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Dataset augmentation for Deep Learning

Dataset augmentation for Deep Learning

Introduction Dataset augmentation for Deep Learning is the finest way to create a machine learning algorithm. The act of maximum Machine Learning models is influenced by the quantity and diversity of data. Most companies use data augmentation to decrease dependency on training data preparation. Data augmentation is a method for making data for machine learning … Read more

Ensemble methods in Deep Learning

Ensemble methods in Deep Learning

Introduction Ensemble methods in Deep Learning associate the output of machine learning models in various stimulating means. We were unmindful of the power of ensemble methods after years of working on machine learning projects. Because this topic is typically ignored or only given a short-lived outline in utmost machine learning courses and books. By testing … Read more

Overflow and Underflow in Deep Learning

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 … Read more

Importance of PyTorch to Develop Deep Learning Models

Importance of PyTorch to Develop Deep Learning Models

Introduction PyTorch maybe a library for Python programs PyTorch to Develop Deep Learning Models that facilitate building deep learning projects. It highlights the flexibility and permits deep learning models to be expressed in idiomatic Python. This approachability and simple use found early adopters within the research community, and within the years since its first release, … Read more

Learning for Structured Prediction

Structure 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. … Read more

Deep learning for text and sequences

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, … Read more

Hyperparameters And Validation Sets In Deep Learning

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 … Read more