A computer program is said to learn from experience $E$ with respect to some class of tasks $T$ and performance measure $P$, if its performance at tasks in $T$, as measured by $P$, improves with experience $E$.
ML deals with intractable computational problems by developing models that learn patterns from data and make predictions or decisions without explicit programming.
Formalisms for Artificial General Intelligence (AGI) aim to provide theoretical frameworks and models that can describe or guide the development of systems with general intelligence comparable to or surpassing human intelligence.
Foundation models are large-scale machine learning models trained on extensive and diverse datasets, designed to serve as the basis for a wide range of downstream tasks by leveraging transfer learning.
A computation graph is a directed acyclic graph (DAG) where nodes represent operations or variables and edges represent the dependencies between these operations, used to model and evaluate complex mathematical expressions or algorithms, particularly in machine learning frameworks.
Parametric Model: A parametric model is a mathematical model that contains a fixed set of parameters, which are numerical values that determine the model's behavior. These parameters are usually learned from data through a process called parameter estimation. Once the parameters are determined, the model can make predictions or generate new data.
Non-Parametric Model: Decision trees, k-nearest neighbors, and support vector machines, …
A machine learning model is a formal representation of a machine along with a set of algorithms designed to train the machine by learning patterns from data to make predictions or decisions.
A prediction problem involves using historical data to forecast or estimate unknown future outcomes or values.
A training technique in machine learning refers to the method or approach used to optimize a model's performance by learning from data.
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Representation learning is a type of machine learning that enables models to automatically discover and learn useful features or representations from raw data, facilitating the performance of various tasks such as classification, regression, and clustering.
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