Neural Networks (NN) are a key component of Artificial Intelligence (AI) and Machine Learning (ML) systems. They are computational models inspired by the structure and functioning of the human brain. Neural Networks consist of interconnected nodes, known as artificial neurons or "units," which work together to process and analyze information.
The basic building block of a neural network is the artificial neuron, also called a perceptron. Each neuron takes inputs, performs a computation, and produces an output. These inputs are multiplied by respective weights and summed together, and then passed through an activation function to produce the output. The activation function introduces non-linearity into the neural network, enabling it to model complex relationships and make nonlinear predictions.
Neural Networks typically consist of multiple layers of interconnected neurons, organized into an input layer, one or more hidden layers, and an output layer. The connections between the neurons have associated weights that determine the strength and impact of each connection on the network's output. During the training process, the network learns to adjust these weights based on the provided input and desired output, allowing it to learn and generalize patterns from the data.
Deep Neural Networks (DNNs) refer to neural networks with multiple hidden layers. Deep learning, a subfield of ML, leverages deep neural networks to learn hierarchical representations of data, enabling them to capture intricate features and patterns. Deep learning has achieved remarkable success in various domains, including computer vision, natural language processing, speech recognition, and many more.
Neural Networks have proven to be powerful tools for solving complex problems, including image and speech recognition, natural language understanding, recommendation systems, anomaly detection, and more. Their ability to learn from large amounts of data and discover intricate patterns makes them a fundamental technology in the field of AI and ML.
