Final Exam: Generative AI Introduction and Overview

WHAT YOU WILL LEARN

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  Provide an overview of generative artificial intelligence (ai) models
  

  outline the use of generative models
  

  recognize the applications of generative ai
  

  recognize the ethical considerations of using generative ai
  

  provide an overview of deepfake ai
  

  identify the risks associated with generative ai
  

  outline the history of generative ai
  

  outline how prompt engineering works
  

  recall how autoencoders work
  

  provide an overview of the autoencoder architecture
  

  review the autoencoder architecture
  

  set up a google colab environment
  

  import and view the fashion modified national institute of standards and technology (mnist) dataset
  

  reconstruct images with dense neural networks (dnns)
  

  reconstruct images with convolutional neural networks (cnns)
  

  train the autoencoder and visualize reconstructions
  

  describe the denoising autoencoder
  

  reconstruct images with denoising autoencoders
  

  provide an overview of variational autoencoders (vaes)
  

  describe the architecture of vaes
  

  train a vae
  

  train a convolutional vae
  

  recall how gans work
  

  describe the architecture of gans
  

  set up a virtual environment and python notebook for gan training
  

  load and explore the modified national institute of standards and technology (mnist) dataset
  

  train a gan
  

  provide an overview of dcgans
  

  create a generator and discriminator
  

  work with the openai playground
• 

  review openai models and apis
  

  assign personas to models and modify the temperature parameter
  

  outline how openai application programming interfaces (apis) work
  

  provide an overview of chatgpt and the openai playground
  

  provide an overview of generative artificial intelligence (ai) and large language models (llms)
  

  use the legacy completions api
  

  view examples of openai prompts and their responses
  

  create application programming interface (api) keys for openai apis
  

  send an api request from curl
  

  send an api request from python
  

  send prompts to the chat completions api
  

  send prompts to the legacy completions api
  

  send prompts to different models and view the results
  

  configure functions to check determinism
  

  adjust the seed parameter
  

  generate images using dall-e
  

  create image variations and perform inpainting
  

  transcribe clips of audio
  

  perform translation and text-to-speech conversion
  

  evaluate audio transcription
  

  set up the whisper model locally
  

  interpret images with the chat application programming interface (api)
  

  create prompt-completion pairs for fine-tuning
  

  upload a file for an assistant
  

  view chat responses from fine-tuned models
  

  create and query an assistant
  

  create and use functions for assistants
  

  introduce embeddings for text data
  

  classify reviews using embeddings
  

  cluster text based on embeddings