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Learner Reviews & Feedback for Generative AI with Large Language Models by DeepLearning.AI

4.8
stars
3,540 ratings

About the Course

In Generative AI with Large Language Models (LLMs), you’ll learn the fundamentals of how generative AI works, and how to deploy it in real-world applications. By taking this course, you'll learn to: - Deeply understand generative AI, describing the key steps in a typical LLM-based generative AI lifecycle, from data gathering and model selection, to performance evaluation and deployment - Describe in detail the transformer architecture that powers LLMs, how they’re trained, and how fine-tuning enables LLMs to be adapted to a variety of specific use cases - Use empirical scaling laws to optimize the model's objective function across dataset size, compute budget, and inference requirements - Apply state-of-the art training, tuning, inference, tools, and deployment methods to maximize the performance of models within the specific constraints of your project - Discuss the challenges and opportunities that generative AI creates for businesses after hearing stories from industry researchers and practitioners Developers who have a good foundational understanding of how LLMs work, as well the best practices behind training and deploying them, will be able to make good decisions for their companies and more quickly build working prototypes. This course will support learners in building practical intuition about how to best utilize this exciting new technology. This is an intermediate course, so you should have some experience coding in Python to get the most out of it. You should also be familiar with the basics of machine learning, such as supervised and unsupervised learning, loss functions, and splitting data into training, validation, and test sets. If you have taken the Machine Learning Specialization or Deep Learning Specialization from DeepLearning.AI, you’ll be ready to take this course and dive deeper into the fundamentals of generative AI....

Top reviews

YA

Nov 6, 2023

I learned so much through the material and topics presented during this course. The topics are explained in detail and easy-to-understand way, and the labs and quizzes solidify the learning.

MB

Jan 4, 2024

This course has been an extra addition in enhancing my understanding of the Generative AI project lifecycle, particularly in the context of architecture and implementation strategies.

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651 - 675 of 844 Reviews for Generative AI with Large Language Models

By Shuxiang Z

•

Jul 24, 2023

Loved it!

By Maciej J

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Jan 9, 2024

Awesome!

By David G G G

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Jun 29, 2023

Amazing!

By akula j

•

Sep 17, 2024

helpful

By Abdullah B

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Mar 20, 2024

Perfect

By Aminah N

•

Dec 19, 2024

useful

By Vipul C H

•

Nov 30, 2023

thanks

By Praveen H

•

Sep 25, 2023

superb

By Justin H

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Sep 1, 2023

Brutal

By Николай Б

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Jul 30, 2023

Greate

By zaidiabbas786 A

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Apr 21, 2025

teert

By Adarsh51

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Mar 2, 2025

Nice!

By Egies R F

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Feb 23, 2025

goodd

By Simone L

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Aug 21, 2023

Super

By mehmet o

•

Aug 5, 2023

great

By SUBHADEEP C

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Oct 25, 2025

good

By Pooja S K

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Sep 21, 2025

Good

By Afiga

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Sep 11, 2025

Good

By ABEER H M

•

Aug 27, 2024

شكرا

By Khaoula E

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Mar 30, 2024

good

By Buri B

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Mar 3, 2024

nice

By Nivrutti R P

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Feb 25, 2024

good

By zed a

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Jan 24, 2024

good

By Padma M

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Dec 11, 2023

good

By Fraz

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Dec 10, 2023

All the instructors were good and delivery was mostly excellent, however, the course was a bit too short can be improved in several ways. There were very few quizes in the video lectures and the ones that were present, were too easy or obvious (does not require much thinking). There should be good, quality quizes in most video lessons similar to the OG ML course by Andrew Ng. The inline quizes in videos help "reinforce" the learning in humans. This is proven by the research yet to be carried out :D Another aspect that I did not like was the jupyter notebooks to run excercises, all solutions were already provided and it does not help in learning the concepts if all we have to do is to press Shift+Enter and merely observe code and results. Actual learning requires some trail and error as part of the exercises, once again the OG ML course by Andrew Ng did a good job of accomplishing this with Octave exercises.