Editing Perceptron (section)

=== ''Perceptrons'' (1969) ===
{{Main|Perceptrons (book)}}
Although the perceptron initially seemed promising, it was quickly proved that perceptrons could not be trained to recognise many classes of patterns. This caused the field of [[neural network (machine learning)|neural network]] research to stagnate for many years, before it was recognised that a [[feedforward neural network]] with two or more layers (also called a [[multilayer perceptron]]) had greater processing power than perceptrons with one layer (also called a [[Feedforward neural network#A threshold (e.g. activation function) added|single-layer perceptron]]).

Single-layer perceptrons are only capable of learning [[linearly separable]] patterns.<ref name="Sejnowski">{{Cite book |last=Sejnowski |first=Terrence J.|author-link=Terry Sejnowski|url=https://books.google.com/books?id=9xZxDwAAQBAJ |title=The Deep Learning Revolution |date=2018|publisher=MIT Press |isbn=978-0-262-03803-4 |language=en|page=47}}</ref> For a classification task with some step activation function, a single node will have a single line dividing the data points forming the patterns. More nodes can create more dividing lines, but those lines must somehow be combined to form more complex classifications. A second layer of perceptrons, or even linear nodes, are sufficient to solve many otherwise non-separable problems.

In 1969, a famous book entitled ''[[Perceptrons (book)|Perceptrons]]'' by [[Marvin Minsky]] and [[Seymour Papert]] showed that it was impossible for these classes of network to learn an [[XOR]] function. It is often incorrectly believed that they also conjectured that a similar result would hold for a multi-layer perceptron network. However, this is not true, as both Minsky and Papert already knew that multi-layer perceptrons were capable of producing an XOR function. (See the page on ''[[Perceptrons (book)]]'' for more information.) Nevertheless, the often-miscited Minsky and Papert text caused a significant decline in interest and funding of neural network research. It took ten more years until neural network research experienced a resurgence in the 1980s.<ref name="Sejnowski"/>{{Verify source|date=October 2024|reason=Does the source support all of the preceding text and is "often incorrectly believed" true today or was it only true in the past?}} This text was reprinted in 1987 as "Perceptrons - Expanded Edition" where some errors in the original text are shown and corrected.