Baydin, Atilim Gunes and Pearlmutter, Barak A.
(2014)
Automatic Differentiation of Algorithms
for Machine Learning.
Working Paper.
arXiv.
Abstract
Automatic differentiation --- the mechanical transformation of numeric computer programs to calculate derivatives efficiently and accurately --- dates to the origin of the computer age. Reverse mode automatic differentiation both antedates and generalizes the method of backwards propagation of errors used in machine learning. Despite this, practitioners in a variety of fields, including machine learning, have been little influenced by automatic differentiation, and make scant use of available tools. Here we review the technique of automatic differentiation, describe its two main modes, and explain how it can benefit machine learning practitioners. To reach the widest possible audience our treatment assumes only elementary differential calculus, and does not assume any knowledge of linear algebra.
| Item Type: |
Monograph
(Working Paper)
|
| Keywords: |
Automatic Differentiation; Algorithms; Machine Learning; |
| Academic Unit: |
Faculty of Science and Engineering > Computer Science |
| Item ID: |
6276 |
| Identification Number: |
arXiv:1404.7456 |
| Depositing User: |
Barak Pearlmutter
|
| Date Deposited: |
21 Jul 2015 14:43 |
| Publisher: |
arXiv |
| Refereed: |
Yes |
| URI: |
|
| Use Licence: |
This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available
here |
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