Overflow and Underflow in Deep Learning

Introduction Deep learning algorithms generally need a high volume of numerical computation. This normally states to algorithms that solve mathematical problems. That is solved by methods to keep informed guesses of the solution through an iterative process. Somewhat than logically deriving a formula in case a symbolic expression for the correct solution. The general operations […]

Learning for Structured Prediction

Introduction Structured prediction is the main term for supervised machine learning techniques. Those techniques are involved predicting structured objects, instead of scalar discrete or real values. Structured prediction models are normally trained by means of observed data. In which the true value is used to regulate model parameters similar to usually used supervised learning techniques. […]

Deep learning for text and sequences

Introduction Deep-learning models that would process text either understood as sequences of word or sequences of characters, statistic, and sequence data generally. The two important deep-learning algorithms for sequence processing are recurrent neural networks and 1D convnets. We’ll discuss both of those approaches. Applications of those algorithms include the following: Document classification and statistic classification, […]

Hyperparameters And Validation Sets In Deep Learning

Introduction Most machine learning algorithms have several settings that we will use to regulate the behavior of the training algorithm. These settings are called hyperparameters. The values of hyperparameters aren’t adopted by the training algorithm itself (though we will design a nested learning procedure where one learning algorithm learns the simplest hyperparameters for an additional learning algorithm).  Description Within the polynomial regression example, there’s one hyperparameter: the degree of the polynomial, which acts as a […]

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