Learn · Optimisation for Machine Learning: Theory and Implementation (Hindi)
Lecture 27 of 36week-06
Variants of Gradient Descent -1
Prof. Pravesh Biyani · IIIT Delhi
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- Optimisation for Machine Learning: Theory and Implementation (Hindi), IIIT Delhi (at NPTEL)
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All the lectures
- Basics of Linear Algebra: Linear Independence
- Linear Algebra: Rank of a matrix
- Linear Algebra - Subspaces of a matrix -1
- Linear Algebra - Subspaces of a matrix -2
- Linear Algebra -- Null space
- Linear Algebra -- Eigen Vectors/Values of a matrix -1
- Linear Algebra -- Eigen Vectors/Values of a matrix -2
- Programming Eigen Decomposition using Python
- Singular Value Decomposition - 1
- Singular Value Decomposition - 2
- Principal Component Analysis - 1
- Principal Component Analysis - 2
- Principal Component Analysis - 3
- Principal Component Analysis - Coding
- Machine Learning - Overview
- Optimisation Problems
- Gradient of a Vector Valued Function -1
- Gradient of a Vector Valued Function -2
- Neural Netowrks - Overview
- Neural Netowrks - Backpropagation
- Optimisation - Introduction to optimisation problems
- Optimisation - Relaxation and approximate convergence
- Optimisation - First Order Optimality Condition
- Optimisation - Second Order Optimality Condition
- Proof of Second Order Optimality Condition, Gradient Methods
- Gradient Descent -2
- Variants of Gradient Descent -1
- Variants of Gradient Descent -2
- Variants of Gradient Descent -3
- Convex Sets
- Convex Functions
- Duality and Lagrangian - Part 1
- Duality and Lagrangian - Part 2
- Duality and Lagrangian - Part 3
- Coding: Introduction to Pytorch
- Guest Lecture: Support Vector Machine