In Part I, we explored how a simple neural network could tell a Bulldog from a German Shepherd just by analyzing images.

In Part II, we go deeper into the mechanics of deep learning.

You'll learn how cost functions, the chain rule, gradient descent, backpropagation, and forward passes help artificial neural networks learn and improve accuracy.

We break it down step-by-step with real-world examples, making it simple to understand how machines "think" and "learn" through constant adjustment.

Topics in this episode:

• How neural networks start with random weights and biases
• What the cost function measures
• How the chain rule helps identify errors
• How gradient descent improves predictions
• What happens during backpropagation and forward passes

Perfect for visual learners and curious minds interested in AI, machine learning, and deep learning.

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📖 Pre-order my book "AI, Machine Learning, Deep Learning: From Novice to Pro" here: https://a.co/d/igaehTZ