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

Picking A Python IDE

Kevin Jacobs reviews a few Python IDEs from the perspective of a data scientist: Ladies and gentlemens, this is one of the most perfect IDEs for editing your Python code! At least in my opinion. Jupyter notebook is a web based code editor and can quickly generate visualizations. You can mix up code and text […]

Read More

Handling Imbalanced Data

Tom Fawcett shows us how to handle a tricky classification problem: The primary problem is that these classes are imbalanced: the red points are greatly outnumbered by the blue. Research on imbalanced classes often considers imbalanced to mean a minority class of 10% to 20%. In reality, datasets can get far more imbalanced than this. […]

Read More


September 2017
« Aug Oct »