In this post, we will explore what a retry strategy entails and delve into the implementation of such a strategy using Polly.
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In this post, we will examine the concept of a circuit breaker, understand its utility, and explore how to implement it using Polly.
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In this brief concluding post, we will explore how it is possible to concurrently combine multiple strategies, thereby constructing highly resilient .NET applications.
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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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