What are Weights and Biases?

What are Weights and Biases?

Weights and Biases (W&B) are two fundamental components in machine learning models, particularly in neural networks.

Weights are the real values attached to each input or feature in a model, and they convey the importance of that input in the model's calculations. They control the strength of the connection between neurons in a neural network, and thus capture the relationships between input features and target outputs.

Biases, on the other hand, are constants associated with each neuron. Unlike weights, biases are not connected to specific inputs but are added to the neuron’s output. They serve as a form of offset or threshold, allowing neurons to activate even when the weighted sum of their inputs is not sufficient on its own. Biases introduce a level of adaptability that ensures the network can learn and make predictions.

In the context of a neural network, the weights and biases develop how data flows forward through the network, a process called forward propagation. Once forward propagation is completed, the neural network refines connections using the errors that emerged in forward propagation. Then, the flow reverses to go through layers and identify nodes and connections that require adjusting.

It's also worth noting that Weights & Biases (W&B) is a machine learning platform designed to support and automate key steps in the MLOps life cycle, such as experiment tracking, dataset versioning, and model management. This platform is separate from the concept of weights and biases in machine learning models.

How do weights and biases differ from other parameters in neural networks?

Weights and biases are distinct neural network parameters with specific roles. Weights, as real values, determine the influence of inputs on outputs by modulating the connection strength between neurons. Biases are constants added to neurons, ensuring activation even without input. They introduce a fixed value of 1 to the neuron's output, enabling activation even when inputs are zero, thus maintaining the network's ability to adapt and learn.

Other parameters in a neural network include the learning rate, the batch size, the number of epochs, the number of layers, the number of neurons per layer, and the type of activation function. These parameters are typically set before training and are not learned from the data. They are often referred to as hyperparameters because they influence how the weights and biases will be learned.

In contrast, weights and biases are learned during the training stage. The algorithm itself, along with the input data, tunes these parameters. They are adjusted during the learning process to optimize the network's performance, a process that involves forward and backward propagation.

How do hyperparameters differ from weights and biases in neural networks?

In neural networks, parameters like weights and biases are learned from data during training. Weights adjust the influence of inputs on outputs by controlling the connection strength between neurons. Biases are constants added to neurons, ensuring activation even without input.

Hyperparameters, such as learning rate, epochs, batch size, and network architecture, are set prior to training and are not derived from data. They are critical for tuning the model's learning speed, complexity, and regularization, ultimately affecting the model's performance.