This is Part 2 of the Neural Network Architecture series. We’ll build from foundational concepts to modern innovations like DeepSeek’s mHC architecture.

Introduction

Hello, dear friends! Today I want to take you on a journey through one of the most important concepts in modern AI: neural network architecture.

When people talk about AI breakthroughs, they often focus on training techniques, datasets, or compute power. But there’s another dimension that’s equally crucial: how we structure the network itself. The architecture—how layers connect, how information flows, how gradients propagate—determines what’s possible to build.

This post is the foundation for understanding modern architectural innovations. Before we can appreciate recent work like DeepSeek’s mHC architecture or other cutting-edge designs, we need to understand the building blocks they’re built upon.

I’ll assume you have basic Python knowledge and have seen neural networks before (if not, you might want to start with my earlier post on artificial neural networks). What we’re going to explore today is deeper: not just “what is a neural network?” but “how do we structure them effectively?”

By the end of this post, you’ll understand:

• Why network depth matters and what problems it creates
• How residual connections revolutionised deep learning
• Why researchers continue innovating on these foundations
• The mathematical intuition behind these architectural choices

Let’s begin.

Part 1: The Fundamental Challenge of Depth

Why We Want Deep Networks

Neural networks transform data through layers of computation. Each layer learns to recognize increasingly abstract patterns:

• Layer 1: Simple edges and textures (in images) or basic word patterns (in text)
• Layer 2: Combinations of edges forming simple shapes or phrase patterns
• Layer 3: Object parts or semantic relationships
• Layer 4+: Complete objects, concepts, or complex reasoning

The intuition is clear: more layers = more abstract reasoning = more powerful models. And empirically, this holds true—when we can successfully train them.

The vanishing-gradient problem, the ResNet revolution and practical takeaways follow below.

Part 2: The ResNet Revolution

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Part 3: Building a Complete ResNet

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Part 4: The Impact and Legacy of Residual Connections

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Part 5: Limitations and Ongoing Research

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Part 6: Practical Takeaways for Building Neural Networks

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Closing Thoughts

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