In this post, we will delve into the concept of probabilities, with a particular emphasis on conditional probabilities. This foundational understanding will prepare us for the next post, where we will explore n-gram language models in greater depth.
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In this post, we provide a brief overview of the Google's autocomplete feature to showcase a concrete application of the concepts we are exploring.
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Starting today, we launch a series of articles delving into the implementation of the Google's autocomplete feature (we will detail later what it is exactly). More broadly, our aim is to explore the n-gram language model, shedding light on how to predict and generate sentence endings based on their beginnings. Our goal …
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Starting today, we begin our journey into understanding the inner workings of neural networks for regression and how to implement them in C#.
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In this post, we provide a brief overview of neural networks and discuss how to train them efficiently. We assume the reader has some prior knowledge of these data structures.
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In this post, we will briefly discuss how the backpropagation technique allows us to train a neural network for regression.
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After covering the intricate details of the backpropagation algorithm, we now move on to practical application by implementing a neural network for regression in C#.
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After all this effort, we will reap the rewards of our labor by demonstrating how a neural network can effectively model different functions.
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After examining how neural networks can effectively approximate non-linear functions, we will now turn our attention to their weaknesses.
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In this final post, we will briefly focus on how neural networks can be used in a simple real-world scenario: predicting the price of a house in the real estate market.
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