The AI Practitioner: Tuning AI Solutions

Artificial Intelligence
  • 14 Videos | 47m 45s
  • Includes Assessment
  • Earns a Badge
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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

  • discover the key concepts covered in this course
    describe the role and importance of hyper parameters in AI development
    describe the process of hyper parameter tuning and list multiple approaches to the process
    describe the role of hyper parameters in common machine learning models and approaches
    describe the role of hyper parameters in deep learning neural network models
    specify how to tune hyper parameters using a Grid Search approach
    specify how to tune hyper parameters using a Random Search approach
  • specify how to tune hyper parameters using Bayesian method
    specify how to tune hyper parameters based on gradient
    specify how to utilize evolutionary hyper parameter tuning
    name multiple libraries that allow for hyper parameter tuning and describe how to use these libraries
    work with the Python Grid Search algorithm for hyper parameter tuning of a machine learning model to configure optimal parameters and recognize an increase in accuracy
    work 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 algorithm
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 26s
    UP NEXT
  • Playable
    2. 
    Hyper Parameters in AI Development
    3m 31s
  • Locked
    3. 
    Hyper Parameter Tuning
    2m 45s
  • Locked
    4. 
    Hyper Parameters in Machine Learning Algorithms
    3m 53s
  • Locked
    5. 
    Hyper Parameters in Deep Learning Algorithms
    4m 15s
  • Locked
    6. 
    Describe Hyper Parameter Tuning Using Grid Search in AI
    2m 47s
  • Locked
    7. 
    Hyper Parameter Tuning Using Random Search
    2m 50s
  • Locked
    8. 
    Hyper Parameter Tuning Using Bayesian Method
    2m 54s
  • Locked
    9. 
    Gradient-based Hyper Parameter Tuning
    2m 57s
  • Locked
    10. 
    Evolutionary Hyper Parameter Tuning
    3m 32s
  • Locked
    11. 
    Hyper Parameter Tuning AI Libraries
    3m 31s
  • Locked
    12. 
    Applying Hyper Parameter Grid Search
    3m 19s
  • Locked
    13. 
    Applying Random Search for Hyper Parameter Tuning
    3m 17s
  • Locked
    14. 
    Course Summary
    49s

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