Neural networks for regression - a comprehensive overview - Part 1

Starting today, we begin our journey into understanding the inner workings of neural networks for regression and how to implement them in C#.

In recent years, neural networks have gained significant popularity for their ability to solve complex problems and have become a core component of deep learning. While they can be applied to various tasks such as classification and regression, this series will focus specifically on their use in regression. We will explore how to train these models, examining both their strengths and limitations.

Neural networks have already been discussed in previous articles on this website, and we encourage readers to refer to those for a foundational understanding.

• Neural networks for regression - a comprehensive overview - Part 2
• Neural networks for regression - a comprehensive overview - Part 3
• Neural networks for regression - a comprehensive overview - Part 4
• Neural networks for regression - a comprehensive overview - Part 5
• Neural networks for regression - a comprehensive overview - Part 6
• Neural networks for regression - a comprehensive overview - Part 7

The subsequent textbooks prove useful for concluding this series.

Deep Learning (Goodfellow, Bengio, Courville)
Deep Learning: Foundations and Concepts (Bishop, Bishop)
Machine Learning: An Algorithmic Perspective (Marsland)

Without further ado and as usual, let's begin with a few prerequisites to correctly understand the underlying concepts. Continue here.