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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

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Description for Generative AI and LLMs: Architecture and Data Preparation

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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