Understanding neural network architectures becomes much clearer when you can see the data flow. This post walks through the major families of deep learning architectures using diagrams, with practical notes on when to use each one.
1. The Feedforward Network (MLP)
The most fundamental architecture. Data flows in one direction — input → hidden layers → output. No cycles, no memory.
When to use: Tabular data, regression, simple classification. MLPs struggle with spatial structure (images) or sequential data (text) because they treat every input feature independently.
2. Convolutional Neural Network (CNN)
CNNs exploit spatial locality — nearby pixels are correlated. A kernel slides over the image, sharing weights across positions. This dramatically reduces parameters compared to a fully connected approach.
Feature hierarchy: Early layers detect edges → mid layers detect textures → deep layers detect semantic objects. This hierarchical feature extraction is why CNNs work so well on images.
3. Recurrent Neural Network (RNN) & LSTM
For sequential data, we need memory. RNNs pass a hidden state hₜ from one time step to the next.
The classic RNN suffers from vanishing gradients over long sequences. The LSTM solves this with three gates:
4. The Transformer Architecture
Transformers replaced RNNs as the dominant architecture for sequence tasks. The key innovation is self-attention — every token attends to every other token in parallel.
Attention formula: Attention(Q, K, V) = softmax(QKᵀ / √dₖ) · V
The √dₖ scaling prevents dot products from growing too large in high dimensions, which would push softmax into saturation.
5. YOLO Detection Pipeline
For real-time object detection, YOLO (You Only Look Once) processes the entire image in a single forward pass — no region proposal step.
The multi-scale head is critical: P3 detects small objects, P4 handles medium, P5 catches large objects. The FPN (top-down) brings semantic richness to small feature maps, while PAN (bottom-up) brings spatial precision to large feature maps.
Architecture Comparison
Choosing the Right Architecture
The right architecture depends on your data modality, compute budget, and latency requirements. For most research prototypes, start with a pretrained backbone and fine-tune — training from scratch is rarely necessary.