> [!info] ML Nodes > In machine learning, nodes are commonly used in various contexts within neural networks, which are a class of algorithms inspired by the structure and functioning of the human brain. Nodes, also known as neurons or units, play a fundamental role in neural network architectures. Here's how nodes are used in machine learning: #nodes #research #development #nodegraphics #nodegraph #visualprogramming #articles #machinelearning 2m 8s read #### 1. Neural Network Layers: Nodes are organized into layers within a neural network. These layers typically include an input layer, one or more hidden layers, and an output layer. Each node in a layer receives input signals from nodes in the previous layer, processes them, and passes the output to nodes in the next layer. #### 2. Feedforward Propagation: During the training and inference phases, data is fed forward through the network in a process known as feedforward propagation. Each node in a layer performs a weighted sum of its inputs, applies an activation function to the result, and passes the output to nodes in the next layer. #### 3. Weights and Biases: Each connection between nodes in adjacent layers is associated with a weight parameter, which determines the strength of the connection. Additionally, each node has a bias parameter, which allows the node to learn an offset or bias from the input data. These weights and biases are learned during the training process through optimization algorithms like gradient descent. #### 4. Activation Functions: Nodes typically apply an activation function to the weighted sum of their inputs to introduce non-linearity into the network and enable it to learn complex patterns in the data. Common activation functions include sigmoid, tanh, ReLU (Rectified Linear Unit), and softmax. #### 5. Learning Representations: Nodes in hidden layers of a neural network learn to extract and represent features from the input data through the iterative process of training. Each layer of nodes learns increasingly abstract and complex representations of the data, leading to hierarchical feature representations that capture relevant patterns in the input data. #### 6. Output Layer Processing: Nodes in the output layer of a neural network produce the final predictions or outputs based on the learned representations. The number of nodes in the output layer and the choice of activation function depend on the specific task being addressed (e.g., classification, regression). > [!NOTE] Overall > nodes play a central role in the operation of neural networks, enabling them to learn complex mappings from input data to output predictions through the iterative adjustment of weights and biases during the training process.