DeepLearning.AI and AWS unveiled a new course referred to as Generative AI with Massive Language Fashions on Coursera.
This hands-on course goals to equip knowledge scientists and engineers with the abilities wanted to grow to be proficient in using giant language fashions (LLMs) for sensible purposes. Members will achieve experience in numerous features, together with deciding on acceptable fashions, coaching them successfully, fine-tuning their efficiency, and deploying them for real-world situations.
It gives a complete exploration of LLMs throughout the context of generative AI tasks, protecting your complete lifecycle of a typical generative AI challenge, encompassing essential steps corresponding to downside scoping, LLM choice, area adaptation, mannequin optimization for deployment, and integration into enterprise purposes. The course not solely emphasizes sensible features but in addition delves into the scientific foundations behind LLMs and their effectiveness.
The course is designed to be versatile and self-paced, divided into three weeks of content material that totals roughly 16 hours. It consists of a wide range of studying supplies, corresponding to movies, quizzes, labs, and supplementary readings. The hands-on labs, facilitated by AWS Associate Vocareum, enable members to straight apply the methods in an AWS surroundings particularly supplied for the course. All the mandatory assets for working with LLMs and exploring their efficacy are included.
Week 1 of the course will cowl generative AI use instances, challenge lifecycle, and mannequin pre-training the place college students will study the transformer structure that powers many LLMs, see how these fashions are skilled, and take into account the compute assets required to develop them.
Week 2 covers the choices for adapting pre-trained fashions to particular duties and datasets by means of a course of referred to as fine-tuning.
Lastly, Week 3 would require customers to make the LLM responses extra humanlike and align them with human preferences utilizing a method referred to as reinforcement studying from human suggestions.
