Types of Neural Networks
Neural networks are a family of computational models built from learnable parameters. Their central promise is to discover useful patterns by stacking many nonlinear transformations on top of one another.
The best type for a given problem depends on how the network is structured, what kind of data it receives, and what the system is asked to learn or predict.
Feedforward Neural Networks
The most basic architecture is the feedforward neural network. Information travels from an input layer through one or more hidden layers to an output layer. Connections are often dense, meaning each input can influence each output.
The multilayer perceptron (MLP) is the classic example. It is praised for its simple design and relatively easy training process. It often performs well on tabular data and on classification or regression tasks.
- Advantages: simple structure, easy to train, good for vectors and tables.
- Disadvantages: large parameter counts, weaker performance on raw images, sequences, or graphs.
An MLP might take four inputs and produce three outputs, as in a small classification model.
Convolutional Neural Networks
Convolutional neural networks use convolutional kernels that slide across an input. This creates local connectivity and parameter sharing, which helps control the number of learned weights.
They are especially suited to data with spatial or grid-like structure, including images, video, and audio spectrograms.
A typical CNN includes:
- Convolutional layers: learn edges, textures, and local patterns.
- Pooling layers: reduce spatial dimensions and improve tolerance to small shifts.
- Fully connected layers: perform the final classification or regression.
Well-known models include LeNet, AlexNet, VGG, and ResNet. ResNet uses residual connections to ease training of deeper networks and remains a strong choice for image tasks.
Recurrent Neural Networks
Recurrent neural networks connect layers across time steps or sequence positions. A hidden state carries information forward, giving the model a kind of memory.
This makes recurrent designs useful for text, speech, and time series. Still, a standard RNN can struggle with long sequences due to vanishing or exploding gradients.
Improvements include:
- Long short-term memory (LSTM): uses input, forget, and output gates.
- Gated recurrent unit (GRU): a simpler gated design that can train faster in some cases.
A common use is feeding word representations into an LSTM and using the final hidden state for sentiment analysis.
Transformer and Attention
The Transformer relies heavily on attention mechanisms. It processes sequence elements in parallel, reducing the sequential bottleneck of older recurrent models. Positional encoding supplies information about order.
The architecture has become central to natural language processing.
- BERT uses an encoder for understanding tasks.
- GPT uses a decoder for generation tasks.
- ViT applies Transformers to image patches.
In machine translation, an encoder reads the source sentence while a decoder generates the target sentence.
Generative Neural Networks
Generative models learn a data distribution and can generate new samples.
- Autoencoder (AE): compresses and reconstructs input, useful for denoising and reduction.
- Variational autoencoder (VAE): adds a probabilistic latent space.
- Generative adversarial network (GAN): uses generator and discriminator competition.
- Diffusion model: removes noise step by step to generate high-quality samples, as in Stable Diffusion.
Graph Neural Networks
Graph neural networks handle data composed of nodes and edges. The core operation is message passing, where a node gathers information from neighbors and updates its own representation.
Variants include graph convolutional networks, graph attention networks, and GraphSAGE. Applications include social network analysis, molecular prediction, recommendations, and knowledge graph reasoning.
Other Important Types
- Radial basis function network (RBF): uses local response functions for approximation.
- Self-organizing map (SOM): unsupervised method for clustering and reduction.
- Deep belief network (DBN): stack of restricted Boltzmann machines, often for pretraining.
- Spiking neural network (SNN): event-driven, low-power model inspired by biological neurons.
Choosing a Network Type
Selection often starts with data shape and task type.
- Tabular or vector data: MLP.
- Image data: CNN or ViT.
- Sequence data: RNN, LSTM, GRU, or Transformer.
- Graph data: GNN.
- Generation tasks: VAE, GAN, or diffusion model.
- Resource-limited settings: lightweight CNN, MobileNet, or SNN.
No single network fits all tasks. Practitioners balance data scale, objectives, compute budget, and interpretability needs. Hybrid models are sometimes the most practical solution.