feed-forward neural nets

(2 hours to learn)

Summary

Feed-forward neural networks are a supervised learning architecture consisting of a set of neuron-like "units," each one of which computes a simple function of its inputs. Because layers of such neurons can be stacked, neural nets are capable of learning complex nonlinear functions of the inputs.

Context

This concept has the prerequisites:

Core resources (read/watch one of the following)

-Free-

Coursera: Machine Learning (2013)
An online machine learning course aimed at a broad audience.
Location: Lecture series "Neural networks: representation"
Author: Andrew Y. Ng
Other notes:
  • Click on "Preview" to see the videos.

-Paid-

Supplemental resources (the following are optional, but you may find them useful)

-Free-

Coursera: Machine Learning
An online machine learning course aimed at advanced undergraduates.
Author: Pedro Domingos
Additional dependencies:
  • gradient descent
  • perceptron algorithm
Other notes:
  • Click on "Preview" to see the videos.
The Elements of Statistical Learning
A graudate-level statistical learning textbook with a focus on frequentist methods.
Authors: Trevor Hastie,Robert Tibshirani,Jerome Friedman

See also