Overview of Neural Networks in Machine Learning
Neural networks are computational models inspired by biological neural networks, consisting of connected artificial neurons organized into layers. They process inputs through weighted connections and non-linear activation functions, learning from data through training processes like backpropagation. Over decades, architectural innovations such as convolutional neural networks, recurrent neural networks, and transformers have driven significant advancements in computer vision, speech recognition, natural language processing, and generative AI.
