The Math Behind Neural Networks, Part 0 — Where Deep Learning Comes From

2026-07-26 · Source: Deep Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Novice, long

Summary

The Math Behind Neural Networks, Part 0" introduces the core concepts of deep learning, focusing on feedforward neural networks, also known as multilayer perceptrons (MLPs). This foundational model aims to approximate complex, unwritten rules by learning from examples, expressed as y = f(x; θ). The article explains that a neural network is structured as a chain of layers, defining its "depth," with each layer comprising "units" that determine its "width." These units process weighted inputs, add a bias, and apply a nonlinear activation function. Crucially, the model's adjustable parameters (θ), including weights and biases, are learned during training. Hidden layers are highlighted as the mechanism for the model to autonomously discover and learn features (φ(x; θ)), enabling it to capture nonlinear relationships beyond simple linear models.

Key takeaway

For AI students or Machine Learning Engineers beginning their deep learning journey, this conceptual overview is essential. It demystifies core vocabulary like layers, weights, and hidden layers, explaining their purpose and origin before mathematical details. Use this foundational understanding to build a robust mental model of feedforward neural networks, ensuring you approach subsequent technical explanations of training algorithms like backpropagation with a clear grasp of the underlying architecture and its function.

Key insights

A neural network is a machine that learns its own features to approximate complex, unwritten rules.

Principles

• A neural network learns its own features.
• "Deep" in deep learning means many layers.
• Hidden layers learn nonlinear input representations.

Topics

• Neural Networks
• Deep Learning Architecture
• Feedforward Networks
• Multilayer Perceptrons
• Feature Learning

Best for: AI Student, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Deep Learning on Medium.