Top 20 AI Jargon
top twenty AI jargon terms with brief definitions:
1. #ArtificialIntelligence AI The simulation of human intelligence processes by machines, especially computer systems, involving tasks like learning, reasoning, and self-correction.
2. #MachineLearning (ML) A subset of AI that involves training algorithms to learn from data and improve performance over time without explicit programming.
3. #DeepLearning A type of machine learning using artificial neural networks with multiple s to analyze data and make complex decisions.
4. #NeuralNetwork A computing system inspired by the human brain that is used to recognize patterns and solve problems in machine learning.
5. Natural Language Processing #NLP The ability of AI to understand, interpret, and respond to human language.
6. #ComputerVision The field of AI that trains computers to interpret and understand the visual world.
7. #ReinforcementLearning A type of machine learning where an agent learns to make decisions by performing actions and receiving rewards or penalties.
8. #SupervisedLearning A machine learning approach where a model is trained using labeled data.
9. #UnsupervisedLearning A type of machine learning that identifies patterns in data without pre-existing labels.
10. #TransferLearning The ability of a machine learning model to apply knowledge gained from one task to a different but related task.
11. Generative Adversarial Networks #GANs A type of neural network used to generate new data by pitting two models against each other in a "game."
12. #Overfitting When a machine learning model learns the details and noise in the training data to such an extent that it negatively impacts the model’s performance on new data.
1 #Underfitting When a machine learning model is too simple to capture the underlying trend of the data.
14. #Backpropagation A method used in neural networks to minimize errors by adjusting weights based on the difference between predicted and actual outcomes.
15. #Algorithm A set of rules or instructions used by a computer to solve a problem or perform a computation.
16. #Bias Systematic errors introduced by the model due to assumptions made during the learning process, leading to unfair or inaccurate predictions.
17. #TrainingData The dataset used to train a machine learning model to understand and learn the desired patterns.
18. #Inference The process of using a trained machine learning model to make predictions on new data.
19. #Hyperparameters Settings that are used to configure machine learning models, such as learning rate and number of epochs, and are set before the training process.
20. #TuringTest A test proposed by Alan Turing to determine if a machine can exhibit behavior indistinguishable from a human.
Subsection: IT Knowledge
Section: Student Zone
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