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What is Generative AI?

Learn the main concepts behind generative AI, how these technologies work, and explore some important business considerations and risks.

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About this course

This course introduces the concepts of generative AI, foundation models, deep learning transformers, and Large Language Models (LLMs). Next, the course covers some common Gen AI technologies with detailed examples. Lastly, you will learn about some data and security workflow considerations that arise with generative AI implementations, and the associated risks.

What's covered
What is Generative AI?
  • Generative AI defined
  • Benefits and business opportunities
  • How generative AI training works: pre-training and fine-tuning models
  • Gen AI uses of ML types: supervised, unsupervised, transfer, and reinforcement learning
  • Three main ML goals: classification, prediction, clustering
  • Generative AI components: foundation models, deep learning transformers, Large Language Models (LLMs)
Gen AI technologies from the inside
  • Text-to-text, text-to-audio, text-to-picture, picture-to-text
  • Prompt engineering types: conditional, zero/one/few shots, style transfer, specific outputs
  • Data access and geolocation, security, regulated industries and explainability
  • Fake/misleading content and AI hallucinations, deepfakes, cybercrime, ethics/legal, privacy/data leaks

Curriculum31 min

About this course

This course introduces the concepts of generative AI, foundation models, deep learning transformers, and Large Language Models (LLMs). Next, the course covers some common Gen AI technologies with detailed examples. Lastly, you will learn about some data and security workflow considerations that arise with generative AI implementations, and the associated risks.

What's covered
What is Generative AI?
  • Generative AI defined
  • Benefits and business opportunities
  • How generative AI training works: pre-training and fine-tuning models
  • Gen AI uses of ML types: supervised, unsupervised, transfer, and reinforcement learning
  • Three main ML goals: classification, prediction, clustering
  • Generative AI components: foundation models, deep learning transformers, Large Language Models (LLMs)
Gen AI technologies from the inside
  • Text-to-text, text-to-audio, text-to-picture, picture-to-text
  • Prompt engineering types: conditional, zero/one/few shots, style transfer, specific outputs
  • Data access and geolocation, security, regulated industries and explainability
  • Fake/misleading content and AI hallucinations, deepfakes, cybercrime, ethics/legal, privacy/data leaks

Curriculum31 min

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