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 Architectures
Neural network architectures are the structural designs behind deep learning systems — CNNs, RNNs, GANs, VAEs, and graph networks, each optimized for different data shapes and tasks.
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What topics does this domain cover?
6 topicsEach topic below is a key concept in this domain. Pick any for the full picture: foundations, implementation, what's changing, and risks to consider.
Convolutional Neural Network →
A Convolutional Neural Network is a deep learning architecture that applies small, learnable filters across input data …
5 articles
Generative Adversarial Network →
A generative adversarial network is a machine learning architecture composed of two neural networks — a generator and a …
4 articles
Graph Neural Network →
A graph neural network is a deep learning architecture that operates directly on graph-structured data, where …
6 articles
Neural Network Basics for LLMs →
Neural networks are computational systems that learn patterns from data by adjusting internal parameters called weights …
7 articles
Recurrent Neural Network →
A recurrent neural network is a neural network architecture that processes sequential data one step at a time, …
6 articles
Variational Autoencoder →
A Variational Autoencoder (VAE) is a generative neural network that encodes input data into a continuous, structured …
5 articles
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Updated Apr 16, 2026
Concepts covered
Adjacency Matrices, Node Features, and the Prerequisites for Understanding Graph Neural Networks
Graph neural networks consume matrices, not pixels. Learn how adjacency matrices, node features, and message passing combine — plus the math you need first.
Oversmoothing, Scalability Walls, and the Hard Technical Limits of Graph Neural Networks
Oversmoothing and neighbor explosion set hard ceilings on graph neural network depth and scale. Learn the mathematical limits behind GNN architecture decisions.
What Is a Graph Neural Network and How Message Passing Propagates Information Across Nodes
Graph neural networks learn from connections, not grids. Understand message passing, how graph convolution differs from CNNs, and the oversmoothing problem.
From Autoencoders to KL Divergence: Prerequisites and Hard Limits of Variational Autoencoders
Learn the math behind variational autoencoders — KL divergence, ELBO, the reparameterization trick — and why VAEs blur where GANs and diffusion models don't.
What Is a Variational Autoencoder and How the Reparameterization Trick Enables Generative Learning
VAEs compress data into structured probability spaces for generation. Learn how the reparameterization trick and ELBO loss enable end-to-end training.
From Latent Vectors to Adversarial Loss: The Building Blocks and Prerequisites of GAN Architecture
Understand GAN architecture from the ground up: generator, discriminator, latent space, and the adversarial loss that ties them together. Prerequisites included.
Mode Collapse, Training Instability, and the Hard Technical Limits of Generative Adversarial Networks
Mode collapse and training instability aren't GAN bugs — they're structural limits of adversarial training. Learn the mechanisms and the diffusion trade-off.
Backpropagation Through Time, Vanishing Gradients, and Why Transformers Replaced Recurrent Networks
Gradients decay exponentially in recurrent networks during backpropagation through time. Learn the math, how LSTM gates patched it, and why attention won.
From LeNet to ConvNeXt: How CNN Architectures Evolved and Where Spatial Inductive Bias Falls Short
Trace CNN evolution from LeNet to ConvNeXt. Understand how spatial inductive bias enables efficient vision but limits global context versus vision transformers.
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.
From Vanilla RNN to LSTM and GRU: How Gating Mechanisms Solved the Long-Term Memory Problem
Trace how LSTM forget, input, and output gates fix the vanishing gradient problem that crippled vanilla RNNs, and how GRU simplifies the three-gate design.
What Is a Convolutional Neural Network and How Learnable Filters Extract Visual Features
Convolutional neural networks detect visual features through learnable filters, not pixel matching. Understand the layer-by-layer mechanism from edges to objects.
What Is a Recurrent Neural Network and How Hidden States Process Sequential Data
RNNs use hidden states to carry memory across time steps. Learn how recurrent neural networks process sequences, why gradients vanish, and how LSTM fixes it.
