[This article was first published on Data R Value, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Neural networks have been a very important area of scientific study that has evolved by different disciplines such as mathematics, biology, psychology, computer science, etc.Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
The study of neural networks leapt from theory to practice with the emergence of computers.
Training a neural network by adjusting the weights of the connections is computationally very expensive so its application to practical problems took until the mid-80s when a more efficient algorithm was discovered.
That algorithm is now known as back-propagation errors or simply backpropagation.
One of the most cited articles on this algorithm is:
Learning representations by back-propagating errors
David E. Rumelhart*, Geoffrey E. Hinton† & Ronald J. Williams*
Nature 323, 533 – 536 (09 October 1986)
Although it is a very technical article, anyone who wants to study and understand neural networks is obliged to pass through this material.
I share the entire article in:
https://github.com/pakinja/Data-R-Value
To leave a comment for the author, please follow the link and comment on their blog: Data R Value.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.