How Neural Networks Learn - 3 Minute Explanation

To solve the coding problem, and other free Machine Learning practice problems, head here: www.gptandchil...
Improving upon the prior practice problems (and corresponding lectures) that I created, I've repackaged them into the full Generative LLMs course, which will always be free!
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Gradient Descent is a powerful optimization algorithm widely used in machine learning and deep learning. It's like navigating a mountainous terrain to find the lowest valley, where our goal is to minimize a certain function. By iteratively adjusting parameters based on the slope (gradient) of the function, we descend towards the optimal solution. It leverages concepts like learning rate, which controls the step size in each iteration, and stochasticity to handle large datasets efficiently. Through backpropagation, it efficiently updates the weights of a neural network, enabling it to learn complex patterns and make accurate predictions. In essence, Gradient Descent is the compass guiding us through the high-dimensional landscape of optimization towards the promised land of minimal loss.

[@avi12]

I had just learned it last week in a machine learning course and you made it so much clearer

[@harryconcat]

thanks for giving me a reason why calculus explicitly gradient descend in this video is needed

[@punk3900]

What is there are local minima that might prevent the progress towards the true minimum?

[@Alex-pm8wy]

Yes - there’s algorithms for that too!