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    Google just launched a free course on AI. You'll like it

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    Google just launched a free course on AI. You'll like it

    Google has recently unveiled a new course on generative AI, which is available for free. While the course is still fresh, a quick overview reveals its promising content. The course comprises 10 modules covering introductory topics on generative AI, large language models, image generation, encoder and decoder architecture, attention mechanisms, and more. Let's delve into a brief exploration of a few modules to get a taste of what to expect.

    The first module impresses with a comprehensive reading list that includes pivotal papers like "Attention is All You Need" from 2017, known for its contributions to recent AI advancements. Module two serves as a beginner-friendly overview of large language models, aiming to clarify common misconceptions around their workings. Moving on to the introduction to image generation, module three caters to data scientists, machine learning engineers, and researchers, requiring prerequisites in machine learning, deep learning, CNNs, and Python programming. Lastly, the attention mechanism module, though targeted at specific technical audiences, is a crucial topic in generative AI that should not be overlooked, even for those outside the intended audience.

    Keywords

    • Google
    • AI course
    • Generative AI
    • Modules
    • Large Language Models
    • Image Generation
    • Attention Mechanism
    • Reading List
    • Beginner-Friendly
    • Technical Audience

    FAQ

    • What is the new course launched by Google? Google has released a free course on generative AI covering various introductory topics in the field.
    • Who is the course suitable for? The course caters to a wide range of audiences, from beginners to experienced data scientists, machine learning engineers, and researchers.
    • What are some key modules included in the course? Some notable modules cover large language models, image generation, encoder and decoder architecture, and attention mechanisms in generative AI.

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