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Automated Deep Learning Using Neural Network Intelligence

they are based on: Evolution search, Annealing search, and the Bayesian Optimization approach. The Neural Architecture Search is covered and;

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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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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 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 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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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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The Neural Crest and Neural Crest Cells in Vertebrate Development and Evolution

A presentation of all aspects of neural crest cell origins (embryological and evolutionary) development and evolution; neural crest cell;

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The Neural Crest and Neural Crest Cells in Vertebrate Development and Evolution

A presentation of all aspects of neural crest cell origins (embryological and evolutionary) development and evolution; neural crest cell;

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Evolutionary Approach to Machine Learning and Deep Neural Networks

integration of several machine learning and deep learning techniques. These include convolutional neural networks, Groebner bases, relevance vector;

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Evolutionary Approach to Machine Learning and Deep Neural Networks

integration of several machine learning and deep learning techniques. These include convolutional neural networks, Groebner bases, relevance vector;

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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 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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Neural Crest Cells

Neural Crest Cells: Evolution, Development and Disease summarizes discoveries of historical significance and provides in-depth, current;

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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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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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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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Deep Learning on Edge Computing Devices

learning, including neural networks. The title helps researchers maximize the performance of Edge-deep learning models for mobile computing and;

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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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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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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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Java Deep Learning Cookbook

Deeplearning4j to implement deep learning models from scratch Explore recipes for developing, training, and fine-tuning your neural network models;

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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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An Introduction to Neural Information Retrieval

Neural models have been employed in many Information Retrieval scenarios, including ad-hoc retrieval, recommender systems, multi-media;

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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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