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29:10
Module 1: Supervised Learning & OptimizationLesson 1: Gradient Descent & Loss Landscapes
Visualize how parameters update along negative gradients to minimize cost functions such as Mean Squared Error and Cross-Entropy.
Key Takeaways
- Learning rate selection: too large oscillates, too small converges slowly
- Stochastic vs mini-batch gradient descent trade-offs
- Convex vs non-convex optimization landscapes
Course Completion0%
0 of 2 lessons completed
Course Curriculum
Module 1: Supervised Learning & Optimization
Instructor
Kavya Nair
AI Research Scientist
Published researcher in deep learning architectures, former lab researcher at Stanford AI, passion for intuitive math.