AI GlossaryInferenceENTRY — AI Fundamentals
[AI Fundamentals]
Inference.
The process of using a trained model to make predictions on new, unseen data.
In-depth explanation
01Once a model is trained, inference is the deployment phase where the model processes new inputs and produces outputs. Inference needs to be fast and efficient, especially in production systems. Techniques like model optimization, quantization, and specialized hardware (GPUs, TPUs) help speed up inference.
Examples
02EX. 01
Getting a translation from Google Translate
EX. 02
Face detection in photos
More in AI Fundamentals
0301AccuracyAccuracy 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.02Active LearningActive 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.03Adam OptimizerAdam (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.04Adversarial AttackAn 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.05Adversarial ExampleAn adversarial example is a specially crafted input designed to deceive a machine learning model, causing it to make an incorrect prediction or classification.06Agentic AIAgentic 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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