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Deep Learning with TensorFlow

You're reading from   Deep Learning with TensorFlow Explore neural networks and build intelligent systems with Python

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788831109
Length 484 pages
Edition 2nd Edition
Languages
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Authors (2):
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Giancarlo Zaccone Giancarlo Zaccone
Author Profile Icon Giancarlo Zaccone
Giancarlo Zaccone
Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Deep Learning 2. A First Look at TensorFlow FREE CHAPTER 3. Feed-Forward Neural Networks with TensorFlow 4. Convolutional Neural Networks 5. Optimizing TensorFlow Autoencoders 6. Recurrent Neural Networks 7. Heterogeneous and Distributed Computing 8. Advanced TensorFlow Programming 9. Recommendation Systems Using Factorization Machines 10. Reinforcement Learning Other Books You May Enjoy 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...

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