Types of Neural Networks and Their Applications

Different types of neural networks are used for specific deep learning applications. Some of the most common types include:

• Convolutional Neural Networks (CNNs): Specialized for image processing and computer vision tasks, CNNs extract spatial features from images and recognize patterns such as edges, textures, and objects. Applications include facial recognition, medical imaging, and self-driving cars.
• Recurrent Neural Networks (RNNs): Designed for sequential data, RNNs process information over time, making them suitable for speech recognition, text translation, and stock market predictions. Variants like LSTMs (Long Short-Term Memory) improve memory retention for long sequences.
• Generative Adversarial Networks (GANs): Used for generating realistic synthetic data, GANs are widely used in deepfake technology, AI-generated art, and image enhancement.
• Transformer Networks: The foundation of modern natural language processing (NLP) models, transformers like GPT and BERT are revolutionizing AI-driven language understanding, text summarization, and chatbot development.

Each neural network type is tailored for specific tasks, making deep learning one of the most powerful AI technologies available today.