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Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R

1. Authors: Taweh Beysolow II
   
2. 
   
3. ISBN-10 书号: 1484227336
   
4. 
   
5. ISBN-13 书号: 9781484227336
   
6. 
   
7. Edition: 1st ed.
   
8. 
   
9. Release 出版日期: 2017-08-18
   
10. 
    
11. pages 页数: (227)
    
12. 
    
13. 
    
14. 59.99
    
15. 
    
16. Book Description
    
17. Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R
    
18. Understand deep learning, the nuances of its different models, and where these models can be applied.
    
19. 
    
20. The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the fundamentals of data science through machine learning and deep learning. This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. All examples are taught in the R statistical language, allowing students and professionals to implement these techniques using open source tools.
    
21. 
    
22. What You’ll Learn
    
23. Understand the intuition and mathematics that power deep learning models
    
24. Utilize various algorithms using the R programming language and its packages
    
25. Use best practices for experimental design and variable selection
    
26. Practice the methodology to approach and effectively solve problems as a data scientist
    
27. Evaluate the effectiveness of algorithmic solutions and enhance their predictive power
    
28. Who This Book Is For
    
29. Students, researchers, and data scientists who are familiar with programming using R. This book also is also of use for those who wish to learn how to appropriately deploy these algorithms in applications where they would be most useful.
    
30. 
    
31. Contents
    
32. Chapter 1: Introduction to Deep Learning
    
33. Chapter 2: Mathematical Review
    
34. Chapter 3: A Review of Optimization and Machine Learning
    
35. Chapter 4: Single and Multilayer Perceptron Models
    
36. Chapter 5: Convolutional Neural Networks (CNNs)
    
37. Chapter 6: Recurrent Neural Networks (RNNs)
    
38. Chapter 7: Autoencoders, Restricted Boltzmann Machines, and Deep Belief Networks
    
39. Chapter 8: Experimental Design and Heuristics
    
40. Chapter 9: Hardware and Software Suggestions
    
41. Chapter 10: Machine Learning Example Problems
    
42. Chapter 11: Deep Learning and Other Example Problems
    
43. Chapter 12: Closing Statements

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