Making programmers awesome at machine learning

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Tweet Share Share Activation functions are a critical part of the design of a neural network. The choice of activation function in the hidden layer will control how well the network model learns the training dataset. The choice of activation function in the output layer will define the type of predictions...

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Tweet Share Share Function optimization involves finding the input that results in the optimal value from an objective function. Optimization algorithms navigate the search space of input variables in order to locate the optima, and both the shape of the objective function and behavior of the algorithm...

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Tweet Share Share Last Updated on January 16, 2021 Gradient descent is an optimization algorithm that follows the negative gradient of an objective function in order to locate the minimum of the function. A limitation of gradient descent is that a single step size (learning rate) is used for all input...

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Tweet Share Share Optimization is a field of mathematics concerned with finding a good or best solution among many candidates. It is an important foundational topic required in machine learning as most machine learning algorithms are fit on historical data using an optimization algorithm. Additionally,...

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Tweet Share Share How to Optimize a Function with One Variable? Univariate function optimization involves finding the input to a function that results in the optimal output from an objective function. This is a common procedure in machine learning when fitting a model with one parameter or tuning...

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Tweet Share Share Applied machine learning is typically focused on finding a single model that performs well or best on a given dataset. Effective use of the model will require appropriate preparation of the input data and hyperparameter tuning of the model. Collectively, the linear sequence of steps...

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Tweet Share Share Semi-supervised learning refers to algorithms that attempt to make use of both labeled and unlabeled training data. Semi-supervised learning algorithms are unlike supervised learning algorithms that are only able to learn from labeled training data. A popular approach to semi-supervised...

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Tweet Share Share Multinomial logistic regression is an extension of logistic regression that adds native support for multi-class classification problems. Logistic regression, by default, is limited to two-class classification problems. Some extensions like one-vs-rest can allow logistic regression...

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Tweet Share Share Semi-supervised learning refers to algorithms that attempt to make use of both labeled and unlabeled training data. Semi-supervised learning algorithms are unlike supervised learning algorithms that are only able to learn from labeled training data. A popular approach to semi-supervised...

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Tweet Share Share Gradient boosting is an ensemble of decision trees algorithms. It may be one of the most popular techniques for structured (tabular) classification and regression predictive modeling problems given that it performs so well across a wide range of datasets in practice. A major problem...

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