Machine Learning for Engineering & Science Applications | IIT Madras - #58 Hyperparameter Optimization | Machine Learning for Engineering & Science Applications
Unlock the Future: Master AI & Machine Learning with NPTEL-IITM’s Comprehensive Course! Dive into Neural Networks, Deep Learning, Probabilities, and Optimization Techniques tailored for Engineering & Science Applications. Your AI journey starts here!
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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.
Welcome to 'Machine Learning for Engineering & Science Applications' course !
This lecture discusses hyperparameter optimization, which is an important aspect of training a deep neural network. Grid search and random search are two common methods for hyperparameter optimization. It is recommended to discretize the hyperparameter values on a log scale.
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To understand various certification options for this course, please visit https://nptel.ac.in/courses/106106198
#HyperparameterOptimization #DeepNeuralNetwork #GridSearch #RandomSearch #LogScale
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