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)
Student Registration Solved: Under 2 Seconds! #shorts
→ Solve complex optimization problems→ Achieve efficient results→ Reduce manual labor
Newton's Method is Just Tangent Lines
→ Apply optimization techniques to solve problems→ Use calculus to find roots of equations
[NeurIPS 2025]: Sharper Convergence Rates for Nonconvex Opt via Reduction Mappings (Markou et al.)
→ Use reduction mappings to improve convergence rates→ Apply optimization techniques to high-dimensional problems
Why we should use Momentum Based Gradient Descent | Accelerate Deep Learning Training Process
→ Analyze optimization methods→ Implement Momentum Based Gradient Descent→ Improve training efficiency
Stanford AA222 I Engineering Design Optimization | Spring 2025 | Disciplined Convex Programming
→ Apply Disciplined Convex Programming→ Solve Engineering Design Optimization problems→ Use Convex Optimization techniques
Optimization School with Dr. Mike - #545
→ Apply optimization techniques→ Solve complex optimization problems→ Improve model performance
Combinatorial Pure Exploration with Limited Observation and Beyond
→ Formulate optimization problems→ Apply optimization techniques→ Analyze convergence rates
Tony Silveti Falls - Training neural networks at any scale
→ Apply linear minimization oracle to neural network training→ Develop stochastic algorithms for large-scale problems
What is the ADAM Optimizer❓- Deep Learning Beginner 👶 - Topic 106 #ai #ml
→ Optimize loss functions→ Use momentum terms→ Avoid local minimums
Research talk: Optimizing the cloud supply chain
→ Optimize cloud supply chain→ Model stochastic demands→ Analyze power and cooling constraints
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