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Hands-On Natural Language Processing with PyTorch 1.x: Build smart, AI-driven linguistic applications using deep learning and NLP techniques
Become a proficient NLP data scientist by developing deep learning models for NLP and extract valuable insights from structured and unstructured data
Key FeaturesGet to grips with word embeddings, semantics, labeling, and high-level word representations using practical examplesLearn modern approaches to NLP and explore state-of-the-art NLP models using PyTorchImprove your NLP applications with innovative neural networks such as RNNs, LSTMs, and CNNsBook DescriptionIn the internet age, where an increasing volume of text data is generated daily from social media and other platforms, being able to make sense of that data is a crucial skill. With this book, you’ll learn how to extract valuable insights from text by building deep learning models for natural language processing (NLP) tasks.
Starting by understanding how to install PyTorch and using CUDA to accelerate the processing speed, you’ll explore how the NLP architecture works with the help of practical examples. This PyTorch NLP book will guide you through core concepts such as word embeddings, CBOW, and tokenization in PyTorch. You’ll then learn techniques for processing textual data and see how deep learning can be used for NLP tasks. The book demonstrates how to implement deep learning and neural network architectures to build models that will allow you to classify and translate text and perform sentiment analysis. Finally, you’ll learn how to build advanced NLP models, such as conversational chatbots.
By the end of this book, you’ll not only have understood the different NLP problems that can be solved using deep learning with PyTorch, but also be able to build models to solve them.
What you will learnUse NLP techniques for understanding, processing, and generating textUnderstand PyTorch, its applications and how it can be used to build deep linguistic modelsExplore the wide variety of deep learning architectures for NLPDevelop the skills you need to process and represent both structured and unstructured NLP dataBecome well-versed with state-of-the-art technologies and exciting new developments in the NLP domainCreate chatbots using attention-based neural networksWho this book is forThis PyTorch book is for NLP developers, machine learning and deep learning developers, and anyone interested in building intelligent language applications using both traditional NLP approaches and deep learning architectures. If you’re looking to adopt modern NLP techniques and models for your development projects, this book is for you. Working knowledge of Python programming, along with basic working knowledge of NLP tasks, is required.
Table of ContentsFundamentals of Machine Learning and Deep LearningGetting Started with PyTorch 1.x for NLPNLP and Text EmbeddingsText Preprocessing, Stemming, and LemmatizationRecurrent Neural Networks and Sentiment AnalysisConvolutional Neural Networks for Text ClassificationText Translation using Sequence to Sequence Neural NetworksBuilding a Chatbot Using Attention-based Neural NetworksThe Road Ahead
Key FeaturesGet to grips with word embeddings, semantics, labeling, and high-level word representations using practical examplesLearn modern approaches to NLP and explore state-of-the-art NLP models using PyTorchImprove your NLP applications with innovative neural networks such as RNNs, LSTMs, and CNNsBook DescriptionIn the internet age, where an increasing volume of text data is generated daily from social media and other platforms, being able to make sense of that data is a crucial skill. With this book, you’ll learn how to extract valuable insights from text by building deep learning models for natural language processing (NLP) tasks.
Starting by understanding how to install PyTorch and using CUDA to accelerate the processing speed, you’ll explore how the NLP architecture works with the help of practical examples. This PyTorch NLP book will guide you through core concepts such as word embeddings, CBOW, and tokenization in PyTorch. You’ll then learn techniques for processing textual data and see how deep learning can be used for NLP tasks. The book demonstrates how to implement deep learning and neural network architectures to build models that will allow you to classify and translate text and perform sentiment analysis. Finally, you’ll learn how to build advanced NLP models, such as conversational chatbots.
By the end of this book, you’ll not only have understood the different NLP problems that can be solved using deep learning with PyTorch, but also be able to build models to solve them.
What you will learnUse NLP techniques for understanding, processing, and generating textUnderstand PyTorch, its applications and how it can be used to build deep linguistic modelsExplore the wide variety of deep learning architectures for NLPDevelop the skills you need to process and represent both structured and unstructured NLP dataBecome well-versed with state-of-the-art technologies and exciting new developments in the NLP domainCreate chatbots using attention-based neural networksWho this book is forThis PyTorch book is for NLP developers, machine learning and deep learning developers, and anyone interested in building intelligent language applications using both traditional NLP approaches and deep learning architectures. If you’re looking to adopt modern NLP techniques and models for your development projects, this book is for you. Working knowledge of Python programming, along with basic working knowledge of NLP tasks, is required.
Table of ContentsFundamentals of Machine Learning and Deep LearningGetting Started with PyTorch 1.x for NLPNLP and Text EmbeddingsText Preprocessing, Stemming, and LemmatizationRecurrent Neural Networks and Sentiment AnalysisConvolutional Neural Networks for Text ClassificationText Translation using Sequence to Sequence Neural NetworksBuilding a Chatbot Using Attention-based Neural NetworksThe Road Ahead
276 pages, Kindle Edition
Published July 9, 2020
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September 8, 2021
The book was awesome, i read it when i was at the beginning of learning pytorch and i was week at NLP. This book helped me so much, specially when i was so frustrated. Because i didn't found any clear article that could help me as a very beginner learner. He raised me to the top so fast. I was hooked perfectly.
The book divided into 3 sections with 9 chapters:
In the first section the book will take you through the basics of deep learning and networks. Then will move to the fundamentals of pytorch and how to build a network from scratch, and how to create the important classes like data class that Dataloader will use it.
After that in the second section you will learn the fundamentals and the basics of NLP. And then about cleaning and processing.
In the last section you will apply real world applications. Each problem will use different Architecture (CNN, LSTM, BiLSTM ,ATTENTION-BASED AND SEC2SEC MODELS) explained very well.
The book divided into 3 sections with 9 chapters:
In the first section the book will take you through the basics of deep learning and networks. Then will move to the fundamentals of pytorch and how to build a network from scratch, and how to create the important classes like data class that Dataloader will use it.
After that in the second section you will learn the fundamentals and the basics of NLP. And then about cleaning and processing.
In the last section you will apply real world applications. Each problem will use different Architecture (CNN, LSTM, BiLSTM ,ATTENTION-BASED AND SEC2SEC MODELS) explained very well.
Displaying 1 of 1 review