IniciGrupsConversesMésTendències
Has donat un cop d'ull al SantaThing, la tradició de fer-se regals de LibraryThing?
ignora
Aquest lloc utilitza galetes per a oferir els nostres serveis, millorar el desenvolupament, per a anàlisis i (si no has iniciat la sessió) per a publicitat. Utilitzant LibraryThing acceptes que has llegit i entès els nostres Termes de servei i política de privacitat. L'ús que facis del lloc i dels seus serveis està subjecte a aquestes polítiques i termes.
Hide this

Resultats de Google Books

Clica una miniatura per anar a Google Books.

S'està carregant…

Hands-On Natural Language Processing with Python: A practical guide to…

de Rajesh Arumugam

MembresRessenyesPopularitatValoració mitjanaConverses
2No n'hi ha cap4,164,496No n'hi ha capNo n'hi ha cap
Foster your NLP applications with the help of deep learning, NLTK, and TensorFlow Key Features Weave neural networks into linguistic applications across various platforms Perform NLP tasks and train its models using NLTK and TensorFlow Boost your NLP models with strong deep learning architectures such as CNNs and RNNs Book Description 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. What 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 Who this book is for Hands-on Natural Language Processing with Python is for you if you are a developer, machine learning or an NLP engineer who wants to build a deep learning application that leverages NLP techniques. This comprehensive guide is also useful for deep learning users who want to extend their deep learning skills in building NLP applications. All you need is the basics of machine learning and Python to enjoy the book.… (més)
Afegit fa poc perphilipanderson

No n'hi ha cap.

No n'hi ha cap
S'està carregant…

Apunta't a LibraryThing per saber si aquest llibre et pot agradar.

No hi ha cap discussió a Converses sobre aquesta obra.

Sense ressenyes
Sense ressenyes | afegeix-hi una ressenya
Has d'iniciar sessió per poder modificar les dades del coneixement compartit.
Si et cal més ajuda, mira la pàgina d'ajuda del coneixement compartit.
Títol normalitzat
Títol original
Títols alternatius
Data original de publicació
Gent/Personatges
Llocs importants
Esdeveniments importants
Pel·lícules relacionades
Premis i honors
Epígraf
Dedicatòria
Primeres paraules
Citacions
Darreres paraules
Nota de desambiguació
Editor de l'editorial
Creadors de notes promocionals a la coberta
Llengua original
CDD/SMD canònics

Referències a aquesta obra en fonts externes.

Wikipedia en anglès

No n'hi ha cap

Foster your NLP applications with the help of deep learning, NLTK, and TensorFlow Key Features Weave neural networks into linguistic applications across various platforms Perform NLP tasks and train its models using NLTK and TensorFlow Boost your NLP models with strong deep learning architectures such as CNNs and RNNs Book Description 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. What 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 Who this book is for Hands-on Natural Language Processing with Python is for you if you are a developer, machine learning or an NLP engineer who wants to build a deep learning application that leverages NLP techniques. This comprehensive guide is also useful for deep learning users who want to extend their deep learning skills in building NLP applications. All you need is the basics of machine learning and Python to enjoy the book.

No s'han trobat descripcions de biblioteca.

Descripció del llibre
Sumari haiku

Dreceres

Cobertes populars

Valoració

Mitjana: Sense puntuar.

Ets tu?

Fes-te Autor del LibraryThing.

 

Quant a | Contacte | LibraryThing.com | Privadesa/Condicions | Ajuda/PMF | Blog | Botiga | APIs | TinyCat | Biblioteques llegades | Crítics Matiners | Coneixement comú | 152,519,631 llibres! | Barra superior: Sempre visible