Recurrent neural networks with python quick start guide

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Recurrent Neural Networks with Python Quick Start Guide, published by Packt

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README.md

Recurrent Neural Networks with Python Quick Start Guide, published by Packt

This is the code repository for Recurrent Neural Networks with Python Quick Start Guide, published by Packt.

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 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.

This book covers the following exciting features:

• 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

If you feel this book is for you, get your copy today!

Instructions and Navigations

All of the code is organized into folders. For example, Chapter02.

The code will look like the following:

def generate_data(): inputs = input_values() return inputs, output_values(inputs) 

Following is what you need for this book: 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.

With the following software and hardware list you can run all code files present in the book (Chapter 1-6).

Software and Hardware List

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

Simeon Kostadinov is a student at the University of Birmingham, who also lives in San Francisco and works for a startup called Speechify which aims to help people go through their readings faster by converting any text into speech. Simeon is Machine Learning enthusiast who writes a blog and works on various projects on the side. His technical experience includes heavy university knowledge, two summer internships and two years of practical experience. Moreover, his blog includes explanations of numerous deep learning techniques. He enjoys reading different research papers and implement some of them in code. His interest covers both the theoretical as well as practical side of deep learning since his background is in mathematics and throughout time he ignited his interest in computer science. He was ranked number 1 in mathematics during his senior year of high school and thus he has deep passion about understanding how the deep learning models work under the hood. His specific knowledge in Recurrent Neural Networks comes from several courses that he has taken at Stanford University and University of Birmingham. They helped in understanding how to apply his theoretical knowledge into practice and build powerful models. In addition, he recently became a Stanford Scholar Initiative which includes working in a team of Machine Learning researchers on a specific deep learning research paper.

Click here if you have any feedback or suggestions.

If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.

About

Recurrent Neural Networks with Python Quick Start Guide, published by Packt

Источник

Recurrent Neural Networks with Python Quick Start Guide

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Book description

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.

Key Features

• 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

Book Description

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.

Источник