Stochastic optimization refers to the use of randomness in the objective function or in the optimization algorithm. Challenging optimization algorithms, such as high-dimensional nonlinear objective problems, may contain multiple local optima in which deterministic optimization algorithms may get stuck. Stochastic optimization algorithms provide an alternative approach that permits less optimal local decisions to be made […]
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How to Implement Bayesian Optimization from Scratch in Python
In this tutorial, you will discover how to implement the Bayesian Optimization algorithm for complex optimization problems. Global optimization is a challenging problem of finding an input that results in the minimum or maximum cost of a given objective function. Typically, the form of the objective function is complex and intractable to analyze and is […]
3 Levels of Deep Learning Competence
Deep learning is not a magic bullet, but the techniques have shown to be highly effective in a large number of very challenging problem domains. This means that there is a ton of demand by businesses for effective deep learning practitioners. The problem is, how can the average business differentiate between good and bad practitioners? […]
LSTMs for Human Activity Recognition Time Series Classification
Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements. Classical approaches to the problem involve hand crafting features from the time series data based on fixed-sized windows and training machine learning models, such as ensembles of decision trees. The difficulty is […]
1D Convolutional Neural Network Models for Human Activity Recognition
Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements. Classical approaches to the problem involve hand crafting features from the time series data based on fixed-sized windows and training machine learning models, such as ensembles of decision trees. The difficulty is […]
How to Develop a Framework to Spot-Check Machine Learning Algorithms in Python
Spot-checking algorithms is a technique in applied machine learning designed to quickly and objectively provide a first set of results on a new predictive modeling problem. Unlike grid searching and other types of algorithm tuning that seek the optimal algorithm or optimal configuration for an algorithm, spot-checking is intended to evaluate a diverse set of […]
Indoor Movement Time Series Classification with Machine Learning Algorithms
Indoor movement prediction involves using wireless sensor strength data to predict the location and motion of subjects within a building. It is a challenging problem as there is no direct analytical model to translate the variable length traces of signal strength data from multiple sensors into user behavior. The ‘indoor user movement‘ dataset is a […]
10 Examples of How to Use Statistical Methods in a Machine Learning Project
Statistics and machine learning are two very closely related fields. In fact, the line between the two can be very fuzzy at times. Nevertheless, there are methods that clearly belong to the field of statistics that are not only useful, but invaluable when working on a machine learning project. It would be fair to say […]
A Gentle Introduction to the Chi-Squared Test for Machine Learning
A common problem in applied machine learning is determining whether input features are relevant to the outcome to be predicted. This is the problem of feature selection. In the case of classification problems where input variables are also categorical, we can use statistical tests to determine whether the output variable is dependent or independent of […]
How to Calculate the 5-Number Summary for Your Data in Python
Data summarization provides a convenient way to describe all of the values in a data sample with just a few statistical values. The mean and standard deviation are used to summarize data with a Gaussian distribution, but may not be meaningful, or could even be misleading, if your data sample has a non-Gaussian distribution. In […]