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

Chapter 4. Convolutional Neural Networks

In this chapter, we will talk about CNNs, which are a feather in the cap of deep learning. CNNs have achieved excellent results in many practical applications, particularly in the field of object recognition in images. We will explain and implement the LeNet architecture (LeNet5), which was the first CNN to have great success with the classic MNIST digit classification system. We will also analyze AlexNet, which is a deep CNN that was invented by Alex Krizhevsky. We'll use these networks to introduce transfer learning, which is a machine learning method that utilizes a pre-trained neural network. We will also introduce the VGG architecture, which is usually used as a deep CNN for object recognition. This was developed by Oxford University's renowned Visual Geometry Group (VGG), which performed very well with the ImageNet dataset. This architecture gives us the opportunity to show how to use a neural network to draw a picture in...

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