Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Deep Learning with R for Beginners

You're reading from   Deep Learning with R for Beginners Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet

Arrow left icon
Product type Course
Published in May 2019
Publisher Packt
ISBN-13 9781838642709
Length 612 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (4):
Arrow left icon
Mark Hodnett Mark Hodnett
Author Profile Icon Mark Hodnett
Mark Hodnett
Pablo Maldonado Pablo Maldonado
Author Profile Icon Pablo Maldonado
Pablo Maldonado
Joshua F. Wiley Joshua F. Wiley
Author Profile Icon Joshua F. Wiley
Joshua F. Wiley
Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Author Profile Icon Yuxi (Hayden) Liu
Yuxi (Hayden) Liu
Arrow right icon
View More author details
Toc

Table of Contents (23) Chapters Close

Title Page
Copyright and Credits
About Packt
Contributors
Preface
1. Getting Started with Deep Learning FREE CHAPTER 2. Training a Prediction Model 3. Deep Learning Fundamentals 4. Training Deep Prediction Models 5. Image Classification Using Convolutional Neural Networks 6. Tuning and Optimizing Models 7. Natural Language Processing Using Deep Learning 8. Deep Learning Models Using TensorFlow in R 9. Anomaly Detection and Recommendation Systems 10. Running Deep Learning Models in the Cloud 11. The Next Level in Deep Learning 12. Handwritten Digit Recognition using Convolutional Neural Networks 13. Traffic Signs Recognition for Intelligent Vehicles 14. Fraud Detection with Autoencoders 15. Text Generation using Recurrent Neural Networks 16. Sentiment Analysis with Word Embedding 1. Other Books You May Enjoy Index

Chapter 12. Handwritten Digit Recognition using Convolutional Neural Networks

Now we dive deep into our R deep learning journey with the fundamental and core concepts of deep learning, and a deep learning 101 project—handwritten digit recognition. We will start with what deep learning is about, why we need it, and its evolution in recent years. We will also discuss why deep learning stands out and several typical deep learning applications. With the important deep learning concepts in mind, we get it started with our image classification project where we first conduct exploratory analysis on the data and make an initial attempt using shallow single-layer neural networks. Then we move on with deeper neural networks and achieve better results. However, we argue that chaining more hidden layers does not necessarily improve classification performance. The key is to extract richer representation and more informative features. And convolutional neural networks (CNNs) are the way to go! We will...

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