Recurrent Neural Networks with Python Quick Start Guide : Sequential Learning and Language Modeling with TensorFlow.

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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...

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Bibliographic Details
Call Number:Libro Electrónico
Main Author: Kostadinov, Simeon
Format: Electronic eBook
Language:Inglés
Published: Birmingham : Packt Publishing Ltd, 2018.
Subjects:
Neural networks (Computer science)
Machine learning.
Python (Computer program language)
Réseaux neuronaux (Informatique)
Apprentissage automatique.
Python (Langage de programmation)
COMPUTERS > General.
Machine learning
Online Access:Texto completo
Table of Contents:
  • 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
  • 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
  • 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
  • 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

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