This use-case is about collaborative filtering. We are going to build a recommendation system based on embeddings created from a deep learning model. To do this, we are going to use the same dataset we used in Chapter 4, Training Deep Prediction Models, which is the retail transactional database. If you have not already downloaded the database, then go to the following link, https://www.dunnhumby.com/sourcefiles, and select Let’s Get Sort-of-Real. Select the option for the smallest dataset, titled All transactions for a randomly selected sample of 5,000 customers. Once you have read the terms and conditions and downloaded the dataset to your computer, unzip it into a directory called dunnhumby/in under the code folder. Ensure that the files are unzipped directly under this folder, and not a subdirectory, as you may have to copy...
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R Deep Learning Essentials - Second Edition
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R Deep Learning Essentials
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Overview of this book
Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem.
This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You’ll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics.
By the end of this book, you will be fully prepared and able to implement deep learning concepts in your research work or projects.
Table of Contents (13 chapters)
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
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