Machine Learning for Engineering & Science Applications | IIT Madras - #20 Introduction to Numerical Optimization Gradient Descent | Part 1

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What you'll learn

Understand the historical development and foundational concepts of artificial intelligence.
Gain proficiency in applying machine learning techniques to engineering and science problems.
Develop skills in using linear algebra and calculus for machine learning modeling.
Learn to implement and optimize machine learning algorithms using Python packages.

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Lecture 20 Introduction to Numerical Optimization Gradient Descent - 1

Full Transcript

Welcome to 'Machine Learning for Engineering & Science Applications' course ! This lecture introduces numerical optimization, specifically focusing on gradient descent, a widely used optimization algorithm. It explains the motivation for numerical optimization when analytical expressions are unavailable or difficult to handle. The lecture covers the basic steps of gradient descent, including the concept of learning rates and hyperparameters. NPTEL Courses permit certifications that can be used for Course Credits in Indian Universities as per the UGC and AICTE notifications. To understand various certification options for this course, please visit https://nptel.ac.in/courses/106106198 #NumericalOptimization #GradientDescent #AnalyticalExpressions #Hyperparameters

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