Introduction
Uncover the necessities of crafting Generative AI purposes via Microsoft’s Generative AI 18-lesson course for freshmen. Every section contains a succinct video introduction, detailed written classes out there within the README, and Python and TypeScript Code Samples suitable with Azure OpenAI and OpenAI API. Moreover, entry supplementary assets to additional improve your information on this dynamic subject.
Every lesson covers its matter, so begin wherever you want to. Classes are labeled both “Be taught” classes explaining a Generative AI idea or “Construct” classes explaining an idea and code examples in Python and TypeScript when doable. Every lesson additionally features a “Hold Studying” part with extra studying instruments. Additional, on this weblog you’ll get to know 18 Microsoft free programs on Gen AI
Conditions
- Entry to the Azure OpenAI Service or OpenAI API – Solely required to finish coding classes
- Fundamental information of Python or Typescript is useful – *For absolute freshmen take a look at these Python and TypeScript programs.
- A Github Account to fork this complete repo to your individual GitHub account
They’ve created a Course Setup lesson that can assist you with organising your developement surroundings.
Establishing for the Microsoft Free Programs
Fork this Repo
Fork this complete repo to your individual GitHub account to have the ability to change any code and full the challenges. It’s also possible to star (🌟) this repo to search out it and associated repos simpler.
Create a Codespace
To keep away from any dependency points when operating the code, we advocate operating this course in a GitHub codespace.
This may be created by deciding on the Code possibility in your forked model of this repo and deciding on the Codespaces possibility.
Storing Your API Keys
Retaining your API keys secure and safe is vital when constructing any sort of utility. We encourage you to not retailer any API keys straight within the code you might be working with as committing these particulars to a public repository might lead to undesirable prices and points.
Tips on how to Run regionally in your laptop
To run the code regionally in your laptop, you would want to have some model of Python put in.
To then use the repository, that you must clone it:
git clone https://github.com/microsoft/generative-ai-for-beginners
cd generative-ai-for-beginners
Now you’ve gotten the whole lot checked out and may begin studying and work with the code.
Microsoft Free Programs for Gen AI Studying
Listed below are the Microsoft Free Programs on Gen AI you will need to know:
Course 1: Introduction to Generative AI and LLMs
This lesson will cowl:
- Introduction to the enterprise situation: our startup concept and mission.
- Generative AI and the way we landed on the present know-how panorama.
- Internal working of a big language mannequin.
- Major capabilities and sensible use instances of Massive Language Fashions.
Studying Targets
After finishing this lesson, you’ll perceive:
- What generative AI is and the way Massive Language Fashions work.
- How one can leverage massive language fashions for various use instances, with a deal with schooling eventualities.
Course 2: Exploring and evaluating totally different LLMs
This lesson will cowl:
- Several types of LLMs within the present panorama.
- Testing, iterating, and evaluating totally different fashions on your use case in Azure.
- Tips on how to deploy an LLM.
Studying Targets
After finishing this lesson, it is possible for you to to:
- Choose the proper mannequin on your use case.
- Perceive learn how to check, iterate, and enhance efficiency of your mannequin.
- Know the way companies deploy fashions.
Course 3: Utilizing Generative AI Responsibly
This lesson will cowl:
- Why you must prioritize Accountable AI when constructing Generative AI purposes.
- Core rules of Accountable AI and the way they relate to Generative AI.
- Tips on how to put these Accountable AI rules into follow via technique and tooling.
Studying Targets
After finishing this lesson you’ll know:
- The significance of Accountable AI when constructing Generative AI purposes.
- When to suppose and apply the core rules of Accountable AI when constructing Generative AI purposes.
- What instruments and methods can be found to you to place the idea of Accountable AI into follow.
Course 4: Understanding Immediate Engineering Fundamentals
On this lesson, we study what Immediate Engineering is, why it issues, and the way we will craft simpler prompts for a given mannequin and utility goal. We’ll perceive core ideas and finest practices for immediate engineering – and find out about an interactive Jupyter Notebooks “sandbox” surroundings the place we will see these ideas utilized to actual examples.
By the top of this lesson we can:
- Clarify what immediate engineering is and why it issues.
- Describe the parts of a immediate and the way they’re used.
- Be taught finest practices and methods for immediate engineering.
- Apply realized methods to actual examples, utilizing an OpenAI endpoint.
Course 5: Creating Superior Prompts
On this chapter, we are going to cowl the next matters:
- Lengthen your information of immediate engineering by making use of totally different methods to your prompts.
- Configuring your prompts to fluctuate the output.
Studying Targets
After finishing this lesson, you’ll have the ability to:
- Apply immediate engineering methods that enhance the result of your prompts.
- Carry out prompting that’s both various or deterministic.
Course 6: Constructing Textual content Era Functions
On this chapter, you’ll:
- Be taught concerning the openai library and it’s core ideas.
- Construct a textual content technology app utilizing openai.
- Perceive learn how to use ideas like immediate, temperature, and tokens to construct a textual content technology app.
Studying Targets
On the finish of this lesson, you’ll have the ability to:
- Clarify what a textual content technology app is.
- Construct a textual content technology app utilizing openai.
- Configure your app to make use of kind of tokens and likewise change the temperature, for a various output.
Course 7: Constructing Chat Functions
This lesson covers:
- Strategies for effectively constructing and integrating chat purposes.
- Tips on how to apply customization and fine-tuning to purposes.
- Methods and issues to successfully monitor chat purposes.
Studying Targets
By the top of this lesson, you’ll have the ability to:
- Describe issues for constructing and integrating chat purposes into current methods.
- Customise chat purposes for particular use-cases.
- Establish key metrics and issues to watch and preserve the standard of AI-powered chat purposes successfully.
- Guarantee chat purposes leverage AI responsibly.
Course 8: Constructing Search Apps Vector Databases
On this lesson, we are going to cowl:
- Semantic vs Key phrase search.
- What are Textual content Embeddings.
- Making a Textual content Embeddings Index.
- Looking a Textual content Embeddings Index.
Studying Targets
After finishing this lesson, it is possible for you to to:
- Inform the distinction between semantic and key phrase search.
- Clarify what Textual content Embeddings are.
- Create an utility utilizing Embeddings to seek for information.
Course 9: Constructing Picture Era Functions
On this lesson, we are going to cowl:
- Picture technology and why it’s helpful.
- DALL-E and Midjourney, what they’re, and the way they work.
- How you’d construct a picture technology app.
Studying Targets
After finishing this lesson, it is possible for you to to:
- Construct a picture technology utility.
- Outline boundaries on your utility with meta prompts.
- Work with DALL-E and Midjourney.
Course 10: Constructing Low Code AI Functions
This lesson covers:
- Introduction to Generative AI in Energy Platform
- Introduction to Copilot and learn how to use it
- Utilizing Generative AI to construct apps and flows in Energy Platform
- Understanding the AI Fashions in Energy Platform with AI Builder
Studying Targets
By the top of this lesson, it is possible for you to to:
- Perceive how Copilot works in Energy Platform.
- Construct a Pupil Project Tracker App for our schooling startup.
- Construct an Bill Processing Move that makes use of AI to extract info from invoices.
- Apply finest practices when utilizing the Create Textual content with GPT AI Mannequin.
Course 11: Integrating Exterior Functions with Operate Calling
This lesson will cowl:
- Clarify what’s operate calling and its use instances.
- Making a operate name utilizing Azure OpenAI.
- Tips on how to combine a operate name into an utility.
Studying Targets
After finishing this lesson it is possible for you to to:
- Clarify the aim of utilizing operate calling.
- Setup Operate Name utilizing the Azure Open AI Service.
- Design efficient operate calls on your utility’s use case.
Course 12: Designing UX for AI Functions
The lesson will cowl the next areas:
- Introduction to Person Expertise and Understanding Person Wants
- Designing AI Functions for Belief and Transparency
- Designing AI Functions for Collaboration and Suggestions
Studying Targets
After taking this lesson, you’ll have the ability to:
- Perceive learn how to construct AI purposes that meet the person wants.
- Design AI purposes that promote belief and collaboration.
Course 13: Securing Your Generative AI Functions
This lesson will cowl:
- Safety throughout the context of AI methods.
- Widespread dangers and threats to AI methods.
- Strategies and issues for securing AI methods.
Studying Targets
After finishing this lesson, you’ll have an understanding of:
- The threats and dangers to AI methods.
- Widespread strategies and practices for securing AI methods.
- How implementing safety testing can forestall surprising outcomes and erosion of person belief.
Course 14: The Generative AI Utility Lifecycle
On this chapter, you’ll:
- Perceive the Paradigm Shift from MLOps to LLMOps
- The LLM Lifecycle
- Lifecycle Tooling
- Lifecycle Metrification and Analysis
Course 15: Retrieval Augmented Era (RAG) and Vector Databases
On this lesson we are going to cowl the next:
- An introduction to RAG, what it’s and why it’s utilized in AI (synthetic intelligence).
- Understanding what vector databases are and creating one for our utility.
- A sensible instance on learn how to combine RAG into an utility.
Studying Targets
After finishing this lesson, it is possible for you to to:
- Clarify the importance of RAG in information retrieval and processing.
- Setup RAG utility and floor your information to an LLM
- Efficient integration of RAG and Vector Databases in LLM Functions.
Course 16: Open Supply Fashions and Hugging Face
Studying Targets
- Achieve an understanding of open supply Fashions
- Understanding the advantages of working with open supply Fashions
- Exploring the open fashions out there on Hugging Face and the Azure AI Studio
Course 17: AI Brokers
AI Brokers signify an thrilling improvement in Generative AI, enabling Massive Language Fashions (LLMs) to evolve from assistants into brokers able to taking actions. AI Agent frameworks allow builders to create purposes that give LLMs entry to instruments and state administration. These frameworks additionally improve visibility, permitting customers and builders to watch the actions deliberate by LLMs, thereby bettering expertise administration.
The lesson will cowl the next areas:
- Understanding what an AI Agent is – What precisely is an AI Agent?
- Exploring 4 totally different AI Agent Frameworks – What makes them distinctive?
- Making use of these AI Brokers to totally different use instances – When ought to we use AI Brokers?
Studying Targets
After taking this lesson, you’ll have the ability to:
- Clarify what AI Brokers are and the way they can be utilized.
- Have an understanding of the variations between a few of the standard AI Agent Frameworks, and the way they differ.
- Perceive how AI Brokers operate in an effort to construct purposes with them.
Course 18: Nice-Tuning LLMs
This lesson introduces the idea of fine-tuning for pre-trained language fashions, explores the advantages and challenges of this method, and supplies steerage on when and learn how to use wonderful tuning to enhance the efficiency of your generative AI fashions.
Studying Targets
- What is okay tuning for language fashions?
- When, and why, is okay tuning helpful?
- How can I fine-tune a pre-trained mannequin?
- What are the restrictions of fine-tuning?
After finishing this lesson, take a look at our Generative AI Studying assortment to proceed leveling up your Generative AI information! Congratsulations!! You have got accomplished the ultimate lesson from the v2 collection for this course! Don’t cease studying and constructing. Take a look at the Sources web page for an inventory of extra recommendations for simply this matter.
Conclusion
The given Microsoft Free Programs on Generative AI marks a big stride in the direction of democratizing entry to cutting-edge know-how schooling. These programs promise to empower learners with the foundational information and sensible expertise wanted to delve into the realm of AI-driven creativity. By offering these Microsoft free programs, Microsoft demonstrates its dedication to fostering innovation and equipping people worldwide with the instruments to harness the potential of Generative AI for various purposes.
Apart from Microsoft Free Programs, you may unlock your potential with the GenAI Pinnacle Program! Elevate your AI experience via revolutionary studying and improvement. Expertise personalised 1:1 mentorship with industry-leading Generative AI consultants, dive deep into a sophisticated curriculum that includes over 200 hours of immersive studying, and grasp 26+ cutting-edge GenAI instruments and libraries. Don’t simply study AI, pioneer its future with GenAI Pinnacle!