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Udemy Generative AI and Large Language Models

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Files:
  1. Get Bonus Downloads Here.url 180 bytes
  2. 1 -Introduction.mp4 59.1 MB
  3. 2 -2. Course Content.mp4 23.0 MB
  4. 1 -3. What is Generative AI.mp4 23.7 MB
  5. 2 -4. Example - Difference between GenAI and Discriminative AI.mp4 25.3 MB
  6. 3 -5. A review on Probability Terms, Bayes theorem.mp4 94.7 MB
  7. 4 -6. Case Study Introduction - Digit Recognition.mp4 15.4 MB
  8. 5 -7. Case Study Introduction - Digit Recognition (Contd.).mp4 40.7 MB
  9. 6 -8. Summary on GenAI.mp4 39.4 MB
  10. 1 -9. Introduction to LLMs.mp4 12.6 MB
  11. 2 -10. Understanding language is not easy.mp4 13.2 MB
  12. 3 -11. LLM Demo.mp4 17.3 MB
  13. 4 -12. What does an LLM do.mp4 18.7 MB
  14. 5 -13. Applications of an LLM.mp4 4.4 MB
  15. 1 -14. Introduction to the architecture used in LLM.mp4 36.9 MB
  16. 2 -15. Fully Connected Networks.mp4 133.0 MB
  17. 3 -16. Neural Networks are Function Approximators.mp4 62.7 MB
  18. 4 -17. Introduction to RNN.mp4 55.1 MB
  19. 5 -18. RNN - A deep dive.mp4 70.4 MB
  20. 6 -18.1 - Pretraining vs Finetuning.mp4 55.7 MB
  21. 1 -19. Introduction to Transformers - Tokenization.mp4 39.1 MB
  22. 2 -20. Python demo on Tokenization.mp4 36.4 MB
  23. 3 -21. Embedding - Words in the vector space.mp4 97.4 MB
  24. 4 -22. Overview on the working of the Encoder-Decoder.mp4 83.4 MB
  25. 5 -23. Self Attention - Full Explanation on the QKV Matrix.mp4 118.6 MB
  26. 6 -24. Embedding - Demo.mp4 50.7 MB
  27. 1 -25. Lab 1 - Building a chabot using Huggingface.mp4 92.2 MB
  28. 2 -26. Inferencing Parameters - top p, top k, temperature.mp4 57.6 MB
  29. 3 -27. Demo on Inferencing Parameters.mp4 33.2 MB
  30. 4 -28. Lab 2 - Sentiment Analysis.mp4 66.1 MB
  31. 5 -29. Lab 3 - Building a simple translator.mp4 45.1 MB
  32. 1 -30. Evaluation Metrics - The BLEU Score.mp4 75.8 MB
  33. 2 -31. Evaluation Metrics - The ROUGE score.mp4 35.4 MB
  34. 3 -32. In Context Learning - Zero shot, few shot, One shot Inferences.mp4 68.7 MB
  35. Bonus Resources.txt 70 bytes

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