Hands-On Natural Language Processing with Python. A practical guide to applying deep learning architectures to your NLP applications
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- Nombre de pages298
- PrésentationBroché
- FormatGrand Format
- Poids0.59 kg
- Dimensions19,2 cm × 23,6 cm × 2,0 cm
- ISBN978-1-78913-949-5
- EAN9781789139495
- Date de parution01/07/2018
- ÉditeurPackt Publishing
Résumé
Natural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today's NLP challenges.
To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection.
You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow. By the end of this book, you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges with best practices developed by domain experts. Things you will learn : Implement semantic embedding of words to classify and find entities ; Convert words to vectors by training in order to perform arithmetic operations ; Train a deep learning model to detect classification of tweets and news ; Implement a question-answer model with search and RNN models ; Train models for various text classification datasets using CNN ; Implement WaveNet a deep generative model for producing a natural-sounding voice ; Convert voice-to-text and text-to-voice ; Train a model to convert speech-to-text using DeepSpeech.
To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection.
You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow. By the end of this book, you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges with best practices developed by domain experts. Things you will learn : Implement semantic embedding of words to classify and find entities ; Convert words to vectors by training in order to perform arithmetic operations ; Train a deep learning model to detect classification of tweets and news ; Implement a question-answer model with search and RNN models ; Train models for various text classification datasets using CNN ; Implement WaveNet a deep generative model for producing a natural-sounding voice ; Convert voice-to-text and text-to-voice ; Train a model to convert speech-to-text using DeepSpeech.
Natural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today's NLP challenges.
To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection.
You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow. By the end of this book, you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges with best practices developed by domain experts. Things you will learn : Implement semantic embedding of words to classify and find entities ; Convert words to vectors by training in order to perform arithmetic operations ; Train a deep learning model to detect classification of tweets and news ; Implement a question-answer model with search and RNN models ; Train models for various text classification datasets using CNN ; Implement WaveNet a deep generative model for producing a natural-sounding voice ; Convert voice-to-text and text-to-voice ; Train a model to convert speech-to-text using DeepSpeech.
To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection.
You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow. By the end of this book, you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges with best practices developed by domain experts. Things you will learn : Implement semantic embedding of words to classify and find entities ; Convert words to vectors by training in order to perform arithmetic operations ; Train a deep learning model to detect classification of tweets and news ; Implement a question-answer model with search and RNN models ; Train models for various text classification datasets using CNN ; Implement WaveNet a deep generative model for producing a natural-sounding voice ; Convert voice-to-text and text-to-voice ; Train a model to convert speech-to-text using DeepSpeech.