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 interest in probability is called a random variable, and the relationship between each possible outcome for a random variable and their probabilities is called a probability distribution. Probability distributions are […]

## What Is Probability?

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 like chance, luck, and risk. Probability is a field of mathematics that gives us the language and tools to quantify the uncertainty of events and reason in a principled […]

## A Gentle Introduction to Uncertainty in Machine Learning

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 data collected from the domain, and in the imperfect nature of any models developed from such data. Managing the uncertainty that is inherent in machine learning for predictive […]

## 5 Reasons to Learn Probability for Machine Learning

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 to getting started. This is misleading advice, as probability makes more sense to a practitioner once they have the context of the applied machine […]

## Resources for Getting Started With Probability in Machine Learning

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 supports machine learning. Probability is a field of mathematics concerned with quantifying uncertainty. Many aspects of machine learning are uncertain, including, most critically, observations from the problem […]

## How to Develop and Evaluate Naive Classifier Strategies Using Probability

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 dataset can be compared. There are different strategies that can be used for a naive classifier, and some are better than others, depending […]

## A Gentle Introduction to Jensen’s Inequality

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 that the mean of the scaled values is the same as the scaled value of the mean raw variable values. This makes […]

## A Gentle Introduction to Generative Adversarial Network Loss Functions

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, although one aspect that remains challenging for beginners is the topic of GAN loss functions. The main reason is that the architecture involves the simultaneous training of two […]

## How to Implement the Frechet Inception Distance (FID) for Evaluating GANs

The Frechet Inception Distance score, or FID for short, is a metric that calculates the distance between feature vectors calculated for real and generated images. The score summarizes how similar the two groups are in terms of statistics on computer vision features of the raw images calculated using the inception v3 model used for image […]

## How to Implement the Inception Score (IS) for Evaluating GANs

Generative Adversarial Networks, or GANs for short, is a deep learning neural network architecture for training a generator model for generating synthetic images. A problem with generative models is that there is no objective way to evaluate the quality of the generated images. As such, it is common to periodically generate and save images during […]