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A Guide to Convolutional Neural Networks for Computer Vision

in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result;

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Guide to Convolutional Neural Networks

evaluating neural networks; presents a practical library for implementing ConvNets, explaining how to use a Python interface for the library to;

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Guide to Convolutional Neural Networks

evaluating neural networks; presents a practical library for implementing ConvNets, explaining how to use a Python interface for the library to;

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Convolutional Neural Networks in Visual Computing

theoretical and practical understanding and a complete toolkit of basic information and knowledge required to understand and build convolutional;

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Convolutional Neural Networks in Visual Computing

theoretical and practical understanding and a complete toolkit of basic information and knowledge required to understand and build convolutional;

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Hands-On Convolutional Neural Networks with TensorFlow

DescriptionConvolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through;

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Tree-Based Convolutional Neural Networks

This book proposes a novel neural architecture, tree-based convolutional neural networks (TBCNNs),for processing tree-structured data;

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Biological and Computer Vision

applications. The success of computer vision is founded on a deep understanding of the neural circuits in the brain responsible for visual processing;

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Biological and Computer Vision

applications. The success of computer vision is founded on a deep understanding of the neural circuits in the brain responsible for visual processing;

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Python Deep Learning

neural networks and deep learning processes Investigate and resolve computer vision challenges using convolutional networks and capsule networks;

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Deep Learning in Mining of Visual Content

some particular very popular architectures used in the domain: convolutional neural networks and recurrent neural networks. Deep Learning;

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Artificial Vision and Language Processing for Robotics

development framework for robotics, in detail Learn all about convolutional neural networks, recurrent neural networks, and robotics Create a chatbot;

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Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep;

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Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep;

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Deep Learning from Scratch Building with Python from First Principles

along the way. Author Seth Weidman shows you how neural networks work using a first principles approach. You'll learn how to apply multilayer;

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PyTorch Deep Learning Hands-On

the PyTorch way, with high-level functions, optimizers, and more Convolutional Neural Networks - create advanced computer vision systems;

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Hands-On Deep Learning with TensorFlow

about convolutional neural networks, and logistic regression while training models for deep learning to gain key insights into your data;

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Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference

includes robust algorithmic approaches and applications in a broad spectrum of scientific fields, namely the use of convolutional neural networks;

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Advanced Deep Learning with R

for image dimension reduction, image de-noising and image correction and transfer learning to prepare, define, train, and model a deep neural;

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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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Practical Convolutional Neural Networks

video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models. This book starts with;

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Artificial Intelligence in Medical Sciences and Psychology

ways to use neural networks to classify patients with diseases. You will know how to apply computer vision techniques and convolutional neural;

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Neural Network Programming with Java

implement these in Java. Recurrent neural networks, which are believed to have memory, are discussed in detail. These work in such a way that the;

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Advanced Deep Learning with Python

behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular;

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Artificial Neural Networks

Like other machine learning methods - systems that learn from data - neural networks have been used to solve a wide variety of tasks that;

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The Deep Learning Workshop

then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs;

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Trends in Deep Learning Methodologies

. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision;

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