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)
Stanford AA222 I Engineering Design Optimization | Spring 2025 | Disciplined Convex Programming
→ Solve optimization problems using convex programming→ Apply disciplined convex programming to engineering design
Optimization Of KNN: KD Tree and Ball Tree
→ Optimize KNN algorithm with KD Tree→ Implement Ball Tree for algorithm optimization
Optimization Skills for Hardware Stamping Part Extraction 🛠️📐⚙️
→ Apply optimization techniques to real-world problems→ Model complex systems using mathematical equations
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
Stanford EE364A Convex Optimization I Stephen Boyd I 2023 I Lecture 8
→ Apply convex optimization techniques→ Solve optimization problems using mathematical methods
Unlocking Profits with AI Pricing Strategies
→ Optimize pricing with AI→ Maximize profits with data-driven insights
Read (10 articles)
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