There are two main types of neural networks:
- Feedforward Neural Networks (FNN): These networks propagate data from input nodes through hidden layers to output nodes without cycles or loops. They are used for tasks such as pattern recognition, classification, and regression.
- Recurrent Neural Networks (RNN): RNNs have connections that form cycles, allowing them to exhibit dynamic temporal behavior by processing sequences of inputs. They are suitable for tasks involving sequential data, such as natural language processing, speech recognition, and time series prediction.
Neural networks can be broadly categorized into two types:
- Feedforward Neural Networks (FNN): These networks process data in a unidirectional manner, passing information from input nodes through hidden layers to output nodes. FNNs are commonly used for tasks like image recognition, classification, and regression.
- Recurrent Neural Networks (RNN): RNNs are designed to handle sequential data by maintaining a state or memory of previous inputs. This enables them to process sequences of data, making them suitable for applications such as natural language processing, handwriting recognition, and speech recognition.
Learning in neural networks typically involves two main types:
- Supervised Learning: In supervised learning, the network learns from labeled training data where inputs are paired with corresponding target outputs. The network adjusts its parameters through iterative optimization algorithms to minimize the difference between predicted and actual outputs, enabling it to generalize and make predictions on new, unseen data.
- Unsupervised Learning: Unsupervised learning involves training the network on unlabeled data, where the network identifies patterns, structures, or relationships within the data without explicit guidance. Common tasks include clustering, dimensionality reduction, and anomaly detection, helping to uncover hidden insights and structure from data.
The two main parts of neural networks are:
- Neurons (Nodes): Neurons are the fundamental units that process and transmit information within a neural network. Each neuron receives input signals, applies weights and biases, and outputs a result to the next layer or as a final output.
- Connections (Weights): Connections between neurons represent the flow of information in a neural network. Each connection is associated with a weight that determines the strength of the relationship between connected neurons. Adjusting these weights during training allows the network to learn and adapt to different tasks and data patterns.
Artificial neural networks (ANNs) encompass various architectures, but two primary types include:
- Feedforward Neural Networks (FNN): FNNs process data in a unidirectional manner, passing information from input nodes through hidden layers to output nodes. They are effective for tasks requiring pattern recognition, classification, and regression.
- Recurrent Neural Networks (RNN): RNNs are designed to handle sequential data by maintaining a memory of previous inputs through feedback loops. This enables them to process time-series data, natural language sequences, and other sequential data types effectively.