Weights And Biases
Distinct neural network parameters with specific roles. Weights determine the influence of inputs on outputs by modulating connection strength between neurons, while biases introduce a fixed value of 1 to the neuron’s output, ensuring activation even without input.
Areas of application
- Deep Learning
- Computer Vision
- Predictive Analytics
- Robotics and Automation
- Speech Recognition
- Natural Language Processing
- Healthcare and Medical Diagnosis
Example
In a neural network trained to recognize handwritten digits, the weights and biases of the layers determine how the network learns to identify different characters. For instance, the weights of the layer connecting the input and hidden layers may adjust the strength of the signal transmitted between them, while the biases of the hidden layers may introduce a fixed value to the output, allowing the network to recognize even slight variations in the input.