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Master advanced ML/DS statistics
·
Module 4
Optimization for Machine Learning
1
Understanding Loss Functions: Quantifying Model Error
Explain the role of a loss function in quantifying model error.
2
Gradient Descent: Minimizing Loss Iteratively
Describe gradient descent as an iterative algorithm for minimizing a loss function.
3
Gradient Descent for Model Optimization
Implement gradient descent to optimize the parameters of a simple model.
4
Batch vs. Mini-Batch vs. Stochastic Gradient Descent
Differentiate between batch, mini-batch, and stochastic gradient descent and their trade-offs.
5
Optimizing Learning Rates
Diagnose and address issues with learning rates (too high or too low).
6
Momentum in Gradient Descent
Explain how momentum can help accelerate and stabilize gradient descent.
7
Understanding Adam: An Adaptive Learning Rate Method
Describe the core idea behind adaptive learning rate methods like Adam.
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Statistical Inference and Estimation
Next module
Linear Regression and Regularization