Understanding Neural Networks: Perceptrons

Akash Sethi explains what a perceptron is:

In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector.
Linear classifier defined that the training data should be classified into corresponding categories i.e. if we are applying classification for the 2 categories then all the training data must be lie in these two categories.
Binary classifier defines that there should be only 2 categories for classification.
Hence, The basic Perceptron algorithm is used for binary classification and all the training example should lie in these categories. The basic unit in the Neuron is called the Perceptron.

Click through to learn more about perceptrons.

Related Posts

Reviewing The Team Data Science Process

I am starting a new series on launching a data science project, and my presentation quickly veers into a pessimistic place: The concept of “clean” data is appealing to us—I have a talk on the topic and spend more time than I’m willing to admit trying to clean up data.  But the truth is that, in a […]

Read More

Methods To Improve Model Accuracy

Tristan Robinson shows how to go back to the drawing board when your model’s accuracy isn’t cutting it: One of the reoccurring principles that appears with machine learning is that of Ockham’s razor, which states that the best models are simple models that fit the data well; this is not an irrefutable principle of logic, but […]

Read More


September 2017
« Aug Oct »