This post develops a Python class to visualize what happens inside a feed-forward neural network when the input is the full 2D space.

This post shows what happens inside a trained feed-forward NN when the input is the full 2D space.

This post shows what happens inside different types of feed-forward neural network for a multi-class classification problem.

This post shows what happens inside different types of feed-forward neural network for a regression problem.

This post develops a Python class to visualize what happens inside a feed-forward neural network.

This post shows what happens inside a trained feed-forward NN.

This post looks for the best set of hyperparameters and compares the computational effort of each of the three machine-learning libraries.

This post analyzes the hyperparameter (HP) space for a regression problem in Keras.

This post analyzes the hyperparameter (HP) space for a multi-class classification problem in Keras.

This post explores the *meta-learning* concept and shows how to achieve this goal with our internally-developed Python class.

This post shows how to train a neural network on some basic examples with Pytorch.

This post introduces the key components and modules of Pytorch, which are going to be applied to a neural network.

This post shows how to train a neural network on some basic examples with Tensorflow.

This post shows how to train a neural network on some basic examples with Keras.

This post shows how to train a neural network on some basic examples with Scikit-learn.

This post shows how to create a dataset for three different basic applications, regression, binary- and multi-classification, which a fully-connected neural network needs to learn.

This post gives some geometric insight into what occurs in a fully-connected neural network.

This post gives some geometric insight into what occurs in a single neuron of a FCNN.

This post wants to be a cheat sheet for fully-connected neural networks. It should be good either for a fresher to start and for a practitioner to quickly review.

This post shows how to identify the polygon vertexes, post-process the intersection points, define a colour scheme, implement the global function to draw a spirograph pattern in a rectangle with Python. Some drawings will be shown at the end of the post.

This post defines the grid definition and the parametric equations required to draw a spirograph pattern in a rectangle with Python.

This post defines the polygon drawing, different colour schemes, implements the global function to draw a spirograph pattern in a circle with Python. Some drawings will be shown at the end of the post.

This post implements a vectorized polygon vertex detection and shows how to postprocess intersection points to create a spirograph pattern in a circle with Python.

This post shows how to define a line through two points and the intersection point of two lines.

This post defines how to create a spirograph pattern in a circle with Python and shows the required visualization basics.

This post develops a Python code to identify the highest employee wages in a company and assesses each method's computational performances.

This post develops a Python code from scratch to identify the highest employee wages in a company.

This post develops a Python code from scratch to process text. How to extract initials from a name string.

This post develops a Python code from scratch to determine the travelled distance of a bike rider in a given time span, if the annual target keeps increasing.

This post shows how to develop some Python code from scratch to determine how many kilometers a bike rider will cover in a given time span.

This post analyses the effects of regularization on a non-linear binary classification problem.

This post analyses the effects of regularization on a non-linear binary classification problem.

This post analyses the effects of regularization on a linear binary classification problem.

This post analyses the effects of regularization on a linear binary classification problem.

This post implements the Ridge regression in Python from scratch. It is applied to a low-degree and high-degree model, compared to a non-regularized model and it is optimized on the validation set.

This post goes through the hyperparameter optimization for model selection, via cross-validation, and the regularization technique. The former topic is more recently referred to as **meta-learning**.

This post aims at visualizing the bias-variance dilemma, understanding how the model capacity relates to its performance and why it is common practice to split the dataset into training and testing, creating some learning curves that should clarify whether gathering additional data might be worthy.

This post defines the bias and variance concepts and apply it to both a linear and a non-linear model.

This post implements a logistic regression in Scikit-learn for the MNIST dataset.

This post implements a logistic regression in Scikit-learn for the digits dataset.

This post implements a multinomial logistic regression with categorical inputs in Scikit-learn.

This post implements a multinomial logistic regression with Scikit-learn.

This post implements a multivariate logistic regression algorithm with non-linear predictors with Scikit-learn.

This post implements a logistic regression algorithm in Scikit-learn and TensorFlow.

This post implements a logistic regression algorithm from scratch with Python and Numpy, using gradient descent and Iteratively reweighted least squares (IRLS) methods.

This post introduces the logistic regression theory, how to define a probabilistic model of a classification problem and the cross-entropy loss via maximum likelihood.

This post explores feature scaling, polynomial features and hypothesis evaluation for linear regression in Scikit-Learn.

This post implements a linear regression algorithm in Scikit-Learn and Tensorflow.

This post applies the linear regression theory to a multi-input linear case from scratch in Numpy.

This post applies the linear regression theory to a single-input case from scratch in Numpy.

This post introduces the linear regression as the "hello-world" machine learning problem.