Optimisation
Understand gradient descent, convex optimisation, and loss landscapes.
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After this skill you can…
- Implement gradient descent from scratch
- Explain SGD, Adam, and RMSProp
- Identify local vs global minima in loss surfaces
Prerequisites
Watch (10 videos)
Advanced Modeling for Discrete Optimization
→ Apply discrete optimization to real-world problems→ Model complex systems for decision making→ Analyze optimization results for resource allocation
Stanford AA222 I Engineering Design Optimization | Spring 2025 | Disciplined Convex Programming
→ Solve optimization problems using convex programming→ Apply disciplined convex programming to engineering design
Solving Optimization Problems with Python Linear Programming
→ Learn optimization techniques→ Apply mathematical optimization to ML problems
Stanford EE364A Convex Optimization I Stephen Boyd I 2023 I Lecture 10
→ Apply convex optimization techniques→ Solve optimization problems
Stanford EE364A Convex Optimization I Stephen Boyd I 2023 I Lecture 5
→ Solve linear programs with MATLAB→ Model quadratic programs with CVX
Stanford AA222 I Engineering Design Optimization | Spring 2025 | Multiobjective Optimization
→ Apply multiobjective optimization techniques to engineering design problems→ Solve optimization problems using Stanford's AA222 course materials
Optimization School with Dr. Mike - #545
→ Apply optimization techniques→ Solve complex optimization problems→ Improve model performance
Improving the Security of United States Elections with Robust Optimization
→ Solve optimization problems using robust methods
What is the ADAM Optimizer❓- Deep Learning Beginner 👶 - Topic 106 #ai #ml
→ Optimize loss functions→ Use momentum terms→ Avoid local minimums
Stanford EE364A Convex Optimization I Stephen Boyd I 2023 I Lecture 2
→ Model and solve optimization problems→ Analyze and optimize machine learning algorithms
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