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Deep Neural Networks and Applications makes the readers aware about the various artificial neural networks (ANN) and the topologies related;
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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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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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Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural;
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AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks;
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AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks;
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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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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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Learn how to train popular deep learning architectures such as autoencoders, convolutional and recurrent neural networks while discovering;
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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: A Practical Guide is an intensive step-by-step guide for neuroscientists to;
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about convolutional neural networks, and logistic regression while training models for deep learning to gain key insights into your data;
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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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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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, 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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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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Apply modern deep learning techniques to build and train deep neural networks using Gorgonia Key Features Gain a practical understanding;
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Uncover the power of artificial neural networks by implementing them through R code. About This Book * Develop a strong background;
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The seven-volume set of LNCS 11301-11307, constitutes the proceedings of the 25th International Conference on Neural Information Processing;
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neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur;
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neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur;
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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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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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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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This book constitutes the refereed proceedings of the 19th International Conference on Engineering Applications of Neural Networks, EANN;
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the challenges of implementing distributed deep learning with Hadoop and how to overcome it * Implement Convolutional Neural Network (CNN;
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machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic;
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