Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Recurrent Neural Networks with Python Quick Start Guide
  • Table Of Contents Toc
  • Feedback & Rating feedback
Recurrent Neural Networks with Python Quick Start Guide

Recurrent Neural Networks with Python Quick Start Guide

By : Kostadinov
3 (4)
close
close
Recurrent Neural Networks with Python Quick Start Guide

Recurrent Neural Networks with Python Quick Start Guide

3 (4)
By: Kostadinov

Overview of this book

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.
Table of Contents (8 chapters)
close
close

Improving your RNN model

When working on a problem using RNN (or any other network), your process looks like this:

First, you come up with an idea for the model, its hyperparameters, the number of layers, how deep the network should be, and so on. Then the model is implemented and trained in order to produce some results. Finally, these results are assessed and the necessary modifications are made. It is rarely the case that you'll receive meaningful results from the first run. This cycle may occur multiple times until you are satisfied with the outcome. 

Considering this approach, one important question comes to mind: How can we change the model so the next cycle produces better results?

This question is tightly connected to your understanding of the network's results. Let's discuss that now. 

As you already know, in the beginning of each...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech

Create a Note

Modal Close icon
You need to login to use this feature.
notes
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Delete Note

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Edit Note

Modal Close icon
Write a note (max 255 characters)
Cancel
Update Note

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY