Regression refers to predictive modeling problems that involve predicting a numeric value. It is different from classification that involves predicting a class label. Unlike classification,... Read more

Activation functions are a critical part of the design of a neural network. The choice of activation function in the hidden layer will control how well the network model learns the training... Read more

Function optimization involves finding the input that results in the optimal value from an objective function. Optimization algorithms navigate the search space of input variables in order t... Read more

Gradient descent is an optimization algorithm that follows the negative gradient of an objective function in order to locate the minimum of the function. A limitation of gradient descent is... Read more

Optimization is a field of mathematics concerned with finding a good or best solution among many candidates. It is an important foundational topic required in machine learning as most machin... Read more

How to Optimize a Function with One Variable? Univariate function optimization involves finding the input to a function that results in the optimal output from an objective function. This is... Read more

Applied machine learning is typically focused on finding a single model that performs well or best on a given dataset. Effective use of the model will require appropriate preparation of the... Read more

Semi-supervised learning refers to algorithms that attempt to make use of both labeled and unlabeled training data. Semi-supervised learning algorithms are unlike supervised learning algorit... Read more

Multinomial logistic regression is an extension of logistic regression that adds native support for multi-class classification problems. Logistic regression, by default, is limited to two-cl... Read more

Semi-supervised learning refers to algorithms that attempt to make use of both labeled and unlabeled training data. Semi-supervised learning algorithms are unlike supervised learning algorit... Read more