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Deep Neural Networks and Applications

Deep Neural Networks and Applications makes the readers aware about the various artificial neural networks (ANN) and the topologies related;

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Deep Neural Networks in a Mathematical Framework

This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools;

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Deep Learning from Scratch Building with Python from First Principles

With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many;

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Advanced Deep Learning with Python

Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural;

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Deep Learning and Practice with MindSpore

AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks;

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Deep Learning and Practice with MindSpore

AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks;

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Deep Learning and Artificial Intelligence

Welcome to this book on Deep Learning and Neural Networks. We're going to be diving into what neural networks are, what the current neural;

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Deep Learning and Artificial Intelligence

Welcome to this book on Deep Learning and Neural Networks. We're going to be diving into what neural networks are, what the current neural;

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Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide

Learn how to train popular deep learning architectures such as autoencoders, convolutional and recurrent neural networks while discovering;

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Advanced Deep Learning with R

learning book starts by covering the essential deep learning techniques and concepts for prediction and classification. You will learn about neural;

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Introduction to Deep Learning and Neural Networks with PythonT

Introduction to Deep Learning and Neural Networks with PythonT: A Practical Guide is an intensive step-by-step guide for neuroscientists to;

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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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Computational Mechanics with Neural Networks

This book shows how neural networks are applied to computational mechanics. Part I presents the fundamentals of neural networks and other;

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Computational Mechanics with Neural Networks

This book shows how neural networks are applied to computational mechanics. Part I presents the fundamentals of neural networks and other;

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MATLAB Deep Learning

, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB;

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Fundamentals Of Deep Learning

With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that's paving;

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Hands-On Deep Learning with Go

Apply modern deep learning techniques to build and train deep neural networks using Gorgonia Key Features Gain a practical understanding;

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Neural Networks with R

Uncover the power of artificial neural networks by implementing them through R code. About This Book * Develop a strong background;

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Neural Information Processing

The seven-volume set of LNCS 11301-11307, constitutes the proceedings of the 25th International Conference on Neural Information Processing;

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Convolutional Neural Networks in Visual Computing

neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur;

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Convolutional Neural Networks in Visual Computing

neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur;

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Deep Learning with R for Beginners

Explore the world of neural networks by building powerful deep learning models using the R ecosystem Key Features Get to grips with the;

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Deep Learning with Keras

you are a data scientist with experience in machine learning or an AI programmer with some exposure to neural networks, you will find this;

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Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools

The research presented in this book shows how combining deep neural networks with a special class of fuzzy logical rules and multi-criteria;

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Engineering Applications of Neural Networks

This book constitutes the refereed proceedings of the 19th International Conference on Engineering Applications of Neural Networks, EANN;

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Deep Learning with Hadoop

the challenges of implementing distributed deep learning with Hadoop and how to overcome it * Implement Convolutional Neural Network (CNN;

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Evolutionary Machine Learning Techniques

machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic;

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