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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

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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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 Learning with Hadoop

learning concepts and set up Hadoop to put them to use * Implement and parallelize deep learning models on Hadoop's YARN framework * A;

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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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Deep Learning Architectures

This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both;

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Deep Learning with Python

and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems;

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Deep Learning with Python

how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world;

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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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Reinforcement Learning for Finance

This book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow;

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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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Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide

will be a quick introduction to using Cognitive Toolkit and will teach you how to train and validate different types of neural networks, such;

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Deep Learning in Computational Mechanics

examples are investigated, such as approximating functions by neural networks or the simulation of the temperature's evolution in a one-dimensional;

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Deep Learning in Computational Mechanics

examples are investigated, such as approximating functions by neural networks or the simulation of the temperature's evolution in a one-dimensional;

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Visual Cortex and Deep Networks

A mathematical framework that describes learning of invariant representations in the ventral stream, offering both theoretical development;

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Deep Learning for Beginners

. The book begins with a basic overview of machine learning, guiding you through setting up popular Python frameworks. You will also understand;

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Deep Learning in Mining of Visual Content

contains chapters which introduce theoretical and mathematical foundations of neural networks and related optimization methods. Then it discusses;

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Python Deep Learning

-performance algorithms and popular Python frameworks. You'll uncover different neural network architectures, such as convolutional networks, recurrent;

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

databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous;

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

databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous;

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Learn Keras for Deep Neural Networks

Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses;

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Mathematical Perspectives on Neural Networks

Recent years have seen an explosion of new mathematical results on learning and processing in neural networks. This body of results rests;

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Mathematical Perspectives on Neural Networks

Recent years have seen an explosion of new mathematical results on learning and processing in neural networks. This body of results rests;

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PyTorch Deep Learning Hands-On

learning models from prototyping to production Book DescriptionPyTorch Deep Learning Hands-On is a book for engineers who want a fast-paced guide;

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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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PyTorch Recipes

PyTorch. This is followed by a deep dive on building models with convolutional neural networks, deep neural networks, and recurrent neural;

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