How do neural networks actually learn from data?
Behind the scenes, there’s a powerful process involving forward passes, backpropagation, gradients, and optimization — and in this video, we break it all down step by step.
No fluff, no unnecessary jargon — just clear intuition.
You’ll learn:
• What happens during the forward pass
• How errors are calculated and propagated backward
• The intuition behind backpropagation
• How gradient descent updates model parameters
• The role of hyperparameters like batch size and learning rate
• Why techniques like dropout and regularization are essential
• How initialization and momentum affect training
By the end, you’ll have a complete mental model of how neural networks learn — not just formulas, but intuition you can actually use.
🎯 This video is part of Sum-It Up — where complex ideas are broken down into clear, structured insights.
📌 Up next:
Optimization tricks, advanced architectures, and how modern deep learning scales.
#AI #NeuralNetworks #DeepLearning #MachineLearning #Backpropagation #GradientDescent #ArtificialIntelligence #SumItUp
