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Self-supervised learning

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Self-supervised learning (SSL) is a paradigm in machine learning where a model is trained on a task using the data itself to generate supervisory signals, rather than relying on externally-provided labels. In the context of neural networks, self-supervised learning aims to leverage inherent structures or relationships within the input data to create meaningful training signals. SSL tasks are designed so that solving them requires capturing essential features or relationships in the data.

Neural network (machine learning)Neural network (machine learning)Computational model used in machine learningA neural network is an interconnected group of nodes, inspired by a simplification of neurons in a brain. Here, each blue/green circular node in the hidden and output layers represents an artificial neuron and each red circular node in the far left layer represents an input data value.Unsupervised learningUnsupervised learningUnsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data. Other frameworks in the spectrum of supervisions include weak- or semi-supervision, where a small portion of the data is tagged, and self-supervision. Some researchers consider self-supervised learning a form of unsupervised learning.Feature (machine learning)Feature (machine learning)In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. Choosing informative, discriminating, and independent features is crucial to producing effective algorithms for pattern recognition, classification, and regression tasks. Features are usually numeric, but other types such as strings and graphs are used in syntactic pattern recognition, after some pre-processing step such as one-hot encoding.AutoencoderAutoencoderNeural network that learns efficient data encoding in an unsupervised mannerA schema of an autoencoder. An autoencoder has two main parts: an encoder that maps the message to a code, and a decoder that reconstructs the message from the code.An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).Adaptive learningAdaptive learningAdaptive learning, also known as adaptive teaching, is an educational method which uses computer algorithms as well as artificial intelligence to orchestrate the interaction with the learner and deliver customized resources and learning activities to address the unique needs of each learner. In professional learning contexts, individuals may "test out" of some training to ensure they engage with novel instruction.Supervised learningSupervised learningIn machine learning, supervised learning (SL) is a type of machine learning paradigm where an algorithm learns to map input data to a specific output based on example input-output pairs. This process involves training a statistical model using labeled data, meaning each piece of input data is provided with the correct output. The term "supervised" refers to the role of a teacher or supervisor who provides this training data, guiding the algorithm towards correct predictions.Machine learningMachine 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 being explicitly programmed. Statistics and mathematical optimisation methods compose the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysis (EDA) through unsupervised learning.Concept driftConcept driftIn predictive analytics, data science, machine learning and related fields, concept drift or drift is an evolution of data that invalidates the data model. It happens when the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways. This causes problems because the predictions become less accurate as time passes.Audio signal processingAudio signal processingAudio signal processing is a subfield of signal processing that is concerned with the electronic manipulation of audio signals. Audio signals are electronic representations of sound waves—longitudinal waves which travel through air, consisting of compressions and rarefactions. As audio signals may be represented in either digital or analog format, processing may occur in either domain.FacebookFacebookSocial networking service owned by Meta PlatformsFacebook is an American social networking service owned by the American technology conglomerateMeta Platforms. It was founded in 2004 by Mark Zuckerberg, along with his Harvard College roommates and fellow students Eduardo Saverin, Andrew McCollum, Dustin Moskovitz, and Chris Hughes. The name Facebook derives from the face book directories often given to American university students.Speech recognitionSpeech recognitionAutomatic conversion of spoken language into textParameterParameterA parameter (from Ancient Greek (pará)'beside, subsidiary' and (métron)'measure'), generally, is any characteristic that can help in defining or classifying a particular system (meaning an event, project, object, situation, etc.).

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