Deep learning and Error-driven learning

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Difference between Deep learning and Error-driven learning

Deep learning vs. Error-driven learning

Deep learning is the subset of machine learning methods based on neural networks with representation learning. Error-driven learning is a type of reinforcement learning method.

Algorithm

In mathematics and computer science, an algorithm is a finite sequence of mathematically rigorous instructions, typically used to solve a class of specific problems or to perform a computation.

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Backpropagation

In machine learning, backpropagation is a gradient estimation method used to train neural network models.

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Computer vision

Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions.

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Deep belief network

In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer.

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Deep learning

Deep learning is the subset of machine learning methods based on neural networks with representation learning.

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Learning rate

In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function.

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Machine translation

Machine translation is use of computational techniques to translate text or speech from one language to another, including the contextual, idiomatic and pragmatic nuances of both languages.

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Named-entity recognition

Named-entity recognition (NER) (also known as (named) entity identification, entity chunking, and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.

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Natural language processing

Natural language processing (NLP) is an interdisciplinary subfield of computer science and artificial intelligence.

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Overfitting

In mathematical modeling, overfitting is "the production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit to additional data or predict future observations reliably".

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Regularization (mathematics)

In mathematics, statistics, finance, and computer science, particularly in machine learning and inverse problems, regularization is a process that changes the result answer to be "simpler".

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Reservoir computing

Reservoir computing is a framework for computation derived from recurrent neural network theory that maps input signals into higher dimensional computational spaces through the dynamics of a fixed, non-linear system called a reservoir.

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Speech recognition

Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers.

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