What are the main types of neural networks?

Neural networks can be categorized into several key types based on their architecture and use cases. The three most common types are feedforward neural networks (FNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). FNNs are the simplest form, where data flows in one direction from input to output layers without cycles. They are often used for tasks like regression or classification, such as predicting housing prices or recognizing handwritten digits. CNNs specialize in processing grid-like data, such as images, using convolutional layers to detect spatial patterns. RNNs handle sequential data by maintaining internal memory, making them suitable for tasks like time-series prediction or natural language processing (NLP).

Beyond these, there are specialized architectures like autoencoders, generative adversarial networks (GANs), and transformers. 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 competing networks—a generator and a discriminator—that learn to create realistic synthetic data, such as generating fake images or enhancing low-resolution photos. Transformers, popularized in NLP, use self-attention mechanisms to process sequences in parallel, enabling models like BERT or GPT to handle long-range dependencies in text. These architectures address specific challenges, such as capturing context in language or generating high-quality synthetic data.

Finally, hybrid and modern architectures combine elements of the above types. For example, U-Net combines CNNs with skip connections for precise image segmentation in medical imaging. Reinforcement learning networks, like Deep Q-Networks (DQNs), integrate neural networks with reward-based training for game-playing agents. Capsule networks aim to improve CNNs by preserving spatial hierarchies between features. Developers choose architectures based on the problem: CNNs for image data, transformers for language, and hybrids for specialized tasks. Understanding these types helps in selecting the right tool, whether building a simple classifier or a complex generative system.