Recurrent Neural Networks with Python Quick Start Guide

by: Simeon Kostadinov (Author)

Publisher: Packt Publishing

Publication Date: 2018/11/29

Language: English

Print Length: 122 pages

ISBN-10: 1789132339

ISBN-13: 9781789132335

Book Description

Lea how to develop intelligent applications with sequential leaing and apply mode methods for language modeling with neural network architectures for deep leaing with Python’s most popular TensorFlow framework. Key FeaturesTrain and deploy Recurrent Neural Networks using the popular TensorFlow library Apply long short-term memory units Expand your skills in complex neural network and deep leaing topicsBook DescriptionDevelopers struggle to find an easy-to-follow leaing resource for implementing Recurrent Neural Network (RNN) models. RNNs are the state-of-the-art model in deep leaing for dealing with sequential data. From language translation to generating captions for an image, RNNs are used to continuously improve results. This book will teach you the fundamentals of RNNs, with example applications in Python and the TensorFlow library. The examples are accompanied by the right combination of theoretical knowledge and real-world implementations of concepts to build a solid foundation of neural network modeling. Your jouey starts with the simplest RNN model, where you can grasp the fundamentals. The book then builds on this by proposing more advanced and complex algorithms. We use them to explain how a typical state-of-the-art RNN model works. From generating text to building a language translator, we show how some of today’s most powerful AI applications work under the hood. After reading the book, you will be confident with the fundamentals of RNNs, and be ready to pursue further study, along with developing skills in this exciting field. What you will leaUse TensorFlow to build RNN models Use the correct RNN architecture for a particular machine leaing task Collect and clear the training data for your models Use the correct Python libraries for any task during the building phase of your model Optimize your model for higher accuracy Identify the differences between multiple models and how you can substitute them Lea the core deep leaing fundamentals applicable to any machine leaing model Who this book is forThis book is for Machine Leaing engineers and data scientists who want to lea about Recurrent Neural Network models with practical use-cases. Exposure to Python programming is required. Previous experience with TensorFlow will be helpful, but not mandatory.Table of ContentsIntroducing Recurrent Neural NetworksBuilding Your First RNN with TensorFlowGenerating Your Own Book ChapterCreating a Spanish-to-English TranslatorBuild Your Personal AssistantImprove Your RNN Performance

About the Author

Lea how to develop intelligent applications with sequential leaing and apply mode methods for language modeling with neural network architectures for deep leaing with Python’s most popular TensorFlow framework. Key FeaturesTrain and deploy Recurrent Neural Networks using the popular TensorFlow library Apply long short-term memory units Expand your skills in complex neural network and deep leaing topicsBook DescriptionDevelopers struggle to find an easy-to-follow leaing resource for implementing Recurrent Neural Network (RNN) models. RNNs are the state-of-the-art model in deep leaing for dealing with sequential data. From language translation to generating captions for an image, RNNs are used to continuously improve results. This book will teach you the fundamentals of RNNs, with example applications in Python and the TensorFlow library. The examples are accompanied by the right combination of theoretical knowledge and real-world implementations of concepts to build a solid foundation of neural network modeling. Your jouey starts with the simplest RNN model, where you can grasp the fundamentals. The book then builds on this by proposing more advanced and complex algorithms. We use them to explain how a typical state-of-the-art RNN model works. From generating text to building a language translator, we show how some of today’s most powerful AI applications work under the hood. After reading the book, you will be confident with the fundamentals of RNNs, and be ready to pursue further study, along with developing skills in this exciting field. What you will leaUse TensorFlow to build RNN models Use the correct RNN architecture for a particular machine leaing task Collect and clear the training data for your models Use the correct Python libraries for any task during the building phase of your model Optimize your model for higher accuracy Identify the differences between multiple models and how you can substitute them Lea the core deep leaing fundamentals applicable to any machine leaing model Who this book is forThis book is for Machine Leaing engineers and data scientists who want to lea about Recurrent Neural Network models with practical use-cases. Exposure to Python programming is required. Previous experience with TensorFlow will be helpful, but not mandatory.Table of ContentsIntroducing Recurrent Neural NetworksBuilding Your First RNN with TensorFlowGenerating Your Own Book ChapterCreating a Spanish-to-English TranslatorBuild Your Personal AssistantImprove Your RNN Performance