Supervised learning teaches AI models to predict outcomes using labeled data, while unsupervised learning explores unlabeled data to discover hidden patterns and insights.
Backpropagation is the neural network training process of feeding error rates back through a neural network to make it more accurate. Here’s what you need to know.
A Markov chain is a stochastic model that outlines the probability of a sequence of events occurring based on the previous event. Here’s what you need to know.
Loss functions in deep learning are used to measure how well a neural network model performs. Learn how to use the right loss function for your project.
A neural network is a series of algorithms that identifies patterns and relationships in data, similar to the way the brain operates. Here's how they work.
