Explore comprehensive guides to the most powerful algorithms in AI, from classical methods to cutting-edge deep learning architectures.
Learn from labeled data to make predictions on unseen examples.
An ensemble learning method that constructs multiple decision trees and merges them for more accurate predictions.
Models the relationship between variables using a linear equation to predict continuous outcomes.
A statistical model that uses a logistic function to model binary or multiclass classification problems.
Finds the optimal hyperplane that maximally separates different classes in high-dimensional space.
A probabilistic classifier based on Bayes' theorem with strong independence assumptions between features.
Classifies data points based on the classes of their k nearest neighbors in the feature space.
Discover patterns and structures in unlabeled data.
Partitions data into K distinct clusters based on feature similarity, minimizing within-cluster variance.
Builds a tree of clusters by iteratively merging or splitting them based on distance metrics.
Reduces dimensionality by transforming data into a set of linearly uncorrelated principal components.
A non-linear dimensionality reduction technique particularly well-suited for visualizing high-dimensional data.
Density-based clustering that groups together points with many nearby neighbors and marks outliers.
Neural networks that learn efficient representations by compressing input data and reconstructing it.
Advanced neural network architectures for complex pattern recognition.
Specialized for processing grid-like data such as images, using convolutional layers to detect features.
Designed for sequential data with feedback connections, maintaining memory of previous inputs.
Advanced RNN architecture with gating mechanisms to learn long-term dependencies effectively.
Revolutionary architecture using self-attention mechanisms, powering GPT, BERT, and modern LLMs.
Two networks compete: a generator creates data while a discriminator evaluates its authenticity.
Very deep networks with skip connections that allow gradients to flow directly through the network.
Probabilistic generative models that learn latent representations with statistical properties.
Efficient CNN architecture designed for mobile and embedded vision applications using depthwise separable convolutions.
Learn optimal actions through interaction with an environment.
Model-free algorithm that learns action-value functions to determine optimal policies.
Combines Q-learning with deep neural networks to handle high-dimensional state spaces.
Directly optimizes the policy by following the gradient of expected rewards.
Combines value-based and policy-based methods using separate actor and critic networks.
State-of-the-art policy gradient method with clipped objective for stable and efficient learning.
Planning algorithm that builds a search tree through simulation and backpropagation of results.