Deep Learning with R, Second Edition, Video Edition

Deep Learning with R, Second Edition, Video Edition

by Francois Chollet, Sigrid Keydana, Tomasz Kalinowski, J.J. Allaire
Released October 2022
Publisher(s): Manning Publications
ISBN: None

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Video description

In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.

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 put deep learning into action. 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 to 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 if they have no 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 and 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.

About the Authors
François Chollet is a software engineer at Google and creator of Keras. Tomasz Kalinowski is a software engineer at RStudio and maintainer of the Keras and Tensorflow R packages. J.J. Allaire is the founder of RStudio, and the author of the first edition of this book.

Quotes
A must-have for scientists and technicians who want to expand their knowledge.
- Fernando García Sedano, Grupo Epelsa

Whether you are new to deep learning or wanting to expand your applications in R, there is no better guide.
- Michael Petrey, Boxplot Analytics

The clear illustrations and insightful examples are helpful to anybody, from beginners to experienced deep learning practitioners.
- Edward Lee, Yale University

Outstandingly well written.
- Shahnawaz Ali, King’s College London

Table of contents

  1. Chapter 1. What is deep learning?
  2. Chapter 1. Before deep learning: A brief history of machine learning
  3. Chapter 1. Why deep learning? Why now?
  4. Chapter 2. The mathematical building blocks of neural networks
  5. Chapter 2. Data representations for neural networks
  6. Chapter 2. The gears of neural networks: Tensor operations
  7. Chapter 2. The engine of neural networks: Gradient-based optimization
  8. Chapter 2. Looking back at our first example
  9. Chapter 2. Summary
  10. Chapter 3. Introduction to Keras and TensorFlow
  11. Chapter 3. What’s Keras?
  12. Chapter 3. Keras and TensorFlow: A brief history
  13. Chapter 3. Python and R interfaces: A brief history
  14. Chapter 3. Setting up a deep learning workspace
  15. Chapter 3. First steps with TensorFlow
  16. Chapter 3. Tensor attributes
  17. Chapter 3. Anatomy of a neural network: Understanding core Keras APIs
  18. Chapter 3. Summary
  19. Chapter 4. Getting started with neural networks: Classification and regression
  20. Chapter 4. Classifying newswires: A multiclass classification example
  21. Chapter 4. Predicting house prices: A regression example
  22. Chapter 4. Summary
  23. Chapter 5. Fundamentals of machine learning
  24. Chapter 5. Evaluating machine learning models
  25. Chapter 5. Improving model fit
  26. Chapter 5. Improving generalization
  27. Chapter 5. Summary
  28. Chapter 6. The universal workflow of machine learning
  29. Chapter 6. Develop a model
  30. Chapter 6. Deploy the model
  31. Chapter 6. Summary
  32. Chapter 7. Working with Keras: A deep dive
  33. Chapter 7. Different ways to build Keras models
  34. Chapter 7. Using built-in training and evaluation loops
  35. Chapter 7. Writing your own training and evaluation loops
  36. Chapter 7. Summary
  37. Chapter 8. Introduction to deep learning for computer vision
  38. Chapter 8. Training a convnet from scratch on a small dataset
  39. Chapter 8. Leveraging a pretrained model
  40. Chapter 8. Summary
  41. Chapter 9. Advanced deep learning for computer vision
  42. Chapter 9. An image segmentation example
  43. Chapter 9. Modern convnet architecture patterns
  44. Chapter 9. Interpreting what convnets learn
  45. Chapter 9. Summary
  46. Chapter 10. Deep learning for time series
  47. Chapter 10. A temperature-forecasting example
  48. Chapter 10. Understanding recurrent neural networks
  49. Chapter 10. Advanced use of recurrent neural networks
  50. Chapter 10. Summary
  51. Chapter 11. Deep learning for text
  52. Chapter 11. Preparing text data
  53. Chapter 11. Two approaches for representing groups of words: Sets and sequences
  54. Chapter 11. The Transformer architecture
  55. Chapter 11. Beyond text classification: Sequence-to-sequence learning
  56. Chapter 11. Summary
  57. Chapter 12. Generative deep learning
  58. Chapter 12. DeepDream
  59. Chapter 12. Neural style transfer
  60. Chapter 12. Generating images with variational autoencoders
  61. Chapter 12. Introduction to generative adversarial networks
  62. Chapter 12. Summary
  63. Chapter 13. Best practices for the real world
  64. Chapter 13. Scaling-up model training
  65. Chapter 13. Summary
  66. Chapter 14. Conclusions
  67. Chapter 14. The limitations of deep learning
  68. Chapter 14. Setting the course toward greater generality in AI
  69. Chapter 14. Implementing intelligence: The missing ingredients
  70. Chapter 14. The future of deep learning
  71. Chapter 14. Staying up-to-date in a fast-moving field
  72. Chapter 14. Final words

Product information

  • Title: Deep Learning with R, Second Edition, Video Edition
  • Author(s): Francois Chollet, Sigrid Keydana, Tomasz Kalinowski, J.J. Allaire
  • Release date: October 2022
  • Publisher(s): Manning Publications
  • ISBN: None