Recurrent Neural Networks with Python Quick Start Guide: Sequential learning and language modeling with TensorFlow

Recurrent Neural Networks with Python Quick Start Guide: Sequential learning and language modeling with TensorFlow
Recurrent Neural Networks with Python Quick Start Guide: Sequential learning and language modeling with TensorFlow by Simeon Kostadinov
English | 2018 | ISBN: 1789132335 | 122 Pages | EPUB | 10 MB

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.
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