Starting today, we initiate a series that explores neural networks and deep learning. The objective is to uncover the complexities of utilizing neural networks to accomplish tasks that elude other algorithms.
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In this post, we revisit key aspects of logistic regression and highlight its limitations, thereby motivating the introduction of more powerful algorithms.
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In this post, we will explore how logistic regression can handle non-linearly separable data and take a step towards automating this process.
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In this post, we explore how we can naturally derive neural networks from logistic regression and enable them to automatically handle complex data configurations.
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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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In this concluding post, we will put into practice what was discussed earlier by implementing it in C#.
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In this article, we elucidate finite automata, a fundamental data structure essential for lexical analysis.
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In this post, we explore the theoretical concepts essential for a precise understanding of the underlying mechanisms of a lexical analyzer.
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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.
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