The AI Practitioner: Tuning AI Solutions

Artificial Intelligence    |    Expert
  • 14 videos | 41m 45s
  • Includes Assessment
  • Earns a Badge
Rating 4.5 of 26 users Rating 4.5 of 26 users (26)
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

  • 1m 26s
  • 3m 31s
    Upon completion of this video, you will be able to describe the role and importance of hyperparameters in AI development. FREE ACCESS
  • Locked
    3.  Hyper Parameter Tuning
    2m 45s
    Upon completion of this video, you will be able to describe the process of hyper parameter tuning and list multiple approaches to the process. FREE ACCESS
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    4.  Hyper Parameters in Machine Learning Algorithms
    3m 53s
    Upon completion of this video, you will be able to describe the role of hyperparameters in common machine learning models and approaches. FREE ACCESS
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    5.  Hyper Parameters in Deep Learning Algorithms
    4m 15s
    Upon completion of this video, you will be able to describe the role of hyperparameters in deep learning neural network models. FREE ACCESS
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    6.  Describe Hyper Parameter Tuning Using Grid Search in AI
    2m 47s
    After completing this video, you will be able to specify how to tune hyperparameters using a Grid Search approach. FREE ACCESS
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    7.  Hyper Parameter Tuning Using Random Search
    2m 50s
    After completing this video, you will be able to specify how to tune hyperparameters using a Random Search approach. FREE ACCESS
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    8.  Hyper Parameter Tuning Using Bayesian Method
    2m 54s
    After completing this video, you will be able to specify how to tune hyperparameters using the Bayesian method. FREE ACCESS
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    9.  Gradient-based Hyper Parameter Tuning
    2m 57s
    After completing this video, you will be able to specify how to tune hyperparameters based on gradient. FREE ACCESS
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    10.  Evolutionary Hyper Parameter Tuning
    3m 32s
    Upon completion of this video, you will be able to specify how to utilize evolutionary hyper parameter tuning. FREE ACCESS
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    11.  Hyper Parameter Tuning AI Libraries
    3m 31s
    Upon completion of this video, you will be able to name multiple libraries that allow for hyper parameter tuning and describe how to use these libraries. FREE ACCESS
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    12.  Applying Hyper Parameter Grid Search
    3m 19s
    In this video, you will learn how to use the Python Grid Search algorithm to tune the hyperparameters of a machine learning model. This will help you configure optimal parameters and improve the accuracy of your model. FREE ACCESS
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    13.  Applying Random Search for Hyper Parameter Tuning
    3m 17s
    During this video, you will learn how to work with the Python Random Search algorithm for hyperparameter tuning of a machine learning model to configure optimal parameters and describe the advantages of using the Random Search algorithm. FREE ACCESS
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    14.  Course Summary
    49s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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