(Part II) How Deep Learning Works | Neural Networks, Backpropagation Explained

In Part I, we explored how a basic neural network could distinguish between a Bulldog and a German Shepherd just by analyzing images.

In this Part II video, we go deeper into how deep learning actually works under the hood.
You’ll learn the role of cost functions, chain rule, gradient descent, backpropagation, and forward passes — all essential steps that help an artificial neural network learn and improve its accuracy.

We break it down step-by-step with simple examples so you can finally understand how machines "think" and "learn" over time.

Topics Covered:
How neural networks start with random weights and biases
What a cost function does in machine learning
How the chain rule helps identify mistakes
How gradient descent finds the best path
What happens during backpropagation and forward passes


🎥 Watch Part I here:    • How Deep Learning Works Explained Simply (...  

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