MIT
Lecture 33: Neural Nets and the Learning Function
Science & MathMathematicsEngineeringElectrical Engineering
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Description This lecture focuses on the construction of the learning function \(F\), which is optimized by stochastic gradient descent and applied to the training data to minimize the loss. Professor Strang also begins his review of distance matrices. Summary Each training sample is given by a vector \(v\). Next layer of the net is \(F_1(v)\) = ReLU\((A_1 v + b_1)\). \( w_1 = A_1 v + b_1\) with optimized weights in \(A_1\) and \(b_1\) ReLU(\(w\)) = nonlinear activation function \(= \max (0,w) \) Minimize loss function by optimizing weights \(x\)’s = \(A\)’s and \(b\)’s Distance matrix given between points: Find the points! Related sections in textbook: VII.1 and IV.10 Instructor: Prof. Gilbert Strang