Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Deep Learning with Keras

You're reading from   Deep Learning with Keras Implementing deep learning models and neural networks with the power of Python

Arrow left icon
Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787128422
Length 318 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (2):
Arrow left icon
Antonio Gulli Antonio Gulli
Author Profile Icon Antonio Gulli
Antonio Gulli
Sujit Pal Sujit Pal
Author Profile Icon Sujit Pal
Sujit Pal
Arrow right icon
View More author details
Toc

Table of Contents (10) Chapters Close

Preface 1. Neural Networks Foundations FREE CHAPTER 2. Keras Installation and API 3. Deep Learning with ConvNets 4. Generative Adversarial Networks and WaveNet 5. Word Embeddings 6. Recurrent Neural Network — RNN 7. Additional Deep Learning Models 8. AI Game Playing 9. Conclusion

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "In addition, we load the true labels into Y_train and Y_test respectively and perform a one-hot encoding on them."

A block of code is set as follows:

from keras.models import Sequential
model = Sequential()
model.add(Dense(12, input_dim=8, kernel_initializer='random_uniform'))

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

# 10 outputs
# final stage is softmax
model = Sequential()
model.add(Dense(NB_CLASSES, input_shape=(RESHAPED,)))
model.add(Activation('softmax'))
model.summary()

Any command-line input or output is written as follows:

pip install quiver_engine

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Our simple net started with an accuracy of 92.22%, which means that about eight handwritten characters out of 100 are not correctly recognized."

Warnings or important notes appear in a box like this.
Tips and tricks appear like this.
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image