Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Deep Learning with TensorFlow
  • Toc
  • feedback
Deep Learning with TensorFlow

Deep Learning with TensorFlow

By : Zaccone, Karim
3 (4)
close
Deep Learning with TensorFlow

Deep Learning with TensorFlow

3 (4)
By: Zaccone, Karim

Overview of this book

Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.
Table of Contents (13 chapters)
close
12
Index

How does an autoencoder work?

Autoencoding is a data compression technique where the compression and decompression functions are data-specific, lossy, and learned automatically from samples rather than human-crafted manual features. Additionally, in almost all contexts where the term autoencoder is used, the compression and decompression functions are implemented with NNs.

An autoencoder is a network with three or more layers, where the input and the output layers have the same number of neurons, and those intermediate (hidden layers) have a lower number of neurons. The network is trained to reproduce output simply, for each piece of input data, the same pattern of activity in the input.

The remarkable aspect of autoencoders is that, due to the lower number of neurons in the hidden layer, if the network can learn from examples and generalize to an acceptable extent, it performs data compression: the status of the hidden neurons provides, for each example, a compressed version of the input...

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
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