This chapter covered topics that are critical to success in deep learning projects. These included the different types of evaluation metric that can be used to evaluate the model. We looked at some issues that can come up in data preparation, including if you only have a small amount of data to train on and how to create different splits in the data, that is, how to create proper train, test, and validation datasets. We looked at two important issues that can cause the model to perform poorly in production, different data distributions, and data leakage. We saw how data augmentation can be used to improve an existing model by creating artificial data and looked at tuning hyperparameters in order to improve the performance of a deep learning model. We closed the chapter by examining a use case where we simulated a problem with different data distributions/data leakage and...
R Deep Learning Essentials
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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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