Data Science

Generative AI and LLMs: Architecture and Data Preparation

(0 reviews)
Share icon
Coursera With GroupifyAI

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

  • 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

    Reviews for Generative AI and LLMs: Architecture and Data Preparation

    0 / 5

    from 0 reviews

    Ease of Use

    Ease of Customization

    Intuitive Interface

    Value for Money

    Support Team Responsiveness

    Alternative Tools for Generative AI and LLMs: Architecture and Data Preparation

    Master the implementation of deep learning algorithms using PyTorch, covering Deep Neural Networks and machine learning techniques, along with Python library utilization, to construct and deploy deep neural networks effectively.

    #PyTorch #Deep Neural Networks
    Visit icon

    Acquire the ability to create custom Datasets and DataLoaders in PyTorch and train a ResNet-18 model for image classification.

    #Machine Learning #Deep Learning
    Visit icon

    Learn to apply prompt engineering to the effective use of large language models such as ChatGPT, utilize prompt patterns to leverage model capabilities, and develop sophisticated prompt-based applications for diverse contexts such as life, business, or education.

    #Prompt Engineering #ChatGPT
    Visit icon

    Acquire practical skills to build a generative AI application by constructing a retrieval augmented generation (RAG) system using data, Qdrant, and LLMs.

    #Python Programming #Machine Learning
    Visit icon

    Acquire practical skills in fundamental machine learning models and their applications using PyTorch, as utilized by leading tech companies.

    #Convolutional Neural Network #Python Programming
    Visit icon

    Achieve a professional status as an AI Engineer. Acquire the knowledge necessary to develop next-generation applications that are propelled by generative AI, a skill that is indispensable for startups, agencies, and large corporations.

    #Vector databases #RAG
    Visit icon

    Unlock and capitalize on the capabilities of generative AI. Discover how the capabilities of generative AI can be leveraged to improve your work and personal life.

    #Artificial Intelligence (AI) #Prompt Engineering
    Visit icon

    Generative AI for Your Benefit. Utilize Generative AI to develop and instruct personalized assistants.

    #Generative AI #Problem Formulation
    Visit icon