Building AI Applications Locally with Ollama - From Setup to Advanced Projects

Building AI Applications Locally with Ollama - From Setup to Advanced Projects

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⌨️ LLM Parameters Deep Dive

10 of 38

10 of 38

⌨️ LLM Parameters Deep Dive

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Classroom Contents

Building AI Applications Locally with Ollama - From Setup to Advanced Projects

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  1. 1 ⌨️ Intro
  2. 2 ⌨️ What Is this course about?
  3. 3 ⌨️ Course Prerequisites
  4. 4 ⌨️ Development Environment Setup
  5. 5 ⌨️ Ollama Deep Dive
  6. 6 ⌨️ Ollama Key Features
  7. 7 ⌨️ Ollama Setup
  8. 8 ⌨️ Download Ollama Locally
  9. 9 ⌨️ Ollama Models - How to Pull Different Ollama Models Locally
  10. 10 ⌨️ LLM Parameters Deep Dive
  11. 11 ⌨️ Understanding Model Benchmarks
  12. 12 ⌨️ Ollama Basic CLI Commands - Pull and Testing Models
  13. 13 ⌨️ Pull in the Llava Multimodal MOdel and Captioning an Image - Hands-on
  14. 14 ⌨️ Summarize and Sentiment Analysis and Customizing Models with the Modelfile
  15. 15 ⌨️ Ollama REST API
  16. 16 ⌨️ Ollama REST API - Request JSON
  17. 17 ⌨️ Ollama Models Support Different Tasks - Summary
  18. 18 ⌨️ Different Ways to Interact with Ollama Models - Overview
  19. 19 ⌨️ Ollama Model Running Under Msty App - Frontend Tool - RAG Hands-on
  20. 20 ⌨️ Introduction to Python Library for Building LLM Applications Locally
  21. 21 ⌨️ Interact with Llama3 in Python using Ollama REST API
  22. 22 ⌨️ Ollama Python Library Chatting with our Model
  23. 23 ⌨️ Chat Example with Streaming
  24. 24 ⌨️ Using Ollama Show Function
  25. 25 ⌨️ Create a Custom Model in Code
  26. 26 ⌨️ Build a Real-world Use case Application - Introduction
  27. 27 ⌨️ Build a LLM App - Grocery List Organizer
  28. 28 ⌨️ Building RAG Systems with Ollama - Overview of RAG Systems and Langchain Crash Course
  29. 29 ⌨️ Deep Dive into Vectorstore and Embeddings - the Whole Picture - Crash Course
  30. 30 ⌨️ Overview of Our PDF RAG System We will be Building
  31. 31 ⌨️ Set up our RAG System - Document Ingestion and Vector DB Creation and Embeddings
  32. 32 ⌨️ RAG System - Retrieval and Querying - Final
  33. 33 ⌨️ RAG System - Cleaner Code Code Refactoring
  34. 34 ⌨️ RAG System - Streamlit Version
  35. 35 ⌨️ BONUS for YOU!
  36. 36 ⌨️ Introduction to the Next Application - AI Recruiter Agency
  37. 37 ⌨️ Building the AI Recruiter Agency
  38. 38 ⌨️ Outro - Final Thoughts and Your Bonus - Thank you!

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