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Generative AI has taken the world by storm, and we’re beginning to see the following wave of widespread adoption of AI with the potential for each buyer expertise and utility to be reinvented with generative AI. Generative AI permits you to to create new content material and concepts together with conversations, tales, photos, movies, and music. Generative AI is powered by very massive machine studying fashions which might be pre-trained on huge quantities of knowledge, generally known as basis fashions (FMs).
A subset of FMs known as massive language fashions (LLMs) are educated on trillions of phrases throughout many natural-language duties. These LLMs can perceive, study, and generate textual content that’s practically indistinguishable from textual content produced by people. And never solely that, LLMs can even have interaction in interactive conversations, reply questions, summarize dialogs and paperwork, and supply suggestions. They will energy functions throughout many duties and industries together with inventive writing for advertising, summarizing paperwork for authorized, market analysis for monetary, simulating scientific trials for healthcare, and code writing for software program growth.
Corporations are transferring quickly to combine generative AI into their services and products. This will increase the demand for knowledge scientists and engineers who perceive generative AI and tips on how to apply LLMs to resolve enterprise use circumstances.
For this reason I’m excited to announce that DeepLearning.AI and AWS are collectively launching a brand new hands-on course Generative AI with massive language fashions on Coursera’s schooling platform that prepares knowledge scientists and engineers to develop into consultants in deciding on, coaching, fine-tuning, and deploying LLMs for real-world functions.
DeepLearning.AI was based in 2017 by machine studying and schooling pioneer Andrew Ng with the mission to develop and join the worldwide AI group by delivering world-class AI schooling.
DeepLearning.AI teamed up with generative AI specialists from AWS together with Chris Fregly, Shelbee Eigenbrode, Mike Chambers, and me to develop and ship this course for knowledge scientists and engineers who need to discover ways to construct generative AI functions with LLMs. We developed the content material for this course beneath the steering of Andrew Ng and with enter from varied business consultants and utilized scientists at Amazon, AWS, and Hugging Face.
Course Highlights
That is the primary complete Coursera course centered on LLMs that particulars the everyday generative AI mission lifecycle, together with scoping the issue, selecting an LLM, adapting the LLM to your area, optimizing the mannequin for deployment, and integrating into enterprise functions. The course not solely focuses on the sensible features of generative AI but additionally highlights the science behind LLMs and why they’re efficient.
The on-demand course is damaged down into three weeks of content material with roughly 16 hours of movies, quizzes, labs, and additional readings. The hands-on labs hosted by AWS Companion Vocareum allow you to apply the methods immediately in an AWS surroundings supplied with the course and consists of all assets wanted to work with the LLMs and discover their effectiveness.
In simply three weeks, the course prepares you to make use of generative AI for enterprise and real-world functions. Let’s have a fast have a look at every week’s content material.
Week 1 – Generative AI use circumstances, mission lifecycle, and mannequin pre-training
In week 1, you’ll study the transformer structure that powers many LLMs, see how these fashions are educated, and contemplate the compute assets required to develop them. Additionally, you will discover tips on how to information mannequin output at inference time utilizing immediate engineering and by specifying generative configuration settings.
Within the first hands-on lab, you’ll assemble and evaluate totally different prompts for a given generative process. On this case, you’ll summarize conversations between a number of individuals. For instance, think about summarizing help conversations between you and your clients. You’ll discover immediate engineering methods, attempt totally different generative configuration parameters, and experiment with varied sampling methods to achieve instinct on tips on how to enhance the generated mannequin responses.
Week 2 – Nice-tuning, parameter-efficient fine-tuning (PEFT), and mannequin analysis
In week 2, you’ll discover choices for adapting pre-trained fashions to particular duties and datasets via a course of known as fine-tuning. A variant of fine-tuning, known as parameter environment friendly fine-tuning (PEFT), permits you to fine-tune very massive fashions utilizing a lot smaller assets—typically a single GPU. Additionally, you will study concerning the metrics used to guage and evaluate the efficiency of LLMs.
Within the second lab, you’ll get hands-on with parameter-efficient fine-tuning (PEFT) and evaluate the outcomes to immediate engineering from the primary lab. This side-by-side comparability will make it easier to achieve instinct into the qualitative and quantitative influence of various methods for adapting an LLM to your area particular datasets and use circumstances.
Week three – Nice-tuning with reinforcement studying from human suggestions (RLHF), retrieval-augmented era (RAG), and LangChain
In week three, you’ll make the LLM responses extra humanlike and align them with human preferences utilizing a way known as reinforcement studying from human suggestions (RLHF). RLHF is essential to bettering the mannequin’s honesty, harmlessness, and helpfulness. Additionally, you will discover methods resembling retrieval-augmented era (RAG) and libraries resembling LangChain that enable the LLM to combine with customized knowledge sources and APIs to enhance the mannequin’s response additional.
Within the ultimate lab, you’ll get hands-on with RLHF. You’ll fine-tune the LLM utilizing a reward mannequin and a reinforcement-learning algorithm known as proximal coverage optimization (PPO) to extend the harmlessness of your mannequin responses. Lastly, you’ll consider the mannequin’s harmlessness earlier than and after the RLHF course of to achieve instinct into the influence of RLHF on aligning an LLM with human values and preferences.
Enroll Right this moment
Generative AI with massive language fashions is an on-demand, three-week course for knowledge scientists and engineers who need to discover ways to construct generative AI functions with LLMs.
Enroll for generative AI with massive language fashions at this time.
— Antje
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