AI Dictionary A-Z

The simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, problem-solving, perception, and language understanding. AI encompasses various approaches including machine learning, deep learning, natural language processing, and computer vision.

A supervised learning algorithm used for training neural networks. It calculates the gradient of the loss function with respect to each weight by propagating errors backwards through the network, enabling efficient optimization of network parameters.

A class of deep neural networks most commonly applied to analyzing visual imagery. CNNs use convolution operations to automatically learn spatial hierarchies of features from input images, making them highly effective for tasks like image classification, object detection, and segmentation.

A subset of machine learning involving neural networks with multiple layers (deep neural networks). These networks can learn hierarchical representations of data, automatically discovering intricate structures in large datasets. Applications include image recognition, speech processing, and natural language understanding.

A subset of AI focused on building systems that learn from data and improve their performance over time without being explicitly programmed. ML algorithms identify patterns in data and make predictions or decisions based on those patterns. Major categories include supervised learning, unsupervised learning, and reinforcement learning.

The branch of AI concerned with enabling computers to understand, interpret, and generate human language. NLP combines computational linguistics with machine learning and deep learning to process text and speech data for applications like translation, sentiment analysis, chatbots, and text summarization.

A type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize cumulative reward. The agent receives feedback through rewards or penalties and adjusts its strategy accordingly. Applications include game playing, robotics, and autonomous systems.

A machine learning paradigm where the algorithm learns from labeled training data, with each example consisting of input features and the correct output. The model learns to map inputs to outputs and can then make predictions on new, unseen data. Common tasks include classification and regression.

A machine learning technique where a model developed for one task is reused as the starting point for a model on a second task. This approach leverages knowledge gained from solving one problem to solve a related problem, often requiring less training data and computational resources.

A machine learning approach where the algorithm learns patterns from unlabeled data without explicit guidance on what to predict. The system tries to find hidden structures or relationships in the data. Common techniques include clustering, dimensionality reduction, and anomaly detection.