Neural networks, also known as artificial neural networks (ANNs), are a type of [[machine learning (ML)]] model inspired by the structure and function of the human brain. They consist of interconnected nodes, or neurons, organized into layers, which work together to process and transform input data into a desired output. Neural networks are particularly effective at recognizing patterns and learning complex relationships within data and [[Big Data]], and form a core part of current [[artificial intelligence (AI)]] research.
## Key components and concepts in neural networks
1. **Neurons**: Neurons are the fundamental building blocks of neural networks. They receive input signals, process them, and produce an output signal. Each neuron computes a weighted sum of its inputs, adds a bias term, and then applies an activation function to generate the output.
2. **Layers**: Neural networks are organized into layers. There are three main types of layers:
- **Input layer**: This layer receives the raw input data and passes it to the subsequent layers.
- **Hidden layer(s)**: These layers perform the majority of the computation in the network. A neural network can have multiple hidden layers, creating a deep neural network (DNN).
- **Output layer**: This layer produces the final output, which could be a class label, a regression value, or a probability distribution.
3. **Weights and biases**: Weights are the connection strengths between neurons, and biases are additional terms added to the weighted sum of inputs. These are the adjustable parameters of a neural network that are learned during training to minimize the error between predicted and true outputs.
4. **Activation functions**: Activation functions introduce non-linearity into neural networks, allowing them to learn complex, non-linear relationships between inputs and outputs. Common activation functions include the sigmoid, hyperbolic tangent (tanh), and Rectified Linear Unit (ReLU).
5. **Forward propagation**: This is the process of passing input data through the network to produce an output. Data flows from the input layer, through hidden layers, and finally to the output layer.
6. **Loss function**: The loss function quantifies the difference between the network's predictions and the true outputs. It is used to evaluate the performance of the network during training.
7. **Backpropagation**: This is the primary algorithm used to train neural networks. It is a form of supervised learning that involves computing the gradient of the loss function with respect to each weight and bias by utilizing the chain rule. The weights and biases are then updated using gradient descent or other optimization algorithms.
8. **Regularization**: Techniques like L1 and L2 regularization, dropout, and early stopping are used to prevent overfitting, which occurs when a neural network learns to perform well on the training data but fails to generalize to new, unseen data.
Neural networks have been applied to a wide range of tasks, including image recognition, [[Natural Language Processing (NLP)]], speech recognition, game playing, and recommendation systems. With the advent of [[deep learning]], neural networks have become increasingly powerful and capable of handling large, complex datasets, driving significant advances in [[artificial intelligence (AI)]].