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Joint Training for Neural Machine Translation

This book presents four approaches to jointly training bidirectional neural machine translation (NMT) models. First, in order to improve;

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Machine Translation

of machine translation, including preprocessing, neural machine translation models, hybrid model, evaluation method, and post-editing.;

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Machine Translation

of machine translation, including preprocessing, neural machine translation models, hybrid model, evaluation method, and post-editing.;

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The Human Factor in Machine Translation

Machine translation has become increasingly popular, especially with the introduction of neural machine translation in major online;

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The Human Factor in Machine Translation

Machine translation has become increasingly popular, especially with the introduction of neural machine translation in major online;

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Neural Machine Translation

Deep learning is revolutionizing how machine translation systems are built today. This book introduces the challenge of machine translation;

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Neural Network Methods in Natural Language Processing

Neural networks are a family of powerful machine learning models and this book focuses on their application to natural language data;

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Neural Network Methods in Natural Language Processing

Neural networks are a family of powerful machine learning models and this book focuses on their application to natural language data;

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Deep Natural Language Processing and AI Applications for Industry 5.0

intelligence is applicable either by simplifying the communication process or by refining and analyzing information. Neural machine translation has;

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TensorFlow For Dummies

translation, robotics, handwriting analysis, financial prediction, and image recognition. TensorFlow is Google's preeminent toolset for machine;

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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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Neural Computing for Advanced Applications

This book presents refereed proceedings of the First International Conference on Neural Computing for Advanced Applications, NCAA 2020;

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Practical TensorFlow.js

applications.You'll work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks;

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Practical TensorFlow.js

'll work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks;

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Introduction to Machine Learning

A substantially revised fourth edition of a comprehensive textbook, including new coverage of recent advances in deep learning and neural;

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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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Evolutionary Machine Learning Techniques

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

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Hands-on Machine Learning with Python

third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks;

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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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Hands-on Machine Learning with Python

third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks;

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Neural Networks with Keras Cookbook

Implement neural network architectures by building them from scratch for multiple real-world applications. Key Features From scratch, build;

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Deep Learning with Theano

Develop deep neural networks in Theano with practical code examples for image classification, machine translation, reinforcement agents, or;

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Example-Based Machine Translation

discussed about Example-Based approach of Machine Translation for English-Bengali language pair. The author wrote this book from his industry level;

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Thinking Machines

computing and neural networks. This is the first book that focuses on machine learning accelerators and hardware development for machine learning;

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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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Artificial Neural Networks and Machine Learning ICANN 2020

The proceedings set LNCS 12396 and 12397 constitute the proceedings of the 29th International Conference on Artificial Neural Networks;

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Artificial Neural Networks and Machine Learning ICANN 2020

The proceedings set LNCS 12396 and 12397 constitute the proceedings of the 29th International Conference on Artificial Neural Networks;

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