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PyTorch Deep Learning Hands-On

. Chapters start with a refresher on how the model works, before sharing the code you need to implement them in PyTorch. This book is ideal if you;

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Natural Language Processing mit PyTorch

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Hands-On Natural Language Processing with PyTorch 1.x

Learn modern approaches to NLP and explore state-of-the-art NLP models using PyTorch Improve your NLP applications with innovative neural;

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PyTorch Recipes

differentiation, and get the best solutions for them. Moving on to algorithms; you will learn how PyTorch works with supervised and unsupervised;

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Natural Language Processing with PyTorchlow

how to apply these methods using PyTorch, a Python-based deep learning library. Authors Delip Rao and Brian McMahon provide you with a;

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Deep Learning with PyTorch Quick Start Guide

those wanting to explore deep learning with PyTorch to harness its power. This book will introduce you to the PyTorch deep learning library and;

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An Introduction to Language Processing with Perl and Prolog

This book teaches the principles of natural language processing and covers linguistics issues. It also details the language-processing;

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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 Learning for Coders with fastai &

Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll;

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Deep Learning with Python

Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory;

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Deep Learning with Python

Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory;

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Natural Language Processing

in natural language processing, mathematical algorithms in natural language processing, and natural language in mobile systems. Section 1 focuses on;

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Advances in Natural Language Processing

This book constitutes the refereed proceedings of the 5th International Conference on Natural Language Processing, FinTAL 2006, held;

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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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Hands-on Machine Learning with Python

. The final chapter contains a detailed end-to-end solution with neural networks in Pytorch. After completing Hands-on Machine Learning with;

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Natural Language Processing with Python Quick Start Guide

Build and deploy intelligent applications for natural language processing with Python by using industry standard tools and recently popular;

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Hands-on Machine Learning with Python

. The final chapter contains a detailed end-to-end solution with neural networks in Pytorch. After completing Hands-on Machine Learning with;

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Natural Language Processing in Action

-to-follow * Clear examples * Hands-on-guide Audience A basic understanding of machine learning and some experience with;

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Speech and Language Processing

scientific evaluation. An accompanying Website contains teaching materials for instructors, with pointers to language processing resources on the Web;

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Computational Processing of the Portuguese Language

selected from 70 submissions. They are grouped in topical sections on speech processing; resources and evaluation; natural language processing;

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Natural Language Processing and Computational Linguistics

Hands-on text analysis with Python, featuring natural language processing and computational linguistics algorithms Learn deep learning;

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Stevens:Deep Learning with PyTorc_p

framework with a strong focus on deep neural networks. Because it emphasizes GPU-based acceleration, PyTorch performs exceptionally well on readily;

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Applied Natural Language Processing in the Enterprise

models, many teams still struggle with building NLP applications that live up to the hype. This hands-on guide helps you get up to speed on the;

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Machine Learning for Text

intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning;

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Graph-based Natural Language Processing and Information Retrieval

research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval;

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Turkish Natural Language Processing

This book brings together work on Turkish natural language and speech processing over the last 25 years, covering numerous fundamental;

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Natural Language Processing Recipes

Implement natural language processing applications with Python using a problem-solution approach. This book has numerous coding exercises;

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