Machine Learning Objectives - Optimization and Gradient Descent

Optimization in machine learning refers to the process of adjusting weights and biases to ensure that the model performs at its best.

The learning process of a model typically involves the following steps:

1. The model takes input data and calculates predicted values.

2. A loss function is utilized to compute the difference or loss between the predicted values and the actual values.

3. If the loss is significant, the model adjusts its weights and biases to make better predictions.

4. This iterative process helps the model gradually learn and improve the accuracy of its predictions.

Through this iterative process, a machine learning model, which initially may not predict answers accurately, learns to make increasingly accurate predictions.

For a model to learn effectively, it needs a method to minimize the loss.

Optimization algorithms are designed to reduce the loss by systematically adjusting the model’s parameters.

The most commonly used optimization algorithm is Gradient Descent.

Gradient Descent is similar to finding the lowest point in a mountain valley.

Imagine starting at the top of a mountain.

A large loss value implies being at the mountain's peak, while a small loss value indicates being closer to the base.

Gradient Descent involves taking small steps in the direction of the steepest decline to reach the point of minimum loss.

The key concept here is the Gradient of the Loss Function.

When descending a mountain, finding the steepest path ensures a faster descent.

Similarly, in Gradient Descent, the gradient (or derivative) of the loss function is calculated to adjust the weights.

If the gradient is steep, the loss value is reduced quickly with larger steps; if it's shallow, the adjustments are smaller and more cautious, aiming for the optimal values.

Thus, Gradient Descent works by calculating the gradient of the loss function and updating the weights and biases in the direction that reduces the loss most effectively.

In the next lesson, we'll have a simple quiz to review the concepts we've covered so far.