Generative AI with Giant Language Fashions — New Palms-on Course by DeepLearning.AI and AWS


Voiced by Polly

Generative AI has taken the world by storm, and we’re beginning to see the subsequent 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, pictures, movies, and music. Generative AI is powered by very massive machine studying fashions which might be pre-trained on huge quantities of information, generally known as basis fashions (FMs).

A subset of FMs known as massive language fashions (LLMs) are skilled on trillions of phrases throughout many natural-language duties. These LLMs can perceive, be taught, and generate textual content that’s almost indistinguishable from textual content produced by people. And never solely that, LLMs also can interact in interactive conversations, reply questions, summarize dialogs and paperwork, and supply suggestions. They’ll energy purposes throughout many duties and industries together with inventive writing for advertising and marketing, summarizing paperwork for authorized, market analysis for monetary, simulating medical trials for healthcare, and code writing for software program growth.

Firms are transferring quickly to combine generative AI into their services and products. This will increase the demand for information scientists and engineers who perceive generative AI and how one can apply LLMs to unravel enterprise use instances.

This is the 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 training platform that prepares information scientists and engineers to develop into specialists in choosing, coaching, fine-tuning, and deploying LLMs for real-world purposes.

DeepLearning.AI was based in 2017 by machine studying and training pioneer Andrew Ng with the mission to develop and join the worldwide AI group by delivering world-class AI training.

Generative AI with large language models

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 information scientists and engineers who wish to learn to construct generative AI purposes with LLMs. We developed the content material for this course underneath the steering of Andrew Ng and with enter from numerous trade specialists and utilized scientists at Amazon, AWS, and Hugging Face.

Course Highlights
That is the primary complete Coursera course targeted on LLMs that particulars the standard generative AI challenge lifecycle, together with scoping the issue, selecting an LLM, adapting the LLM to your area, optimizing the mannequin for deployment, and integrating into enterprise purposes. The course not solely focuses on the sensible features of generative AI but in addition 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 Accomplice Vocareum allow you to apply the methods immediately in an AWS setting supplied with the course and consists of all sources 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 purposes. Let’s have a fast take a look at every week’s content material.

Week 1 – Generative AI use instances, challenge lifecycle, and mannequin pre-training
In week 1, you’ll look at the transformer structure that powers many LLMs, see how these fashions are skilled, and contemplate the compute sources required to develop them. Additionally, you will discover how one can 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 examine totally different prompts for a given generative job. 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 numerous sampling methods to achieve instinct on how one can enhance the generated mannequin responses.

Week 2 – Fantastic-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 sources—typically a single GPU. Additionally, you will be taught in regards to the metrics used to judge and examine the efficiency of LLMs.

Within the second lab, you’ll get hands-on with parameter-efficient fine-tuning (PEFT) and examine the outcomes to immediate engineering from the primary lab. This side-by-side comparability will enable you to acquire instinct into the qualitative and quantitative influence of various methods for adapting an LLM to your area particular datasets and use instances.

Week 3 – Fantastic-tuning with reinforcement studying from human suggestions (RLHF), retrieval-augmented technology (RAG), and LangChain
In week 3, 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 akin to retrieval-augmented technology (RAG) and libraries akin to LangChain that enable the LLM to combine with customized information sources and APIs to enhance the mannequin’s response additional.

Within the remaining 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 As we speak
Generative AI with massive language fashions is an on-demand, three-week course for information scientists and engineers who wish to learn to construct generative AI purposes with LLMs.

Enroll for generative AI with massive language fashions at this time.

— Antje



Latest articles

Related articles

Leave a reply

Please enter your comment!
Please enter your name here