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2. Machine learning - Wikipedia

   en.wikipedia.org/wiki/Machine_learning

   e. Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data and thus perform tasks without explicit instructions. [1] Recently, artificial neural networks have been able to surpass many previous approaches in ...

3. Feature learning - Wikipedia

   en.wikipedia.org/wiki/Feature_learning

   Feature learning is intended to result in faster training or better performance in task-specific settings than if the data was inputted directly, compare transfer learning. [1] In machine learning, feature learning or representation learning [2] is a set of techniques that allows a system to automatically discover the representations needed for ...

4. Deep learning - Wikipedia

   en.wikipedia.org/wiki/Deep_learning

   Deep learning is a subset of machine learning methods based on neural networks with representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data. The adjective "deep" refers to the use of multiple layers (ranging from three to ...

5. Neural network (machine learning) - Wikipedia

   en.wikipedia.org/wiki/Neural_network_(machine...

   v. t. e. In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function of biological neural networks in animal brains. [1][2] An ANN consists of connected units or nodes called artificial neurons, which loosely model the neurons in the brain.

6. Reinforcement learning - Wikipedia

   en.wikipedia.org/wiki/Reinforcement_learning

   Machine learningand data mining. Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent ought to take actions in a dynamic environment in order to maximize the cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside ...

7. Feature (machine learning) - Wikipedia

   en.wikipedia.org/wiki/Feature_(machine_learning)

   In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. [1] Choosing informative, discriminating and independent features is a crucial element of effective algorithms in pattern recognition, classification and regression. Features are usually numeric, but structural features ...

8. Convolutional neural network - Wikipedia

   en.wikipedia.org/wiki/Convolutional_neural_network

   v. t. e. A convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [1]

9. Precision and recall - Wikipedia

   en.wikipedia.org/wiki/Precision_and_recall

   Precision and recall. In pattern recognition, information retrieval, object detection and classification (machine learning), precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space. Precision (also called positive predictive value) is the fraction of relevant instances among the ...