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.

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Spis treści

R Deep Learning Essentials. A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet - Second Edition eBook -- spis treści

• 1. Getting Started with Deep Learning
• 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