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Course Outline

Introduction

  • What is generative AI?
  • Generative AI compared to other AI types.
  • Overview of key techniques and models in generative AI.
  • Applications and use cases of generative AI.
  • Challenges and limitations of generative AI.

Creating Images with Generative AI

  • Generating images from textual descriptions.
  • Utilizing GANs to produce realistic and diverse images.
  • Using VAEs to generate images with latent variables.
  • Applying artistic styles to images via style transfer.

Creating Text with Generative AI

  • Generating text from text prompts.
  • Leveraging transformer-based models for contextually coherent text.
  • Summarizing long texts to create concise summaries.
  • Paraphrasing text to express the same meaning in different ways.

Creating Audio with Generative AI

  • Generating speech from text.
  • Transcribing speech to text.
  • Composing music from text or audio inputs.
  • Producing speech with a specific voice profile.

Creating Other Content with Generative AI

  • Generating code from natural language descriptions.
  • Creating product sketches from text.
  • Generating video content from text or images.
  • Constructing 3D models from text or images.

Evaluating Generative AI

  • Assessing the quality and diversity of generative AI content.
  • Utilizing metrics such as Inception Score, Fréchet Inception Distance, and BLEU score.
  • Conducting human evaluation through crowdsourcing and surveys.
  • Applying adversarial evaluation methods, including Turing tests and discriminators.

Understanding Ethical and Social Implications of Generative AI

  • Ensuring fairness and accountability.
  • Preventing misuse and abuse.
  • Respecting the rights and privacy of content creators and consumers.
  • Fostering creativity and collaboration between humans and AI.

Summary and Next Steps

Requirements

  • Understanding of fundamental AI concepts and terminology.
  • Experience with Python programming and data analysis.
  • Familiarity with deep learning frameworks such as TensorFlow or PyTorch.

Audience

  • Data scientists.
  • AI developers.
  • AI enthusiasts.
 14 Hours

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