CP
Reviewed Feb 28, 2024
Great overview of GPT with some labs and very recent information. Deep Learning training is recommended.
Great overview of GPT with some labs and very recent information. Deep Learning training is recommended.
Generative Pre-trained Transformers (GPT)
Large Language Models (LLMs), including GPT models that power applications such as ChatGPT, are transforming how people interact with technology and how computers understand and generate language. In this course, you'll explore the core concepts of natural language processing (NLP) and language modelling that underpin today's generative AI systems. You'll learn how language models are trained, how Transformer architectures revolutionised modern AI, and why they have become the foundation for a wide range of applications, from conversational assistants and content generation to summarisation, translation, and question answering. Along the way, you'll examine the strengths and limitations of LLMs, including topics such as hallucinations, evaluation, responsible AI, and the ethical considerations involved in developing and deploying these technologies. Through hands-on Python labs, you'll explore the building blocks of Transformer-based language models, experiment with text generation, and gain practical experience applying smaller language models to real-world tasks. Regular practice quizzes and interactive learning activities will reinforce key concepts and help prepare you for the graded assessments. Whether you're looking to understand how modern LLMs work or build a foundation for working with generative AI, this course provides the knowledge and practical experience to get started.
Status: Generative Model Architectures
Status: Natural Language Processing
IntermediateCourse13 hours
Featured reviews
RH
Reviewed Jan 20, 2024
I liked the course, It was informative with a little of coding assignments. The coding assignments could be a bit more in depth.
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christophe
5.0
Reviewed Feb 29, 2024Joris
4.0
Reviewed Dec 21, 2023good: tough tests bad: - course materials not available as pdf - nothing on algorithmic efficiency, scaling laws or emergence ugly: - w1: it's not said that it focuses on causal language models, 2nd exercise lacks method signature, smoothing not explained - w2: limitation of perplexity to causal language models not explained (cfr https://huggingface.co/docs/transformers/perplexity) - w3: sound quality, free article instead of nyt (https://arstechnica.com/tech-policy/2023/06/lawyers-have-real-bad-day-in-court-after-citing-fake-cases-made-up-by-chatgpt/), rlhf follows fine-tuning
Anders
4.0
Reviewed Oct 5, 2023A really good course, especially the first two weeks. The third week is also okay, but completely different and not very technical. The course is a bit rough around the edges: a few quiz questions are ambiguously formulated and the Python notebooks sometimes have typos. Still, a good course to learn about language models and to some get hands-on experience.
Milad
1.0
Reviewed Nov 10, 2023I want to gain hands-on experience with GPT rather than knowing the things mentioned in Module 3. The whole course is a waste of time! I'd rather take other courses!
Daniel
5.0
Reviewed May 14, 2024Good course overall. The video explanations could be a little more in-depth. And I think they could explore more labs than dedicating the entire last week to non-technical questions, or plus one more week maybe.
Roel
5.0
Reviewed Jan 21, 2024I liked the course, It was informative with a little of coding assignments. The coding assignments could be a bit more in depth.
Sabri
5.0
Reviewed Dec 3, 2023Excellent !!!
marco
4.0
Reviewed Mar 9, 2024get into more details of transformers models
John
2.0
Reviewed Oct 28, 2024Lots of gaps and code mistakes and little to no discussion.