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Recurrent Neural Networks with Python Quick Start Guide : Sequential Learning and Language Modeling with TensorFlow.
Developers struggle to find an easy to follow learning resource for implementing Recurrent Neural Network(RNN) models. RNNs are the state-of-the-art model in deep learning for dealing with sequential data. From language translation to generating captions for an image, RNNs are used to continuously i...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Kostadinov, Simeon. | |
| 245 | 1 | 0 | |a Recurrent Neural Networks with Python Quick Start Guide : |b Sequential Learning and Language Modeling with TensorFlow. |
| 260 | |a Birmingham : |b Packt Publishing Ltd, |c 2018. | ||
| 300 | |a 1 online resource (115 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Introducing Recurrent Neural Networks; What is an RNN?; Comparing recurrent neural networks with similar models; Hidden Markov model; Recurrent neural network; Understanding how recurrent neural networks work; Basic neural network overview; Obtaining data; Encoding the data; Building the architecture ; Training the model; Evaluating the model; Key problems with the standard recurrent neural network model; Summary; External links; Chapter 2: Building Your First RNN with TensorFlow | |
| 505 | 8 | |a What are you going to build?Introduction to TensorFlow; Graph-based execution; Eager execution; Coding the recurrent neural network; Generating data; Building the TensorFlow graph; Training the RNN; Evaluating the predictions; Summary; External links; Chapter 3: Generating Your Own Book Chapter; Why use the GRU network?; Generating your book chapter; Obtaining the book text; Encoding the text; Building the TensorFlow graph; Training the network; Generating your new text; Summary; External links; Chapter 4: Creating a Spanish-to-English Translator; Understanding the translation model | |
| 505 | 8 | |a What is an LSTM network?Understanding the sequence-to-sequence network with attention; Building the Spanish-to-English translator; Preparing the data; Constructing the TensorFlow graph; Training the model; Predicting the translation; Evaluating the final results; Summary; External links; Chapter 5: Building Your Personal Assistant; What are we building?; Preparing the data; Creating the chatbot network; Training the chatbot; Building a conversation; Summary; External links; Chapter 6: Improving Your RNN Performance; Improving your RNN model; Improving performance with data; Selecting data | |
| 505 | 8 | |a Processing dataTransforming data; Improving performance with tuning; Grid search; Random search ; Hand-tuning; Bayesian optimization; Tree-structured Parzen Estimators (TPE); Optimizing the TensorFlow library; Data processing; Improving data loading; Improving data transformation; Performing the training; Optimizing gradients; Summary; External links; Other Books You May Enjoy; Index | |
| 520 | |a Developers struggle to find an easy to follow learning resource for implementing Recurrent Neural Network(RNN) models. RNNs are the state-of-the-art model in deep learning for dealing with sequential data. From language translation to generating captions for an image, RNNs are used to continuously improve the results. This book will teach you ... | ||
| 504 | |a Includes bibliographical references. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Neural networks (Computer science) | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Python (Langage de programmation) | |
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| 650 | 7 | |a Machine learning |2 fast | |
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| 650 | 7 | |a Python (Computer program language) |2 fast | |
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| 776 | 0 | 8 | |i Print version: |a Kostadinov, Simeon. |t Recurrent Neural Networks with Python Quick Start Guide : Sequential Learning and Language Modeling with TensorFlow. |d Birmingham : Packt Publishing Ltd, ©2018 |z 9781789132335 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5609746 |z Texto completo |
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