Generative Modeling Foundations

Generative AI    |    Intermediate
  • 14 videos | 1h 57m 49s
  • Includes Assessment
  • Earns a Badge
Rating 4.0 of 1 users Rating 4.0 of 1 users (1)
This course dives deep into the world of generative models, providing learners with a comprehensive understanding of various generative techniques and their applications. This course is carefully designed to bridge theoretical concepts with practical applications, demystifying the methods used in popular generative models like generative adversarial networks (GANs), variational autoencoders (VAEs), and more. Through a combination of rich imagery, illustrative examples, and detailed explanations, participants will explore the differences between generative and discriminative modeling, the foundational framework of generative artificial intelligence (AI), and the various evaluation metrics that gauge the success of these models. Whether you're a budding data scientist, an AI enthusiast, or a seasoned researcher, this course offers a deep dive into the cutting-edge techniques that are shaping the future of artificial intelligence.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Outline how generative models are trained using images and practical examples
    Outline bayes theorem examples of prior probability, posterior probability, likelihood, and evidence
    Recognize the differences between generative and discriminative modeling
    Outline the foundational framework of generative modeling
    Identify popular types of generative models
    Outline the intricacies and applications of variational autoencoders (vaes)
  • Provide an overview of generative adversarial networks (gans) and their groundbreaking applications
    Outline the use of long short-term memory (lstm) and pixelcnn under the umbrella of autoregressive models
    Outline the concept, applications, and benefits of normalizing flow models
    Recognize the principles, advantages, and uses of energy-based models in artificial intelligence (ai)
    Outline the concept and significance of diffusion models in generative ai
    Recognize the metrics and methods used to evaluate the performance of generative models
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 56s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 7m 25s
    After completing this video, you will be able to outline how generative models are trained using images and practical examples. FREE ACCESS
  • Locked
    3.  Bayesian Statistics and Examples
    10m 25s
    After completing this video, you will be able to outline Bayes theorem examples of prior probability, posterior probability, likelihood, and evidence. FREE ACCESS
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    4.  Generative vs. Discriminative Models
    8m 45s
    Upon completion of this video, you will be able to recognize the differences between generative and discriminative modeling. FREE ACCESS
  • Locked
    5.  Framework of Generative Modeling
    13m 44s
    After completing this video, you will be able to outline the foundational framework of generative modeling. FREE ACCESS
  • Locked
    6.  Generative Model Types
    11m 8s
    Upon completion of this video, you will be able to identify popular types of generative models. FREE ACCESS
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    7.  Deep Dive into Variational Autoencoders (VAEs)
    10m 48s
    After completing this video, you will be able to outline the intricacies and applications of variational autoencoders (VAEs). FREE ACCESS
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    8.  Generative Adversarial Networks (GANs) Unveiled
    9m 41s
    Upon completion of this video, you will be able to provide an overview of generative adversarial networks (GANs) and their groundbreaking applications. FREE ACCESS
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    9.  Autoregressive Models Explored
    9m 1s
    After completing this video, you will be able to outline the use of long short-term memory (LSTM) and PixelCNN under the umbrella of autoregressive models. FREE ACCESS
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    10.  Introduction to Normalizing Flow Models
    7m 29s
    Upon completion of this video, you will be able to outline the concept, applications, and benefits of normalizing flow models. FREE ACCESS
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    11.  Energy-based Models: A Primer
    9m 3s
    After completing this video, you will be able to recognize the principles, advantages, and uses of energy-based models in artificial intelligence (AI). FREE ACCESS
  • Locked
    12.  Diffusion Models
    8m 11s
    Upon completion of this video, you will be able to outline the concept and significance of diffusion models in generative AI. FREE ACCESS
  • Locked
    13.  Generative Model Evaluation
    10m 31s
    After completing this video, you will be able to recognize the metrics and methods used to evaluate the performance of generative models. FREE ACCESS
  • Locked
    14.  Course Summary
    42s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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