Introduction to Generative Adversarial Networks (GANs)

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31-03-2023, 15:00
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  • Introduction to Generative Adversarial Networks (GANs)
    Free Download Introduction to Generative Adversarial Networks (GANs)
    Released: 03/2023
    Duration: 29m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 319 MB
    Level: Beginner | Genre: eLearning | Language: English
    Recently, you've probably seen the impacts of large-scale generative art, generative text, and generative movies. Do you want to understand the basics of how this type of AI works? In this course, Martin Kemka, founder of the machine learning production house Northraine, introduces you to a very important component in the world of generative AI: Generative Adversarial Networks (GANs). Learn about the history of GANs, including where they came from and how they changed over the last decade. Find out how to train a model as you examine the model architecture and how the structure of multiple models works together. Get hands-on experience training a simple model in Jupyter Notebook. Plus, get insights on the current state of GAI and thoughts on where it's going next.

Introduction to Generative Adversarial Networks (GANs)
Free Download Introduction to Generative Adversarial Networks (GANs)
Released: 03/2023
Duration: 29m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 319 MB
Level: Beginner | Genre: eLearning | Language: English
Recently, you've probably seen the impacts of large-scale generative art, generative text, and generative movies. Do you want to understand the basics of how this type of AI works? In this course, Martin Kemka, founder of the machine learning production house Northraine, introduces you to a very important component in the world of generative AI: Generative Adversarial Networks (GANs). Learn about the history of GANs, including where they came from and how they changed over the last decade. Find out how to train a model as you examine the model architecture and how the structure of multiple models works together. Get hands-on experience training a simple model in Jupyter Notebook. Plus, get insights on the current state of GAI and thoughts on where it's going next.


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