Deep Learning with R (2 ed)

Description

Deep Learning with R, Second Edition 2nd ed. Edition

We are conducting deep learning from the ground up using R and the powerful Keras library!

In Deep Learning with R, Second Edition, you will learn:

• Deep learning from first principles
• Image classification and image segmentation
• Time series forecasting
• Text classification and machine translation
• Text generation, neural style transfer, and image generation

Deep Learning with R, Second Edition shows you how to implement deep learning. It’s based on the revised new edition of François Chollet’s bestselling Deep Learning with Python. All code and examples have been expertly translated into the R language by Tomasz Kalinowski, who maintains the Keras and Tensorflow R packages at RStudio. Novices and experienced ML practitioners will love the expert insights, practical techniques, and important theory for building neural networks.

About the technology

Deep learning has become essential knowledge for data scientists, researchers, and software developers. The R language APIs for Keras and TensorFlow put deep learning within reach for all R users, even without experience with advanced machine learning or neural networks. This book shows you how to get started on core DL tasks like computer vision, natural language processing, and more using R.

About the book

Deep Learning with R, Second Edition is a hands-on guide to deep learning using the R language. As you move through this book, you’ll quickly lock in the foundational ideas of deep learning. The intuitive explanations, crisp illustrations, and clear examples guide you through core DL skills like image processing, text manipulation, and even advanced features like transformers. This revised and expanded new edition is adapted from Deep Learning with Python, Second Edition by François Chollet, the creator of the Keras library.

What's inside

• Image classification and image segmentation
• Time series forecasting
• Text classification and machine translation
• Text generation, neural style transfer, and image generation

About the reader

For readers with intermediate R skills. No previous experience with Keras, TensorFlow, or deep learning is required.

Table of Contents

1. What is deep learning?
2. The mathematical building blocks of neural networks
3. Introduction to Keras and TensorFlow
4. Getting started with neural networks: Classification and regression
5. Fundamentals of Machine Learning
6. The universal workflow of machine learning
7. Working with Keras: A deep dive
8. Introduction to deep learning for computer vision
9. Advanced deep learning for computer vision
10. Deep learning for time series
11. Deep learning for text
12. Generative deep learning
13. Best Practices for the Real World
14. Conclusions