Deep Learning: Theory and Practice Matrix Calculus 31-1-2019 - PowerPoint PPT Presentation

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Deep Learning: Theory and Practice Matrix Calculus 31-1-2019 Linear and Logistic Regression Models deeplearning.cce2019@gmail.com Matrix Derivatives Linear Models for Classification Optimize a modified cost function Bishop - PRML book

Deep Learning: Theory and Practice Matrix Calculus 31-1-2019 Linear and Logistic Regression Models deeplearning.cce2019@gmail.com
Matrix Derivatives
Linear Models for Classification ❖ Optimize a modified cost function Bishop - PRML book (Chap 3)
Least Squares for Classification ❖ K-class classification problem ❖ With 1-of-K hot encoding, and least squares regression Bishop - PRML book (Chap 3)
Gradient Descent For Function Minimization
Non-linear Optimization Typical Error Surface as a function of parameters (weights) Highly Non-linear
Approximate Minimization
Approximate Minimization Method of Steepest Descent Error surface close to a local optima Move to local optima
Logistic Regression ❖ 2- class logistic regression ❖ Maximum likelihood solution ❖ K-class logistic regression ❖ Maximum likelihood solution Bishop - PRML book (Chap 3)
Least Squares versus Logistic Regression Bishop - PRML book (Chap 4)
Least Squares versus Logistic Regression Bishop - PRML book (Chap 4)

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