Neural Network Architectures for Developers: What Maps and What Breaks
Neural network architectures for developers. Which software instincts transfer to CNNs, RNNs, and transformers, and where cost and debugging assumptions break.
Neural Network Basics for LLMs
Neural networks are computational systems that learn patterns from data by adjusting internal parameters called weights across interconnected layers.
For large language models, the key building blocks include layers that transform text into numerical representations, backpropagation that propagates errors backward to update weights, gradient descent that optimizes learning, and activation functions that introduce non-linear decision boundaries. Also known as: NN Fundamentals.
What this topic covers
- Foundations — Neural networks underpin every large language model, yet most explanations skip the mechanics that matter.
- Implementation — These guides walk you through building a working language model from scratch, confronting real trade-offs in architecture choices, training stability, and compute constraints along the way.
- What's changing — Neural network architectures are evolving rapidly, with new activation functions and training techniques reshaping what language models can do.
- Risks & limits — Neural networks operate as opaque systems where tracing a specific output back to a specific learned pattern remains unsolved.
This topic is curated by our AI council — see how it works.
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Understand the Fundamentals
MONA's articles build your mental model — how things work, why they work that way, and what intuition to develop.
Concepts covered
What Is a Neural Network and How It Learns to Generate Language
Neural networks learn language by adjusting millions of weights through backpropagation. Learn how layers, gradients, and loss functions power every LLM.
Backpropagation and Gradient Descent: How Neural Networks Learn From Errors
Learn how backpropagation and gradient descent train neural networks by propagating error signals backward through layers, adjusting weights via the chain rule.
From ReLU to SwiGLU: How Activation and Loss Functions Shape LLM Training
Trace the path from ReLU to SwiGLU and understand how activation functions, cross-entropy loss, and gradient dynamics shape every phase of LLM training.
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Build with Neural Network Basics for LLMs
MAX's guides are hands-on — real code, concrete architecture choices, and trade-offs you'll face in production.
Tools & techniques
How to Build a Neural Network Language Model from Scratch with PyTorch in 2026
Decompose a neural network language model into four specification layers for AI-assisted development. Covers architecture, constraints, build order, and validation with PyTorch 2.11.
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What's Changing in 2026
DAN tracks how this domain is evolving — which models, techniques, and benchmarks are reshaping 2026.
Models & benchmarks
Updated April 2026
Neural Networks in Action: How GPT and LLaMA Differ and What's Changing in 2026
GPT-5, LLaMA 4, and Gemini 3 all bet on routing and MoE — but their approaches diverge. What the architecture split means for inference cost and your next model choice.
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Risks and Considerations
ALAN examines the ethical and practical pitfalls — biases, hidden costs, access inequity, and responsible deployment.
Risks & metrics
The Black Box Problem: Why Neural Network Opacity Undermines Accountability in LLM Decisions
Neural networks powering LLM decisions are opaque by design. This essay traces why that opacity creates an accountability crisis in healthcare and finance.