The probability for a discrete random variable can be summarized with a discrete probability distribution. Discrete probability distributions are used in machine learning, most notably in th... Read more
Last Updated on September 18, 2019 Probability can be used for more than calculating the likelihood of one event; it can summarize the likelihood of all possible outcomes. A thing of interes... Read more
Uncertainty involves making decisions with incomplete information, and this is the way we generally operate in the world. Handling uncertainty is typically described using everyday words lik... Read more
Applied machine learning requires managing uncertainty. There are many sources of uncertainty in a machine learning project, including variance in the specific data values, the sample of dat... Read more
Probability is a field of mathematics that quantifies uncertainty. It is undeniably a pillar of the field of machine learning, and many recommend it as a prerequisite subject to study prior... Read more
Machine Learning is a field of computer science concerned with developing systems that can learn from data. Like statistics and linear algebra, probability is another foundational field that... Read more
A Naive Classifier is a simple classification model that assumes little to nothing about the problem and the performance of which provides a baseline by which all other models evaluated on a... Read more
It is common in statistics and machine learning to create a linear transform or mapping of a variable. An example is a linear scaling of a feature variable. We have the natural intuition tha... Read more
Last Updated on September 3, 2019 Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural netwo... Read more
The generative adversarial network, or GAN for short, is a deep learning architecture for training a generative model for image synthesis. The GAN architecture is relatively straightforward,... Read more
