Learn how to develop intelligent applications with sequential learning and apply modern methods for language modeling with neural network architectures for deep learning with Python's most popular TensorFlow framework.
- Train 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 learning topics
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 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 journey 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 learn
- Use TensorFlow to build RNN models
- Use the correct RNN architecture for a particular machine learning 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
- Learn the core deep learning fundamentals applicable to any machine learning model
Who this book is for
This book is for Machine Learning engineers and data scientists who want to learn 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 contents
- Title Page
- Copyright and Credits
- About Packt
Introducing Recurrent Neural Networks
- What is an RNN?
- Comparing recurrent neural networks with similar models
- Understanding how recurrent neural networks work
- Key problems with the standard recurrent neural network model
- External links
- Building Your First RNN with TensorFlow
- Generating Your Own Book Chapter
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
- External links
- Building Your Personal Assistant
Improving Your RNN Performance
- Improving your RNN model
- Optimizing the TensorFlow library
- External links
- Other Books You May Enjoy
- Title: Recurrent Neural Networks with Python Quick Start Guide
- Release date: November 2018
- Publisher(s): Packt Publishing
- ISBN: 9781789132335
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