Definition: Gradient descent is an optimization algorithm used to minimize a function. It iteratively updates the parameters of the function in the direction of the steepest descent of the gradient.

Detailed Description:

Gradient descent is a widely used optimization algorithm in machine learning and other fields. It is used to train models, such as neural networks, by finding the optimal values of the model’s parameters.

There are different variants of gradient descent, including:

• Batch gradient descent: Calculates the gradient over the entire dataset.
• Stochastic gradient descent: Calculates the gradient over a single data point.
• Mini-batch gradient descent: Calculates the gradient over a small subset of the data.

Gradient descent is a powerful tool for optimization, but it can be sensitive to the learning rate and can get stuck in local minima.

Related Terms:

• Optimization
• Machine Learning
• Neural Networks
• Gradient
• Learning Rate

FAQs:

1. What is the difference between batch gradient descent and stochastic gradient descent?

   • Batch gradient descent calculates the gradient over the entire dataset, while stochastic gradient descent calculates the gradient over a single data point.

2. How does gradient descent help in training neural networks?

   • Gradient descent is used to adjust the weights and biases of a neural network to minimize the loss function.

3. What are the challenges of using gradient descent?

   • Challenges include choosing the appropriate learning rate, dealing with local minima, and handling noisy data.