Neural networks and deep learning Definitions

  • A

    augmented memory

    Augmented memory is the practice of artificially increasing a person's ability to produce long term memories and retain information.

  • automated storytelling

    Automated storytelling is a process involving the use of artificial intelligence (AI) to create written stories.

  • What is artificial intelligence?

    Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.

  • C

    cognitive modeling

    Cognitive modeling is an area of computer science that deals with simulating human problem-solving and mental processing in a computerized model.

  • complex adaptive system (CAS)

    Complex adaptive system is a term used by DevOps teams to describe an IT platform or project composed of multiple components that interact in ways that cannot be predicted or controlled with complete accuracy.

  • D

    deconvolutional networks (deconvolutional neural networks)

    Deconvolutional networks are convolutional neural networks (CNN) that work in a reversed process.

  • deep learning

    Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge.

  • deep learning agent

    A deep learning agent is any autonomous or semi-autonomous AI-driven system that uses deep learning to perform and improve at its tasks.

  • dropout

    Dropout refers to data, or noise, that's intentionally dropped from a neural network to improve processing and time to results.

  • I

    image recognition

    Image recognition, in the context of machine vision, is the ability of software to identify objects, places, people, writing and actions in images.

  • K

    knowledge engineering

    Knowledge engineering is a field of artificial intelligence (AI) that tries to emulate the judgment and behavior of a human expert in a given field.

  • M

    machine learning

    Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

  • N

    neuromorphic computing

    Neuromorphic computing is a method of computer engineering in which elements of a computer are modeled after systems in the human brain and nervous system.

  • What is a neural network? Explanation and examples

    In information technology, an artificial neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain.

  • P

    predictive modeling

    Predictive modeling, also called predictive analytics, is a mathematical process that seeks to predict future events or outcomes by analyzing patterns that are likely to forecast future results.

  • R

    reinforcement learning

    Reinforcement learning is a machine learning training method based on rewarding desired behaviors and/or punishing undesired ones.

  • S

    self-driving car (autonomous car or driverless car)

    A self-driving car (sometimes called an autonomous car or driverless car) is a vehicle that uses a combination of sensors, cameras, radar and artificial intelligence (AI) to travel between destinations without a human operator.

  • supervised learning

    Supervised learning is an approach to creating artificial intelligence (AI), where a computer algorithm is trained on input data that has been labeled for a particular output.

  • T

    Turing Test

    A Turing Test is a method of inquiry in artificial intelligence (AI) for determining whether or not a computer is capable of thinking like a human being.

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