Deep Learning - Crash Course 2023 - Gradient Descent

The video tutorial explains the process of optimizing parameters in a model using gradient descent. It begins by discussing the importance of loss functions, such as squared error loss, in guiding parameter adjustments. The tutorial then introduces gradient descent as a method to minimize loss by iteratively updating weights and biases. It delves into the concept of derivatives, explaining how they help determine the direction and magnitude of parameter updates. The video also covers the calculation of partial derivatives and the role of the learning rate in controlling the update step size. The tutorial concludes by emphasizing the importance of these concepts in improving model performance.

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