;

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Deep Learning in Computer Vision: Principles and Applications

Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions;

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Deep Learning for Coders with fastai &

in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most;

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Deep Learning with Applications Using Python

Explore deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and;

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Deep Learning through Sparse and Low-Rank Modeling

variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for;

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Advanced Deep Learning with Python

) to computer vision problems, NLP, and GANs Book DescriptionIn order to build robust deep learning systems, you'll need to understand;

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TensorFlow 2.0 Computer Vision Cookbook

vision tasks using TensorFlow. The book begins by taking you through the basics of deep learning for computer vision, along with covering;

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Computer Vision Projects with PyTorch

in computer vision with PyTorch. Implement transfer learning and perform image classification, object detection, image segmentation, and other;

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The Deep Learning Workshop

Features Understand how to implement deep learning with TensorFlow and Keras Learn the fundamentals of computer vision and image recognition;

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Computer Vision Systems

in topical sections on biological and cognitive vision; hardware-implemented and real-time vision systems; high-level vision; learning and adaptation;

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