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AI, LLM and Neural Networks from first principles - Pt 5 - Back Propagation

Remember in our section on derivatives, we discussed how you can define the derivative as a slope at a point. Depending on the properties of this slope, you can descend down it until you reach a desired point (i.e., where the cost is zero). Since we have our cost function (which is MSE), we can find the derivative of this cost function in

AI, LLM and Neural Networks from first principles - Pt 4 - Forward Propagation

Let's imagine we want to create a machine learning algorithm that determines whether a certain property is worth investing in or not. In a real-world use case, you would gather as much labeled data as possible—that is, data on previously sold properties (including as many features as possible, such as price, location, aesthetic beauty, a

AI, LLM and Neural Networks from first principles - Pt 3 - Main Concepts and Terms

Inspired by neurons in the biological brain, scholars in the 20th century (Warren McCulloch, Walter Pitts, Frank Rosenblatt, among others) sought to replicate how the brain works to achieve computation functions. This effort has progressed into what it is today, of which key, well-established concepts now include

AI, LLMs and Neural Networks from first principles - Pt 2 - And There Was the Derivative

Before we can delve any further into machine learning / neural networks work, we need to understand the mathematical (calculus) topic of derivatives. This is a core aspect present in almost all standard neural networks and entails the magic sauce that enables neural networks to learn.

AI, LLMs and Neural Networks from first principles - Pt 1 - Introduction

Since the launch of ChatGPT 3.5, Artificial Intelligence (AI) and Large Language Models (LLMs) have irrevocably entered the developer's toolkit and everyday workflow. It's now standard to encounter or integrate AI logic into applications, making a fundamental understanding of AI and its essential skill for developers.


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