{"id":450,"date":"2020-09-06T12:44:04","date_gmt":"2020-09-06T12:44:04","guid":{"rendered":"https:\/\/aaidm.buas.nl\/?page_id=450"},"modified":"2020-09-06T16:07:14","modified_gmt":"2020-09-06T16:07:14","slug":"ai1-preparation-for-week-2-what-is-a-perceptron","status":"publish","type":"page","link":"https:\/\/aaidm.buas.nl\/?page_id=450","title":{"rendered":"AI1- Preparation for Week 2 – What is a Perceptron"},"content":{"rendered":"\n
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Author:<\/td>Francesco Lelli<\/a><\/td><\/tr>
Learning Line:<\/td>What is a Perceptron and how you can use it<\/td><\/tr>
Course:<\/td>AI1: Introduction to Neural Networks<\/td><\/tr>
Week:<\/td>2<\/td><\/tr>
Competencies:<\/td>Understand and implement a Perceptron<\/td><\/tr>
BoKS:<\/td>3aK1 The student understands a supervised learning process and its components. She knows how to apply such a strategy in a specific (business) context.<\/td><\/tr>
Learning Goals:<\/td>Getting familiar with the notion of Perceptron and how you can use it<\/td><\/tr><\/tbody><\/table><\/figure>\n<\/div><\/div>\n\n\n\n

In a 1958 press conference organized by the US Navy, Rosenblatt made statements about the perceptron that caused a heated controversy among the fledgling AI community; based on Rosenblatt’s statements, The New York Times reported the perceptron to be “the embryo of an electronic computer that [the Navy] expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence. <\/p>\n\n\n\n

Unfortunatelly it was quickly proved that perceptrons could not be trained to recognize many classes of patterns<\/p>\n\n\n\n

Nevertheless perceprons are the basic building block of neural networks. In short, they are algorithms for supervised learning of binary classifiers. A binary classifier is a function which can decide whether or not an input, represented by a vector of numbers, belongs to some specific class<\/p>\n\n\n\n

This video presents the perceptron, a simple model of an individual neuron, and the simplest type of neural network. The manner in which perceptrons define a linear decision boundary is shown, as well as the mechanics of the perceptron learning algorithm. <\/p>\n\n\n\n

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