Generative AI and LLMs: Architecture and Data Preparation

Generative AI and LLMs: Architecture and Data Preparation

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Learn about various generative AI models and architectures, the application of LLMs in language processing, and implement NLP preprocessing techniques using libraries and PyTorch.

Key AI Functions:Tokenization,Hugging Face,Libraries,NLP Data Loader,pytorch,Large Language Models

Description for Generative AI and LLMs: Architecture and Data Preparation

Features of Course

  • Distinguish between generative AI architectures and models, including RNNs, Transformers, VAEs, GANs, and diffusion models.
  • Explain the application of LLMs, including GPT, BERT, BART, and T5, in the field of language processing.
  • Using NLP libraries such as NLTK, spaCy, BertTokenizer, and XLNetTokenizer, implement tokenization to preprocess raw textual data.
  • Develop an NLP data processor that utilizes PyTorch to perform tokenization, numericalization, and padding on text data.
  • Level: Intermediate

    Certification Degree: Yes

    Languages the Course is Available: 1

    Offered by: On Coursera provided by IBM

    Duration: 5 hours to complete

    Schedule: Flexible

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