AI Basics: What is behind a Feedforward Neural Network

Author(s): Caspar Bannink

Originally published on Towards AI.

Welcome to part 3 of the AI basics series. In this post, we will take a look at the feedforward neural network.

Image 1) Credits to author (AI-assisted)

In the ever-evolving world of artificial intelligence, model architecture changes rapidly. The first mainstream model was the feedforward neural network (FNN), which propelled the field of AI into the public’s perception. The performance achieved by these earlier models proved to the wider public that AI was not just an academic concept but had actual real-world utility. To this day, FNNs are still relevant and utilized in even the most cutting-edge architectures, like transformers, used by all current Large Language Models like the GPTs. Besides this, intricate knowledge of the FNN architecture is crucial for understanding the more advanced concepts like Convolutional or Recurrent neural networks.

This article will take a deep dive into FNNs, exploring the architecture and the hyperparameters that guide their behavior. We’ll discuss the training process, and finally, we code our own neural network using the Keras library.

Going forward, I assume that you already understand the artificial neuron. If this is not the case or you want to freshen up your knowledge on this topic, click on the article below.

How does the… Read the full blog for free on Medium.

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Published via Towards AI