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Course Outline

Introduction

  • What is generative AI?
  • Generative AI compared to other types of AI.
  • Overview of main 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 text descriptions.
  • Using GANs to create realistic and diverse images.
  • Using VAEs to create images with latent variables.
  • Using style transfer to apply artistic styles to images.

Creating Text with Generative AI

  • Generating text from text prompts.
  • Using transformer-based models to create text with context and coherence.
  • Using text summarization to create concise summaries of long texts.
  • Using text paraphrasing to create different ways of expressing the same meaning.

Creating Audio with Generative AI

  • Generating speech from text.
  • Generating text from speech.
  • Generating music from text or audio.
  • Generating speech with a specific voice.

Creating Other Content with Generative AI

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

Evaluating Generative AI

  • Assessing content quality and diversity in generative AI.
  • Using metrics like inception score, Fréchet inception distance, and BLEU score.
  • Utilizing human evaluation through crowdsourcing and surveys.
  • Applying adversarial evaluation methods such as Turing tests and discriminators.

Understanding Ethical and Social Implications of Generative AI

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

Summary and Next Steps

Requirements

  • A foundational understanding of AI concepts and terminology.
  • Experience in 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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