GenAI Lesson 52 – Monitoring | Dataplexa

Generative AI Course

I. GenAI Basics
1. What is GenAI? 2. Evolution of AI 3. Foundation Models 4. Gen vs Disc 5. Key Concepts 6. GenAI Pipeline 7. Train vs Infer 8. Safety & Bias 9. Compute Infra 10. Applications 11. Word Embeddings 12. Sentence Embeddings 13. Document Embeddings 14. OpenAI Embeddings 15. Similarity Search
II. Generative Models
16. Vector DBs 17. ChromaDB 18. Pinecone 19. Autoencoders 20. VAE 21. GAN Basics 22. DCGAN 23. CycleGAN 24. Diffusion 25. Denoising 26. Latent Space 27. Image Generation 28. Eval Metrics 29. Transformers 30. Self-Attention
III. Transformers & LLMs
31. Positional Encode 32. Enc-Dec 33. Decoder-Only 34. BERT 35. GPT 36. Tokenization 37. Train LLMs 38. Instruction FT 39. RLHF 40. LoRA 41. Quantization 42. Function Calling 43. Multimodal Models 44. RAG Intro 45. RAG Architecture
IV. RAG + Real-World Applications
46. Chunking 47. Indexing 48. Prompt Cache 49. Agents 50. Memory 51. Evaluation 52. Monitoring 53. Cost Optimize 54. Chatbots 55. Text Gen 56. Image Gen 57. Audio Gen 58. Video Gen 59. Prompt Programming 60. Final GenAI Project
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