In this section, we will use a new dataset to create a binary classification task. The dataset we will use here is a transactional dataset that is available at https://www.dunnhumby.com/sourcefiles. This dataset has been made available from dunnhumby, which is perhaps best known for its link to the Tesco (a British grocery store) club-card, which is one of the largest retail loyalty systems in the world. I recommend the following book, which describes how dunnhumby helped Tesco to become the number one retailer by applying analytics to their retail loyalty program: Humby, Clive, Terry Hunt, and Tim Phillips. Scoring points. Kogan Page Publishers, 2008. Even though this book is relatively old, it remains one of the best use cases to describe how to roll out a business-transformation program based on data analytics.
Deep Learning with R for Beginners
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Deep Learning with R for Beginners
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Overview of this book
Deep learning has a range of practical applications in several domains, while R is the preferred language for designing and deploying deep learning models.
This Learning Path introduces you to the basics of deep learning and even teaches you to build a neural network model from scratch. As you make your way through the chapters, you’ll explore deep learning libraries and understand how to create deep learning models for a variety of challenges, right from anomaly detection to recommendation systems. The Learning Path will then help you cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud, in addition to model optimization, overfitting, and data augmentation. Through real-world projects, you’ll also get up to speed with training convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) in R.
By the end of this Learning Path, you’ll be well-versed with deep learning and have the skills you need to implement a number of deep learning concepts in your research work or projects.
Table of Contents (23 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Free Chapter
Getting Started with Deep Learning
Training a Prediction Model
Deep Learning Fundamentals
Training Deep Prediction Models
Image Classification Using Convolutional Neural Networks
Tuning and Optimizing Models
Natural Language Processing Using Deep Learning
Deep Learning Models Using TensorFlow in R
Anomaly Detection and Recommendation Systems
Running Deep Learning Models in the Cloud
The Next Level in Deep Learning
Handwritten Digit Recognition using Convolutional Neural Networks
Traffic Signs Recognition for Intelligent Vehicles
Fraud Detection with Autoencoders
Text Generation using Recurrent Neural Networks
Sentiment Analysis with Word Embedding
Other Books You May Enjoy
Index
Customer Reviews