In this post, we will explore how to train the neural network and introduce the famous backpropagation algorithm for that purpose.
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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.
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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.
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In this post, we offer a concise overview of clustering, delve into the accompanying challenges, and introduce various state-of-the-art algorithms.
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Before delving into the C# implementation of the three algorithms, we will briefly outline the logical structure of the Visual Studio project in this post.
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In this post, we will witness the most widely used clustering algorithm in action and assess its sensitivity to outliers and irregular shapes.
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In this post, we continue our exploration by examining the characteristics of hierarchical clustering.
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In this concluding post, we will explore the DBSCAN algorithm and demonstrate how it can be employed to alleviate the limitations of both K-Means and hierarchical clustering.
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In this final post, we will apply in practice the concepts we've discussed by implementing logistic regression using the ML.NET framework.
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In this post, we will outline the process of training the logistic regression algorithm.
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