In this post, we revisit key aspects of logistic regression and highlight its limitations, thereby motivating the introduction of more powerful algorithms.
Read More

In this post, we will explore how logistic regression can handle non-linearly separable data and take a step towards automating this process.
Read More

In this post, we explore how we can naturally derive neural networks from logistic regression and enable them to automatically handle complex data configurations.
Read More

In this post, we will explore how to train the neural network and introduce the famous backpropagation algorithm for that purpose.
Read More

In this post, we will translate theory into practice by implementing a basic neural network in C#, observing its functionality on the dataset introduced at the outset of this series.
Read More

In this concluding post, we will put into practice what was discussed earlier by implementing it in C#.
Read More

In this article, we elucidate finite automata, a fundamental data structure essential for lexical analysis.
Read More

In this post, we explore the theoretical concepts essential for a precise understanding of the underlying mechanisms of a lexical analyzer.
Read More

Commencing today, we embark on a concise series delving into lexical analysis, with a specific emphasis on SQL. The aim is to unveil the intricacies of how a query compiler within a RDBMS operates, unraveling its ability to comprehend the underlying meaning of a query and discern the user's intended objectives.
Read More

Beginning today, we are initiating a series of articles on data clustering, featuring a comparison of significant algorithms in the field (in particular K-Means and DBSCAN). Our goal is to blend theoretical concepts with practical implementation (in C#), offering clear illustrations of the associated challenges.
Read More