All Cognivo articles filed under Machine Learning.
A step-by-step look at what happens during the training of a neural network, from raw data to a working model.
A confusion matrix is a simple table that reveals exactly where a classification model is making mistakes.
A tour of the most widely used machine learning algorithms and where each one is typically applied.
A conceptual look at what 'learning' really means for a machine, and how it differs from human learning.
Decision trees are one of the most intuitive machine learning models, working like a flowchart of yes-or-no questions.
Reinforcement learning trains AI systems through trial and error using rewards, similar to how habits form.
Transfer learning lets a model reuse knowledge from one task to learn a new, related task much faster.
Two of the most common problems in machine learning, and practical ways to avoid both.
Machine learning lets computers improve at a task through experience instead of explicit programming.
Gradient descent is the optimisation method that lets neural networks gradually improve during training.