Deep Learning
A subset of machine learning using artificial neural networks with multiple layers to model complex patterns.
In-depth explanation
Deep learning uses neural networks with many hidden layers (hence "deep") to progressively extract higher-level features from raw input. For example, in image recognition, lower layers might identify edges, while deeper layers identify complex shapes and objects. Deep learning has revolutionized computer vision, NLP, and many other fields.
Examples
Related terms
More in AI Fundamentals
Accuracy
Accuracy is a metric used in machine learning to measure the percentage of correctly predicted instances in relation to the total number of instances evaluated. It is widely used to assess the performance of classification models.
Active Learning
Active learning is a machine learning approach where the algorithm selectively queries a human expert to label new data points with the goal of improving the model's performance with minimal labeled data.
Adam Optimizer
Adam (Adaptive Moment Estimation) is an optimization algorithm used in training machine learning models, particularly neural networks. It combines the advantages of two other extensions of stochastic gradient descent, specifically AdaGrad and RMSProp, to adaptively adjust the learning rate of each parameter.
Adversarial Attack
An adversarial attack is a deliberate attempt to manipulate the inputs to an AI model in order to cause it to make errors or incorrect predictions, often by introducing subtle perturbations that are imperceptible to humans.
Adversarial Example
An adversarial example is a specially crafted input designed to deceive a machine learning model, causing it to make an incorrect prediction or classification.
Agentic AI
Agentic AI refers to artificial intelligence systems designed to perceive their environment, make decisions, and take actions autonomously to achieve specific goals.
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