Neural Network Architecture Basics: When to Go Deep
Deep learning gets a lot of attention, but it isn't automatically the right tool. This resource is less about the maths of neural networks and more about a practical question: when does reaching for one actually make sense, and which architecture fits which kind of data?
When classical machine learning is usually the better choice: structured/tabular data (spreadsheet-like rows and columns), small-to-medium datasets, and situations where interpretability matters. Gradient boosting and other classical methods (see Ensemble Methods) very often match or beat neural networks on this kind of data, train much faster, and are far easier to debug.
When deep learning tends to earn its complexity: unstructured data — images, audio, video, and text — where the raw input doesn't come in neat, pre-defined features, and where large amounts of data are available (or a strong pretrained model can be adapted instead of training from scratch).
A rough map of common architectures to data types:
- Feedforward / fully connected networks (MLPs) — the simplest neural network, a reasonable starting point for structured data if you specifically want a neural approach, though classical methods often still win here.
- Convolutional Neural Networks (CNNs) — built around detecting local, position-invariant patterns, which makes them a natural fit for images (and other grid-like data) — edges and textures combine into shapes, shapes into objects.
- Recurrent Neural Networks (RNNs, LSTMs, GRUs) — designed for sequential data where order matters (time series, older approaches to text), processing one step at a time while carrying forward some memory of what came before. Largely superseded by transformers for most text tasks, but still relevant for some time-series and streaming applications.
- Transformers — the current standard for most NLP tasks and increasingly used for vision and other domains too. Process an entire sequence at once using an attention mechanism that lets every position weigh the relevance of every other position, which handles long-range dependencies far better than RNNs and parallelises much better during training.
Practical starting advice:
- Start with a simple baseline before reaching for a deep architecture. If a simple model already does well, a neural network may not be worth its added complexity and training cost.
- For most practical projects, using a pretrained model via transfer learning is far more realistic than designing and training a novel architecture from scratch.
- Bigger and deeper isn't automatically better — more layers and parameters increase the risk of overfitting (see Bias-Variance Tradeoff) unless you have the data and regularization to back it up.
Where to go deeper: the free online book Dive into Deep Learning covers CNNs, RNNs, and transformers with both intuition and runnable code, and is a solid next step once you're ready to go past the basics covered here.