Bootstrap sampling: an implementation with Python

Bootstrap methods are powerful techniques used in non-parametric statistics, that means, whenever we are provided with data drawn from an unknown distribution law. The underlying issue that bootstrap is meant to address is the well known problem of statistics: we want to collect information about a population, but we are provided only with a sample […]

Interactive Convolutional Neural Network

Image recognition is one of the main topics Deep Learning is focusing on. Indeed, the family of algorithms entitled to deal with image recognition belongs to the class of Neural Networks, typical multi-layers algorithms employed in deep learning tasks. More specifically, image recognition employs Convolutional Neural Networks (CNNs), which I’ve been explaining in my previous […]

Optimization algorithms: the Newton Method

Predictive Statistics and Machine Learning aim at building models with parameters such that the final output/prediction is as close as possible to the actual value. This implies the optimization of an objective function, which might be either minimized (like loss functions) or maximized (like Maximum Likelihood function). The idea behind optimization routine is starting from […]

Building a ML model in 3 lines of code? Yes you can

Machine Learning as a subject is not easy. It is indeed a set of tools (mainly algorithms and optimization procedures) whose comprehension involves, inevitably, a deep understanding of Maths and Stats. Nevertheless, the implementation of a ML model to a real scenario might be easier than expected. Indeed, once you got familiar with theoretical concepts, […]