Master decision science with our BCS405C Optimization Technique VTU Notes. Explore linear programming, simplex methods, and transportation models for the 2022 Scheme at the all-new vtubuddy.in student resource portal.
Master decision science with our BCS405C Optimization Technique VTU Notes. Explore linear programming, simplex methods, and transportation models for the 2022 Scheme at the all-new vtubuddy.in student resource portal.
Functions of several variables, Differentiation and partial differentials, gradients of vector-valued functions, gradients of matrices, useful identities for computing gradients, linearization and multivariate Taylor series.
Backpropagation and automatic differentiation, gradients in a deep network, The Gradient of Quadratic Cost, Descending the Gradient of Cost, The Gradient of Mean Squared Error.
Local and global optima, convex sets and functions separating hyperplanes, application of Hessian matrix in optimization, Optimization using gradient descent, Sequential search 3- point search and Fibonacci search
Unconstrained optimization -Method of steepest ascent/descent, NR method, Gradient descent, Mini batch gradient descent, Stochastic gradient descent.
Momentum-based gradient descent methods: Adagrad, RMSprop and Adam. Non-Convex Optimization: Convergence to Critical Points, Saddle-Point methods.
BCS702
BCS701
BIS654C
BCS3012Mod
BCEDK103
BCSL305
BCS30122550question
BCS303
XYZS301
