Machine Learning
451 learners
Foundations of Optimization Algorithms
Optimization is critical in machine learning to minimize loss functions. This course covers basic to advanced optimization algorithms, equipping you with the techniques needed to fine-tune machine learning models.
Numpy
Python
4 lessons
17 practices
2 hours
Deep Learning and Neural Networks
Lessons and practices
Optimize the Quadratic Function using Newton's Method
Minimize and Plot Optimization Path using Newton's Method
Minimize Function and Plot Optimization Paths from Different Initial Guesses
Minimize or Maximize?
Finding Minimum of a Complex Function Using Gradient Descent
Changing Starting Points in Gradient Descent
Experimenting with Learning Rate in Gradient Descent
Minimize a 3-Variable Function Using Gradient Descent
Implement Gradient Descent with Tolerance Stopping Criterion
Applying Momentum in Gradient Descent
Gradient Descent with Momentum: Minimize and Plot Contour
Adjust Momentum to Observe Convergence Speed
Plotting Gradient Descent with Momentum
Gradient Descent with Momentum from Multiple Initial Points
Implementing Adagrad for Function Optimization
Optimization Paths using Adagrad from Multiple Initial Points
Minimize and Plot Paths with Adagrad and Gradient with Momentum
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