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Automated Deep Learning Using Neural Network Intelligence

Intermediate-Advanced user level;...

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Automated Deep Learning Using Neural Network Intelligence

develop effective neural networks Construct new deep learning models from scratch Execute the automated Neural Architecture Search to create;

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Applied Deep Learning and Computer Vision for Self-Driving Cars

. You will use real-world examples to implement various neural network architectures to develop your own autonomous and automated vehicle using;

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Mastering PyTorch

PyTorch 1.x to build advanced neural network models Learn to perform a wide range of tasks by implementing deep learning algorithms and;

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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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Hands-On Neural Networks

Design and create neural networks with deep learning and artificial intelligence principles using OpenAI Gym, TensorFlow, and Keras Key;

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Hardware Accelerator Systems for Artificial Intelligence and Machine Learning

it has seen with the advent of Deep Neural Networks (DNNs) and Machine Learning. Updates in this release include chapters on Hardware;

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Automated Design of Machine Learning and Search Algorithms

directions in this fast-developing area. Methods have been developed to automate the design of neural networks, heuristics and metaheuristics using;

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Neural Networks with R

in neural networks with R, to implement them in your applications * Build smart systems using the power of deep learning * Real-world case studies;

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Advanced Deep Learning with R

networks, deep learning architectures, and the fundamentals for implementing deep learning with R. The book will also take you through using;

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Codeless Deep Learning with KNIME

comprehensive guide to the KNIME GUI and KNIME deep learning integration, helping you build neural network models without writing any code. It'll guide;

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PyTorch Recipes

Learn how to use PyTorch to build neural network models using code snippets updated for this second edition. This book includes new;

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The Deep Learning with PyTorch Workshop

network architecture in deep learning Implement helpful methods to create and train a model using PyTorch syntax Discover how intelligent;

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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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R Deep Learning Essentials

Implement neural network models in R 3.5 using TensorFlow, Keras, and MXNet Key Features Use R 3.5 for building deep learning models for;

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Deep Learning and Practice with MindSpore

(CNNs), recurrent neural networks (RNNs), unsupervised learning, deep reinforcement learning, automated machine learning, device-cloud;

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Deep Learning and Practice with MindSpore

(CNNs), recurrent neural networks (RNNs), unsupervised learning, deep reinforcement learning, automated machine learning, device-cloud;

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Deep Learning for Natural Language Processing

Explore convolutional and recurrent neural networks and long short-term memory networks Book DescriptionApplying deep learning approaches to;

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Emerging Deep Learning Applications of Short Range Radars

of deep learning are explored, outlining the history of neural networks and the optimization algorithms to train them. Modern deep convolutional;

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Artificial Intelligence for Humans, Volume 3

learning and convolution are taking neural networks in bold new directions. In this book, we will demonstrate the neural networks in a variety;

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Deep Learning with Keras

Autoencoders * Evolve a deep neural network using reinforcement learning In Detail This book starts by introducing you to supervised learning;

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Deep Learning with PyTorch Quick Start Guide

train your neural networks with relative ease. What you will learn Set up the deep learning environment using the PyTorch library Learn to build;

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Intelligent Data Engineering and Automated Learning - IDEAL 2016

methodologies, frameworks, and techniques to applications including various topics such as evolutionary algorithms; deep learning; neural networks;

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Advances in Computational Intelligence

systems; random-weights neural networks; pattern recognition; deep learning and natural language processing; software testing and intelligent;

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Advances in Computational Intelligence

systems; random-weights neural networks; pattern recognition; deep learning and natural language processing; software testing and intelligent;

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Neural Networks with Keras Cookbook

book will take you from the basics of neural networks to advanced implementations of architectures using a recipe-based approach. We will learn;

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TensorFlow 2.0 Quick Start Guide

Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks. Key Features Train;

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