Deep learning is not a single model type. Different architectures are designed to handle different types of structures in data, such as pixels in an image, tokens in a sentence, events in a sequence, nodes in a graph, and signals across multiple modalities. Choosing an appropriate model typically begins with the nature of the problem, the type of data available, and the type of output the system needs to produce.
| type | Ideal for these people | example |
|---|---|---|
| feedforward neural network | Basic prediction and classification of structured data | Multilayer Perceptron (MLP) |
| convolutional neural network | Images, videos, spatial patterns | ResNet, VGG, EfficientNet |
| Recurrent Neural Network | Sequences and time series data | RNN, LSTM, GRU |
| transformers | Linguistic, visual, vocal, and multimodal tasks | GPT, BERT, T5, Vision Transformer |
| auto encoder | Compression, noise removal, anomaly detection | Variational autoencoder, noise removal autoencoder |
| generative adversarial network | Generate realistic images, sounds, and data | GAN, Style GAN, CycleGAN |
| popularization model | Generate high-quality images, audio, and video | DDPM, stable diffusion model |
| graph neural network | Data represented as nodes and edges | social networks, molecules, recommendation graphs |
| deep reinforcement learning model | Learning behavior through rewards | DQN, AlphaGo style system |
| multimodal model | Combine text, images, audio, and video | Visual language model, image captioning system |
feedforward neural network
Feedforward neural networks move data in one direction from the input layer to the hidden layer to the output layer. They are often used for structured prediction and classification tasks where the input features are already well-defined, such as predicting churn, classifying risk, and scoring leads.
convolutional neural network
Convolutional neural networks (also known as CNNs) are designed to detect spatial patterns. They are often used for images and videos because they can learn local patterns such as edges, textures, and shapes, and combine those signals to generate higher-level representations.
Recurrent Neural Network
Recurrent neural networks process sequences by transmitting information from previous steps to subsequent steps. Long short-term memory networks and gated repetition units have been widely used in language and time-series tasks because they can model order and time dependence, but transformers have replaced them in many language and multimodal systems.
transformers
Transformers use attention mechanisms to model relationships across sequences. Rather than processing tokens strictly one at a time, it learns which parts of the input need to interact with each other. This architecture underpins many modern language models, vision language models, and other foundational models.
auto encoder
The autoencoder learns a compressed representation of the input data and reconstructs the original input from that compressed form. These are useful for dimensionality reduction, denoising, and anomaly detection, as reconstruction errors can reveal data points that differ from the normal pattern.
generative adversarial network
Generative adversarial networks (GANs) use two networks: a generator that creates synthetic outputs, and a discriminator that evaluates whether those outputs resemble real data. GANs have been used for image generation, style transfer, and synthetic data generation, but they can be difficult to train reliably.
popularization model
Diffusion models produce output by learning how to reverse the noise addition process. They are widely related to high-quality image generation and are also used for audio, video, and other generation tasks. In an enterprise environment, its value depends on use case, data rights, latency requirements, and content governance controls.
graph neural network
Graph neural network model data as nodes and edges. These are useful when the relationships between entities are as important as the entities themselves, such as customers and products in recommendation graphs, accounts and transactions in fraud networks, and molecules and bonds in drug discovery research.
deep reinforcement learning model
Deep reinforcement learning combines neural networks and reward-based learning. Rather than learning only from labeled examples, agents learn by performing actions, receiving rewards or penalties, and adjusting their behavior over time. These models are used in robotics, games, optimization, and some autonomous decision-making systems.
multimodal model
Multimodal models handle multiple data types, such as text and images, audio and video, and structured records and unstructured documents. For example, a multimodal customer support model might analyze product images, support tickets, purchase history, and troubleshooting text together, rather than treating each input as a separate system.
