A comprehensive guide to advanced optimization algorithms used in modern machine learning. This course covers the theoretical underpinnings and practical implementations of sophisticated methods designed for complex, large-scale, and non-convex optimization problems encountered when training machine learning models.
Prerequisites ML fundamentals, calculus, Python
Level:
Second-Order Methods
Understand and implement Newton's method, Quasi-Newton methods (BFGS, L-BFGS), and trust-region methods.
Adaptive Learning Rates
Analyze and apply algorithms like AdaGrad, RMSprop, Adam, and their variants for efficient convergence.
Large-Scale Optimization
Implement variance reduction techniques (SAG, SVRG) and strategies for handling massive datasets.
Distributed Optimization
Understand parameter server architectures, synchronous/asynchronous updates, and communication-efficient algorithms.
Non-Convex Optimization
Analyze challenges in deep learning optimization and apply techniques to navigate complex loss landscapes.
Convergence Analysis
Evaluate the theoretical convergence properties and practical performance of different optimization algorithms.
Implementation
Gain hands-on experience implementing and tuning advanced optimization algorithms using common ML frameworks.
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