Published by: Neha Khadka
Published date: 30 Jul 2024
A neural network is a computational model that is based on the information processing mechanisms of biological neural networks found in the human brain. These networks are made up of interconnected nodes, or neurons, that cooperate to carry out certain activities or find solutions to issues.
Here are some key concepts and components of neural networks:
Neurons: The basic units of a neural network, analogous to the nerve cells in the brain. Each neuron receives input, processes it, and produces an output.
Layers: Neural networks are organized into layers:
Weights and Biases: To reduce errors during training, a weight is assigned to each neuronal connection. Together with the weights, additional parameters called biases are used to modify the outcome.
Activation functions: These add non-linearities to the model by deciding whether or not to activate a neuron. ReLU (Rectified Linear Unit), sigmoid, and tanh are examples of common activation functions.
Forward Propagation: The process of passing input data through the network to generate an output.
Backpropagation: A training algorithm that adjusts the weights and biases of the network by minimizing the error between the predicted and actual outputs. It involves calculating the gradient of the loss function with respect to each weight and updating the weights accordingly.
Loss Function: A function that measures the difference between the network's output and the actual target values. Common loss functions include Mean Squared Error (MSE) and Cross-Entropy Loss.
Training and Learning: The process of optimizing the neural network's parameters (weights and biases) using training data. This often involves using optimization algorithms like Gradient Descent.
Neural networks are the foundation of many advanced machine learning techniques, including deep learning, where networks have many hidden layers. They are used in various applications such as image recognition, natural language processing, and game playing.