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| 1 -14. Introduction to the architecture used in LLM.mp4 |
36.89Мб |
| 1 -19. Introduction to Transformers - Tokenization.mp4 |
39.06Мб |
| 1 -25. Lab 1 - Building a chabot using Huggingface.mp4 |
92.23Мб |
| 1 -3. What is Generative AI.mp4 |
23.74Мб |
| 1 -30. Evaluation Metrics - The BLEU Score.mp4 |
75.82Мб |
| 1 -9. Introduction to LLMs.mp4 |
12.57Мб |
| 1 -Introduction.mp4 |
59.06Мб |
| 2 -10. Understanding language is not easy.mp4 |
13.25Мб |
| 2 -15. Fully Connected Networks.mp4 |
133.01Мб |
| 2 -2. Course Content.mp4 |
23.03Мб |
| 2 -20. Python demo on Tokenization.mp4 |
36.37Мб |
| 2 -26. Inferencing Parameters - top p, top k, temperature.mp4 |
57.63Мб |
| 2 -31. Evaluation Metrics - The ROUGE score.mp4 |
35.41Мб |
| 2 -4. Example - Difference between GenAI and Discriminative AI.mp4 |
25.26Мб |
| 3 -11. LLM Demo.mp4 |
17.34Мб |
| 3 -16. Neural Networks are Function Approximators.mp4 |
62.71Мб |
| 3 -21. Embedding - Words in the vector space.mp4 |
97.43Мб |
| 3 -27. Demo on Inferencing Parameters.mp4 |
33.22Мб |
| 3 -32. In Context Learning - Zero shot, few shot, One shot Inferences.mp4 |
68.73Мб |
| 3 -5. A review on Probability Terms, Bayes theorem.mp4 |
94.69Мб |
| 4 -12. What does an LLM do.mp4 |
18.73Мб |
| 4 -17. Introduction to RNN.mp4 |
55.10Мб |
| 4 -22. Overview on the working of the Encoder-Decoder.mp4 |
83.40Мб |
| 4 -28. Lab 2 - Sentiment Analysis.mp4 |
66.11Мб |
| 4 -6. Case Study Introduction - Digit Recognition.mp4 |
15.41Мб |
| 5 -13. Applications of an LLM.mp4 |
4.38Мб |
| 5 -18. RNN - A deep dive.mp4 |
70.44Мб |
| 5 -23. Self Attention - Full Explanation on the QKV Matrix.mp4 |
118.57Мб |
| 5 -29. Lab 3 - Building a simple translator.mp4 |
45.11Мб |
| 5 -7. Case Study Introduction - Digit Recognition (Contd.).mp4 |
40.71Мб |
| 6 -18.1 - Pretraining vs Finetuning.mp4 |
55.65Мб |
| 6 -24. Embedding - Demo.mp4 |
50.66Мб |
| 6 -8. Summary on GenAI.mp4 |
39.43Мб |
| Bonus Resources.txt |
70б |
| Get Bonus Downloads Here.url |
180б |