Neural network models refer to a class of computational models inspired by the structure and functioning of biological neural networks. They are a subset of machine learning algorithms designed to mimic the way the human brain processes and learns from information. Neural networks consist of interconnected artificial nodes or "neurons" organized in layers, with each neuron transforming input data with a weighted sum and applying an activation function to produce an output.
These models are used to address complex problems in various domains such as image recognition, natural language processing, and decision-making. They are particularly effective in handling large datasets and can automatically learn from the data to make predictions or classify new inputs.
Neural network models can be broadly categorized into three main types: feedforward neural networks, recurrent neural networks, and convolutional neural networks. Feedforward networks process data in a forward direction, without having any feedback connections. Recurrent networks allow for feedback connections, enabling them to process sequential information and capture dependencies over time. Convolutional networks are specifically designed for image and signal processing tasks, using convolutional layers to extract relevant features automatically.
These models undergo a training process, often referred to as "deep learning," where they adjust their internal weights and biases based on the input data and desired outputs. This training is typically done through an iterative optimization algorithm called gradient descent, aiming to minimize the difference between predicted and actual outputs.
Neural network models have gained significant popularity in recent years due to their ability to handle complex tasks and achieve state-of-the-art performance in various applications. However, their interpretability and explainability remain challenging areas of research.
