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Overview
Technologies like OpenAI's ChatGPT and Google's Bard are changing the way we work and generative AI will become increasingly popular. As data professionals and developers, it is important to know how these tools work under the hood and how you can leverage large language models (LLMs) for your work.
Features
LLMs have revolutionized the field of natural language processing (NLP) and are increasingly being used to solve a wide range of NLP problems in various industries. Understanding LLMs can help developers and data scientists, like you, to:
- Build better NLP models: LLMs are state-of-the-art models for many NLP tasks, and understanding how they work can help developers and data scientists to build better models and achieve better performance on their NLP tasks.
- Develop custom NLP applications: LLMs can be fine-tuned to specific NLP tasks, making them highly adaptable to different domains and use cases. Developers and data scientists who understand LLMs can leverage this flexibility to develop custom NLP applications for their specific needs.
- Optimize model performance: Understanding LLMs can help developers and data scientists to optimize model performance by selecting the appropriate architecture, prompt engineering, fine-tuning strategies, and downstream tasks for their specific use case.
With the most recent release of OpenAI's GPT-4 language model, it is being used by Morgan Stanley wealth management to organize its vast knowledge base, Be My Eyes to transform visual accessibility, Stripe to streamline user experinece and combat fraud, and the Government of Iceland to preserve its language. This Foundations of Large Language Models - Tools, Techniques, and Applications course is offered at the University of Waterloo.
Programme Structure
What you will learn:
- Understand the evolution of transformer architectures and the historical context behind ChatGPT: Decoder-only models (e.g., GPT-4), encoder-only models (e.g., BERT), encoder-decoder models (e.g., T5)
- Understand the use cases for working with different machine learning paradigms (supervised, self-supervised, in-context learning)
- Explain the lifecycle of LLMs: pre-training, fine-tuning, and inference
- Know and work with different types of downstream tasks: Text classification, text similarity, search, question-answering, summarization, translation, and named entity recognition
- Construct prompts and effective completions with OpenAI APIs
- Understand the use cases for working with zero-shot and few-shot in-context learning approaches
- Fine-tune and evaluate models in with the Hugging Face Transformers library
Key information
Duration
- Part-time
- 1 months
- 5 hrs/week
Start dates & application deadlines
- StartingApply anytime.
Language
English
Delivered
Online
Campus Location
- Waterloo, Canada
Disciplines
Artificial Intelligence View 24 other Short Courses in Artificial Intelligence in CanadaWhat students do after studying
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Academic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
Prerequisites:
- Proficient in reading and writing code in Python. (understanding of different data types and the basics of object-oriented programming).
- Intermediate to advanced experience with data and machine learning Python libraries such as Numpy, Scikit-Learn, and Pandas.
- Comfortable working with web applications and big data.
- Experience working with API endpoints and the ability to write a simple Python code to send requests and parse responses from an endpoint.
- OpenAI account and API key
- Google Colab Pro
Who should enrol:
- Software engineers and developers
- Data analysts and data scientists
- Machine learning and artificial intelligence engineers
- Programmers and developers looking to apply NLP, machine learning, and prompt engineering to their stack
Tuition Fees
Tuition fees are shown in and the most likely applicable fee is shown based on your nationality.
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International Applies to you
Applies to youNon-residents1450 CAD / full≈ 1450 CAD / full -
Domestic Applies to you
Applies to youCitizens or residents1450 CAD / full≈ 1450 CAD / full
Funding
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