What Weights (in neural networks) Meaning, Applications & Example
The parameters of a neural network that are learned during training.
What are Weights (in neural networks)
Weights in neural networks are parameters that determine the strength of the connection between two neurons. They play a crucial role in the learning process, as they are adjusted during training to minimize the error in predictions. Each weight controls how much influence the input has on the output of a neuron, helping the network make accurate predictions or classifications.
Role of Weights
- Input-Output Relationship: Weights control how input values are transformed as they pass through the network, affecting the output produced by each neuron.
- Learning Process: During training, weights are adjusted using optimization algorithms (like Gradient Descent ) to minimize the loss function, which measures the difference between the predicted and actual output.
- Backpropagation : This process is used to update the weights based on the error gradient. Each weight is adjusted to reduce the error in subsequent predictions.
Applications of Weights
- Classification : Weights help the network classify data points by adjusting the decision boundary between classes.
- Regression: In regression tasks, weights allow the network to make predictions that best fit the data points.
- Image Recognition : Weights enable the network to detect patterns and features in images, such as edges or textures, by learning from labeled data.
Example of Weights (in neural networks)
In a binary classification task (e.g., determining whether an image contains a cat), the weights in a neural network are adjusted so that when the image is processed, the network outputs a value close to 1 for “cat” or 0 for “no cat.” During training, the weights are continuously updated to minimize the error in the network’s predictions, allowing it to classify images accurately over time.