1.4 The First Machine Learning Classifier
- The Perceptron — A Perceiving and Recognizing Automaton (1957) by Frank Rosenblatt
What we covered in this video lecture
In this lecture, we introduced the perceptron algorithm, a binary classification algorithm inspired by how neurons in the human brain work.
In the forward pass, the perceptron takes the input features, computes the net input, and finally applies a threshold to determine the predicted class labels. The perceptron then updates its weight and bias parameters based on whether the prediction is correct. Here, the weights and bias parameters are the model parameters learned from the training set.
We use the letter m (as a subscript) to denote the number of features (dimensions) in a given dataset. And we use the letter n (as a superscript) to indicate the number of training examples. Another piece of technical jargon we introduced in this lecture is the concept of a training epoch. A training epoch refers to one full iteration over the training set.
Additional resources if you want to learn more
If you are interested in the early perceptron algorithms and how it all began, I recommend checking one of the early papers The Perceptron — A Perceiving and Recognizing Automaton (1957) by Frank Rosenblatt.
While it is absolutely not necessary for this course or for understanding neural networks in general, it can be interesting to look at the perceptron from a geometrical perspective. If you are interested, I have a lecture video on the geometric intuition behind the perceptron here.
If you can’t get enough of the perceptron, there is even a whole book devoted to the topic: Perceptrons (1969). (Note that this book was written and published to highlight the limitations of the perceptron.)
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