A 15-day email course on Generative AI

The 15-day Generative AI email course by Armand Ruiz, Director of AI at IBM, provides bite-sized lessons on essential topics. Whether you're new to Generative AI or desiring an informative refresher, this course maps out the key terms and ideas you need through sticky content and visuals.

An index of all the keywords referenced in the series:

  1. Artificial General Intelligence (AGI)
  2. Artificial Intelligence
  3. AI Engineer
  4. Auto-GPT 
  5. Base model
  6. BERT 
  7. Chatbots
  8. ChatGPT
  9. Chinchilla
  10. Classification 
  11. Content Generation
  12. Conversational AI 
  13. Convolutional Neural Network (CNNs) 
  14. Data Scientist
  15. Deep learning
  16. Diffusion 
  17. Discriminative Models
  18. EleutherAI
  19. Embeddings
  20. Fine Tuning
  21. Few-Shot Prompting
  22. Foundational model
  23. Generative AI
  24. Generative Adversarial Network (GAN) 
  25. Generative Models
  26. Generative Question-Answering (GQA).
  27. GPT 
  28. Granite 
  29. Graphics Processing Unit (GPU)
  30. Hyperparameters
  31. Insight Extraction
  32. LaMDA
  33. LangChain
  34. Large Language Model (LLM)
  35. Llama
  36. Low-Rank Adaptation (LoRA)
  37. Machine Learning
  38. Mistral 
  39. Model
  40. Multi-modal - By integrating text, image, sound, and more, AI can understand and respond to complex queries with unprecedented accuracy. 
  41. Named Entity Recognition
  42. Natural Language Processing (NLP)
  43. One-Shot Prompting
  44. Parameters
  45. Parameter Efficient Fine Tuning (PEFT) 
  46. Pre-trained models
  47. Predictive analytics 
  48. Prompt
  49. Prompt Engineering
  50. PyTorch
  51. Recommendation systems 
  52. Recurrent Neural Networks (RNN)
  53. Reinforcement Learning - By training the AI without data and by learning through trial and error, AI systems will become more autonomous and capable of solving complex, real-world problems. 
  54. Restricted Boltzmann Machines (RBMs)
  55. Retrieval Augmented Generation (RAG)
  56. Self-Attention Mechanism
  57. Semantic Search 
  58. Small Language Model (SLM)
  59. Supervised Learning
  60. Synthetic Data - If the limit to a better model is more data, artificial datasets can be created that can train AI without compromising privacy or relying on scarce real-world data. 
  61. TensorFlow
  62. The Pile
  63. Tokens
  64. Tokenization
  65. Traditional ML 
  66. Transformers
  67. Transformer Models
  68. Variational Autoencoders (VAEs)
  69. Vector
  70. Vector Database
  71. Vector Store
  72. Weights
  73. Zero Shot Prompting

W.I.P

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