## neuralnetwork.jpg

The image depicts a neural network diagram with three main layers: an Input layer, a Hidden layer, and an Output layer. Each layer is represented by circles connected by lines that signify the flow of data between nodes.

### **Input Layer**
- The Input layer consists of several nodes labeled as \( x_1 \), \( x_2 \), ..., up to \( x_n \). These represent input variables or features.
- Each node in this layer has a connection line leading outwards, indicating that these inputs are passed on for processing.

### **Hidden Layer**
- The Hidden layer is the middle section of the network and contains multiple nodes labeled as \( u_1 \), \( u_2 \), ..., up to \( u_m \). These nodes perform computations based on the input data.
- Each node in this layer receives inputs from all nodes in the Input layer. This means that each hidden unit takes a weighted sum of its inputs and applies an activation function, which is not explicitly shown but implied by the structure.

### **Output Layer**
- The Output layer contains several nodes labeled as \( o_1 \), \( o_2 \), ..., up to \( o_p \). These represent the final output or predictions made by the network.
- Each node in this layer receives inputs from all hidden units. This means that each output unit takes a weighted sum of its inputs and applies an activation function, which is not explicitly shown but implied.

### **Weights**
- The connections between nodes are labeled with weights such as \( w_{ij} \), \( w'_{jk} \). These weights represent the strength or importance of the connection from one node to another.
  - \( w_{ij} \) represents the weight connecting an input node \( x_i \) to a hidden node \( u_j \).
  - \( w'_{jk} \) represents the weight connecting a hidden node \( u_k \) to an output node \( o_k \).

### **Target**
- At the far right of the diagram, there is a label "Target" which indicates that these are the expected or desired outputs for each corresponding input. The target values help in training the network by comparing its predictions with actual outcomes.

This neural network structure is commonly used in machine learning and artificial intelligence to model complex relationships between inputs and outputs.

This description was generated automatically from image files by a local LLM, and thus, may not be fully accurate. Please feel free to ask questions if you have further questions about the nature of the image or its meaning within the presentation.