Introduction to Neural Networks
Neural networks are a subset of machine learning algorithms designed to mimic the way the human brain operates. They are composed of layers of interconnected nodes, or neurons, which process data in a hierarchical manner. The network learns by adjusting the weights of the connections between neurons, enabling it to make accurate predictions or classifications.
Key Concepts:
- Neurons: Basic units of a neural network that receive input, process it, and pass the output to the next layer.
- Layers: Neural networks typically consist of an input layer, one or more hidden layers, and an output layer.
- Weights: Parameters within the network that are adjusted during training to minimize the error of the predictions.
- Activation Functions: Functions that determine the output of a neuron. Common activation functions include sigmoid, tanh, and ReLU (Rectified Linear Unit).
Each of these subtopics can be expanded with examples, case studies, and practical exercises to solidify understanding and application in real-world scenarios.
