Backpropagation Done Right! Caclulus for ML - Part Many/Many
In this lecture, we use the knowledge we developed from calculus to derive the mathematics of backpropagation from scratch. This is the power horse of AI!
#ai #machinelearning #reinforcementlearning #deeplearning
00:00 What is Backprop
03:45 Loss Function and Partial Derivatives
07:55 Example K-Layer MLP
16:31 Gradient on K-1 Layer
22:12 Gradient of K-2 Layer
GPT4s Rhythm:
In neural networks where dreams are shaped, backpropagation helps escape,
Mistakes of yore, now insights pour, as layers adapt and reshape.
Through hidden layers, errors flow, as gradients teach weights to grow,
In reverse they tread, refining the spread, till perfect predictions show.