BACKPROPAGATION DERIVATION: Output Layer Weights & Bias Gradients
5:07
YouTubeProtorials By Saif
BACKPROPAGATION DERIVATION: Output Layer Weights & Bias Gradients
Unlock the core mechanism of Deep Learning! In this video, we go step-by-step through the Backpropagation derivation for the output layer parameters (w_1, w_2, b_o). You will learn exactly how to calculate the Gradients that power Gradient Descent in any Neural Network. We use a simple 2-2-1 network structure and the Mean Squared Error (MSE ...
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1.7K views · 102 reactions | Backpropagation explained via scrollytelling By Daniel Smilkov : https://google-developers.appspot.com/machine-learning/crash-course/backprop-scroll/ #artificialintelligence #backpropagation #deeplearning #neuralnetworks | Montreal.AI | Facebook
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