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

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 biasesWhat the cost function measuresHow the chain rule helps identify errorsHow gradient descent improves predictionsWhat happens during backpropagation and forward passesPerfect for visual learners and curious minds interested in AI, machine learning, and deep learning.-----------------📖 Pre-order my book "AI, Machine Learning, Deep Learning: From Novice to Pro" here: https://a.co/d/igaehTZ