Complete schedule along with Code Notebooks, Whitepapers to learn about Foundational Models, various LLMs, and GenAI. For more information and reference please refer to 5 Day Gen AI Intensive Course with Google
🎒 Complete the Intro Unit – “Foundational Large Language Models & Text Generation”, which is: [Optional] Listen to the summary podcast episode for this unit created by NotebookLM. Read the Foundational Large Language Models & Text Generation whitepaper.
Complete Unit 1 – “Prompt Engineering”, which is: [Optional] Listen to the summary podcast episode for this unit created by NotebookLM. Read the Prompt Engineering whitepaper. Complete this code lab on Kaggle where you’ll learn prompting fundamentals. Make sure you phone verify your account before starting, it's necessary for the code labs.
💡 What You’ll Learn Today you’ll explore the evolution of LLMs, from transformers to techniques like fine-tuning and inference acceleration. You’ll also get trained in the art of prompt engineering for optimal LLM interaction. The code lab will walk you through getting started with the Gemini API and cover several prompt techniques and how different parameters impact the prompts.
🎒 Complete Unit 2: “Embeddings and Vector Stores/Databases”, which is: [Optional] Listen to the summary podcast episode for this unit created by NotebookLM. Read the Embeddings and Vector Stores/Databases whitepaper.
Complete these code labs on Kaggle:
- Build a RAG question-answering system over custom documents
- Explore text similarity with embeddings
- Build a neural classification network with Keras using embeddings
💡 What You’ll Learn Today you will learn about the conceptual underpinning of embeddings and vector databases and how they can be used to bring live or specialist data into your LLM application. You’ll also explore their geometrical powers for classifying and comparing textual data.
🎒 Today’s Assignments
Complete Unit 3: “Generative AI Agents”, which is: [Optional] Listen to the summary podcast episode for this unit created by NotebookLM. Read the Generative AI Agents whitepaper.
Complete these code labs on Kaggle:
💡 What You’ll Learn Learn to build sophisticated AI agents by understanding their core components and the iterative development process. The code labs cover how to connect LLMs to existing systems and to the real world. Learn about function calling by giving SQL tools to a chatbot, and learn how to build a LangGraph agent that takes orders in a café.
🎒 Today’s Assignment
Complete Unit 4: “Domain-Specific LLMs”, which is:
[Optional] Listen to the summary podcast episode for this unit created by NotebookLM. Read the Solving Domain-Specific Problems Using LLMs whitepaper. Complete these code labs on Kaggle:
💡 What You’ll Learn In today’s reading, you’ll delve into the creation and application of specialized LLMs like SecLM and MedLM/Med-PaLM, with insights from the researchers who built them. In the code labs you will learn how to add real world data to a model beyond its knowledge cut-off by grounding with Google Search. You will also learn how to fine-tune a custom Gemini model using your own labeled data to solve custom tasks.
🎒 Final Assignment
Complete Unit 5: “MLOps for Generative AI”, which is:
[Optional] Listen to the summary podcast episode for this unit created by NotebookLM. Read the MLOps for Generative AI whitepaper. No code lab for today! We will do a code walkthrough and live demo of goo.gle/e2e-gen-ai-app-starter-pack, a resource created for making MLOps for Gen AI easier and accelerating the path to production. Please go through the repository in advance.
💡 What You’ll Learn Discover how to adapt MLOps practices for Generative AI and leverage Vertex AI's tools for foundation models and generative AI applications.
Complete List of all the daily livestream for 5-Day Gen AI Intensive Course