Discussing the article: "Neural Network in Practice: Sketching a Neuron"
New article Neural network in practice: Sketching a neuron has been published:
Author: Daniel Jose
Excellent article, congratulations on the didactic approach. I'm looking forward to the next ones!
Thanks for the content.
Excellent article, congratulations on the teaching. I'm looking forward to the next ones!
Thanks for the content.
The next ones, related to neural networks, will be even better. And a lot more fun. I can guarantee that. 😁👍 Since the aim is precisely to show how neural networks work under the hood.
Excellent article, congratulations on the teaching. I'm looking forward to the next ones!
Thanks for the content.
One detail: As you may have noticed. I'm working on another profile too. Since I want to pass on as much of my knowledge as possible to everyone here in the community. But in fact, the content on neural networks is designed precisely to explain how they actually work. Thank you for your comment. And I hope you have fun with the content, just as I'm having fun showing you how it all works. 🙂 👍
Petit détail : comme vous l'avez peut-être remarqué, je travaille également sur un autre profil. Je souhaite transmettre un maximum de mes connaissances à tous les membres de la communauté. En réalité, le contenu sur les réseaux neuronaux est précisément conçu pour expliquer leur fonctionnement. Merci pour votre commentaire. J'espère que vous prendrez plaisir à le découvrir, tout comme je prends plaisir à vous montrer comment tout cela fonctionne. 🙂 👍
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Check out the new article: Neural Network in Practice: Sketching a Neuron.
In this article we will build a basic neuron. And although it looks simple, and many may consider this code completely trivial and meaningless, I want you to have fun studying this simple sketch of a neuron. Don't be afraid to modify the code, understanding it fully is the goal.
In the previous article Neural Network in Practice: Pseudoinverse (II), I discussed the importance of dedicated computational systems and the reasons behind their development. In this new article related to neural networks, we will delve deeper into the subject. Creating material for this stage is no simple task. Despite appearing straightforward, explaining something that often causes significant confusion can be quite challenging.
What will we cover at this stage? In this series, I aim to demonstrate how a neural network learns. So far, we have explored how a neural network establishes correlations between different data points. However, the methods discussed until now are only applicable when working with a pre-processed and filtered dataset. This allows the neural network to identify optimal solutions based on existing information. But what happens when the data is unfiltered? How does a neural network establish correlations in such cases? This is where many people mistakenly believe that a neural network possesses some form of intelligence. They assume it "learns" how to classify things in an autonomous, human-like manner.
This common misconception makes explaining neural networks particularly challenging. Often, those who seek to understand them lack the basic knowledge of how to sort through different types of information, even if the data has some relationship to each other. This is the most confusing point for those who do not work with it. As a result, they may misinterpret explanations, leading to further confusion about how neural networks function.
Author: Daniel Jose