Understand the magic behind the machines. A guide to the models and libraries powering Dalil AI.
Perfect for structured data (tables, spreadsheets). The foundation of data science.
The simplest way to predict a number. It finds the "line of best fit" through your data points. Used for predicting house prices, sales, or temperature.
Despite the name, it's for classification. Predicts probabilities (0 to 1). Great for "Yes/No" questions like "Is this email spam?" or "Will this customer churn?"
A "forest" of Decision Trees. Each tree votes on the outcome, and the majority wins. Highly accurate and resistant to overfitting.
Finds the optimal boundary (hyperplane) that strictly separates different classes of data. Powerful for clean, high-dimensional datasets.
Mimicking the human brain to solve complex perceptual tasks.
The foundation of Deep Learning. Layers of connected mathematical "neurons" that pass signals forward. Good for simple patterns but struggles with images or time.
Designed for eyes. scans images using "filters" to detect edges, shapes, and textures. The technology behind Face ID, self-driving cars, and medical imaging.
Has "memory". Unlike ANN, it remembers previous inputs. Essential for data that comes in a sequence, like text, audio, or stock prices.
A supercharged RNN. It can remember things for long periods and "forget" irrelevant info. This is the brain behind Siri, Google Translate, and autocorrect.
Dalil AI wraps the world's best open-source libraries into one easy interface.