What are the different types of neural networks?

Neural networks are computational models inspired by the human brain, designed to recognize patterns and solve problems. The most common types include feedforward neural networks (FNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). FNNs are the simplest type, where data flows in one direction from input to output layers, with no cycles or loops. They are often used for tasks like classification or regression, such as predicting house prices based on features like size and location. CNNs specialize in processing grid-like data, such as images, using convolutional layers to detect spatial patterns. For example, a CNN might identify edges or textures in a photo to classify it as a cat or dog. RNNs handle sequential data by maintaining a “memory” of previous inputs through loops, making them suitable for tasks like time-series forecasting or natural language processing (NLP).

Beyond these foundational types, specialized architectures address specific challenges. Long Short-Term Memory networks (LSTMs) and Gated Recurrent Units (GRUs) are RNN variants designed to handle long-term dependencies in sequences. LSTMs use memory cells and gates to control information flow, which helps avoid the vanishing gradient problem in standard RNNs. For instance, LSTMs are used in speech recognition systems to process audio signals over time. Transformers, another advanced architecture, rely on self-attention mechanisms to weigh the importance of different parts of input data. This makes them highly effective for NLP tasks like translation or text generation, as seen in models like BERT or GPT. Unlike RNNs, transformers process entire sequences in parallel, improving training efficiency for large datasets.

Other notable architectures include autoencoders, generative adversarial networks (GANs), and graph neural networks (GNNs). Autoencoders compress input data into a lower-dimensional representation and reconstruct it, useful for tasks like anomaly detection or image denoising. GANs consist of two networks—a generator and a discriminator—that compete to create realistic synthetic data, such as generating photorealistic images from random noise. GNNs operate on graph-structured data, enabling applications like social network analysis or molecular property prediction. For example, a GNN could predict the toxicity of a chemical compound by analyzing its molecular structure. Each architecture addresses specific data types or problem domains, allowing developers to choose the right tool based on the task’s requirements, such as data structure, computational resources, and desired outcomes.