The AI Practitioner: Tuning AI Solutions
Artificial Intelligence
| Expert
- 14 Videos | 41m 45s
- Includes Assessment
- Earns a Badge
Tuning hyper parameters when developing AI solutions is essential since the same models might behave quite differently with different parameters set. AI Practitioners recognize multiple hyper parameter tuning approaches and are able to quickly determine best set of hyper parameters for particular models using AI toolbox. In this course, you'll learn advanced techniques for hyper parameter tuning for AI development. You'll examine how to recognize the hyper parameters in ML and DL models. You'll learn about multiple hyper parameter tuning approaches and when to use each approach. Finally, you'll have a chance to tune hyper parameters for a real AI project using multiple techniques.
WHAT YOU WILL LEARN
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discover the key concepts covered in this coursedescribe the role and importance of hyper parameters in AI developmentdescribe the process of hyper parameter tuning and list multiple approaches to the processdescribe the role of hyper parameters in common machine learning models and approachesdescribe the role of hyper parameters in deep learning neural network modelsspecify how to tune hyper parameters using a Grid Search approachspecify how to tune hyper parameters using a Random Search approach
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specify how to tune hyper parameters using Bayesian methodspecify how to tune hyper parameters based on gradientspecify how to utilize evolutionary hyper parameter tuningname multiple libraries that allow for hyper parameter tuning and describe how to use these librarieswork with the Python Grid Search algorithm for hyper parameter tuning of a machine learning model to configure optimal parameters and recognize an increase in accuracywork with the Python Random Search algorithm for hyper parameter tuning of a machine learning model to configure optimal parameters and describe the advantages of using the Random Search algorithmsummarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview1m 26sUP NEXT
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2.Hyper Parameters in AI Development3m 31s
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3.Hyper Parameter Tuning2m 45s
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4.Hyper Parameters in Machine Learning Algorithms3m 53s
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5.Hyper Parameters in Deep Learning Algorithms4m 15s
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6.Describe Hyper Parameter Tuning Using Grid Search in AI2m 47s
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7.Hyper Parameter Tuning Using Random Search2m 50s
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8.Hyper Parameter Tuning Using Bayesian Method2m 54s
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9.Gradient-based Hyper Parameter Tuning2m 57s
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10.Evolutionary Hyper Parameter Tuning3m 32s
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11.Hyper Parameter Tuning AI Libraries3m 31s
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12.Applying Hyper Parameter Grid Search3m 19s
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13.Applying Random Search for Hyper Parameter Tuning3m 17s
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14.Course Summary49s
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