What is Gradient Descent?

Gradient Descent is crucial for training machine learning models, especially in deep learning. It is the backbone of most optimization processes used in AI, enabling models to become more accurate by minimizing their prediction errors.

Types of Gradient Descent

• Batch Gradient Descent: Calculates the gradient using the entire dataset and updates the model parameters accordingly.
• Stochastic Gradient Descent (SGD): Updates the model parameters after each training example, making it faster and suitable for large datasets.
• Mini-Batch Gradient Descent: A compromise between batch and stochastic gradient descent, updating the parameters after processing a small batch of examples.

Applications of Gradient Descent

• Training Neural Networks: Gradient descent is used to optimize the weights of neural networks, allowing them to learn from data and make accurate predictions.
• Linear Regression: In linear regression, gradient descent is used to find the optimal line that minimizes the prediction error.
• Support Vector Machines (SVMs): Gradient descent is also used in training SVMs to find the hyperplane that best separates different classes.

Conclusion

Gradient Descent is a foundational algorithm in machine learning, enabling models to learn by minimizing their errors. Its role in the optimization process is critical for the success of many AI applications.