The AI Practitioner: Optimizing AI Solutions

Artificial Intelligence    |    Expert
  • 14 videos | 38m 39s
  • Includes Assessment
  • Earns a Badge
Rating 4.2 of 72 users Rating 4.2 of 72 users (72)
Optimization is required for any AI model to deliver reliable outcomes in most of the use cases. AI Practitioners use their knowledge of optimization techniques to choose and apply various solutions and improve accuracy of existing models. In this course, you'll learn about advanced optimization techniques for AI Development, including multiple optimization approaches like Gradient Descent, Momentum, Adam, AdaGrad and RMSprop optimization. You'll examine how to determine the preferred optimization technique to use and the overall benefits of optimization in AI. Lastly, you'll have a chance to practice implementing optimization techniques from scratch and applying them to real AI models.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Define ai optimization and its importance in relation to the ai practitioner role
    Specify the types of ai optimization and describe key differences in the approaches
    Identify key benefits of and improvements that can be achieved by ai optimization
    Describe the principle of gradient descent optimization in ai and cases in which it is used
    Describe the principle of stochastic gradient descent optimization in ai and specify cases in which it is used
    Describe the principle of momentum optimization in ai and specify cases in which it is used
  • Describe the principle of adagrad optimization in ai and specify cases in which it is used
    Describe the principle of rmsprop optimization in ai and specify cases in which it is used
    Describe the principle of adam optimization in ai and specify cases in which it is used
    Describe the principle of adamax optimization in ai and specify cases in which it is used
    Implement gradient descent optimization algorithm from scratch using python libraries and describe how algorithm convergence achieves loss minimization goal
    Implement adagrad optimization algorithm from scratch using python libraries and specify formatting for inputs and outputs of the computation
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 27s
  • 2m 30s
    During this video, you will learn how to define AI optimization and its importance in relation to the AI Practitioner role. FREE ACCESS
  • Locked
    3.  Types of AI Optimization
    3m
    Upon completion of this video, you will be able to specify the types of AI optimization and describe key differences in the approaches. FREE ACCESS
  • Locked
    4.  Benefits of AI Optimization
    2m 35s
    During this video, you will learn how to identify key benefits of and improvements that can be achieved through AI optimization. FREE ACCESS
  • Locked
    5.  Gradient Descent Optimization in AI
    3m 44s
    After completing this video, you will be able to describe the principle of Gradient Descent optimization in AI and the cases in which it is used. FREE ACCESS
  • Locked
    6.  Stochastic Gradient Descent Optimization in AI
    2m 37s
    After completing this video, you will be able to describe the principle of Stochastic Gradient Descent optimization in AI and specify cases in which it can be used. FREE ACCESS
  • Locked
    7.  Momentum Optimization in AI
    2m 8s
    After completing this video, you will be able to describe the principle of momentum optimization in AI and specify cases in which it is used. FREE ACCESS
  • Locked
    8.  AdaGrad Optimization in AI
    2m 12s
    Upon completion of this video, you will be able to describe the principle of AdaGrad optimization in AI and specify cases in which it is used. FREE ACCESS
  • Locked
    9.  RMSprop Optimization in AI
    3m 7s
    After completing this video, you will be able to describe the principle of RMSprop optimization in AI and specify cases in which it is used. FREE ACCESS
  • Locked
    10.  Adam Optimization in AI
    3m 3s
    Upon completion of this video, you will be able to describe the principle of Adam optimization in AI and specify cases in which it can be used. FREE ACCESS
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    11.  AdaMax Optimization in AI
    3m 10s
    After completing this video, you will be able to describe the AdaMax principle of optimization in AI and specify cases in which it is used. FREE ACCESS
  • Locked
    12.  Applying Gradient Descent Optimization
    3m 56s
    In this video, you will implement the Gradient Descent Optimization algorithm from scratch using Python libraries and describe how the algorithm's convergence achieves the loss minimization goal. FREE ACCESS
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    13.  Applying AdaGrad Optimization
    4m 21s
    In this video, you will learn how to implement the AdaGrad Optimization algorithm from scratch using Python libraries and specify formatting for inputs and outputs of the computation. FREE ACCESS
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    14.  Course Summary
    50s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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