Udemy Generative AI and Large Language Models
Seeders : 8 Leechers : 8
| Torrent Hash : | 79AD7B3E863CE7C196EEB5DFED83A14DED636788 |
| Torrent Added : | at June 21, 2025, 10:32 p.m. in Other |
| Torrent Size : | 1.7 GB |
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Please Update (Trackers Info) Before Start " Udemy Generative AI and Large Language Models" Torrent Downloading to See Updated Seeders And Leechers for Batter Torrent Download Speed.Torrent File Content (3 files)
Udemy Generative AI and Large Language Models
Get Bonus Downloads Here.url -
1 -Introduction.mp4 -
2 -2. Course Content.mp4 -
1 -3. What is Generative AI.mp4 -
2 -4. Example - Difference between GenAI and Discriminative AI.mp4 -
3 -5. A review on Probability Terms, Bayes theorem.mp4 -
4 -6. Case Study Introduction - Digit Recognition.mp4 -
5 -7. Case Study Introduction - Digit Recognition (Contd.).mp4 -
6 -8. Summary on GenAI.mp4 -
1 -9. Introduction to LLMs.mp4 -
2 -10. Understanding language is not easy.mp4 -
3 -11. LLM Demo.mp4 -
4 -12. What does an LLM do.mp4 -
5 -13. Applications of an LLM.mp4 -
1 -14. Introduction to the architecture used in LLM.mp4 -
2 -15. Fully Connected Networks.mp4 -
3 -16. Neural Networks are Function Approximators.mp4 -
4 -17. Introduction to RNN.mp4 -
5 -18. RNN - A deep dive.mp4 -
6 -18.1 - Pretraining vs Finetuning.mp4 -
1 -19. Introduction to Transformers - Tokenization.mp4 -
2 -20. Python demo on Tokenization.mp4 -
3 -21. Embedding - Words in the vector space.mp4 -
4 -22. Overview on the working of the Encoder-Decoder.mp4 -
5 -23. Self Attention - Full Explanation on the QKV Matrix.mp4 -
6 -24. Embedding - Demo.mp4 -
1 -25. Lab 1 - Building a chabot using Huggingface.mp4 -
2 -26. Inferencing Parameters - top p, top k, temperature.mp4 -
3 -27. Demo on Inferencing Parameters.mp4 -
4 -28. Lab 2 - Sentiment Analysis.mp4 -
5 -29. Lab 3 - Building a simple translator.mp4 -
1 -30. Evaluation Metrics - The BLEU Score.mp4 -
2 -31. Evaluation Metrics - The ROUGE score.mp4 -
3 -32. In Context Learning - Zero shot, few shot, One shot Inferences.mp4 -
Bonus Resources.txt -
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Get Bonus Downloads Here.url -
180 bytes
1 -Introduction.mp4 -
59.1 MB
2 -2. Course Content.mp4 -
23.0 MB
1 -3. What is Generative AI.mp4 -
23.7 MB
2 -4. Example - Difference between GenAI and Discriminative AI.mp4 -
25.3 MB
3 -5. A review on Probability Terms, Bayes theorem.mp4 -
94.7 MB
4 -6. Case Study Introduction - Digit Recognition.mp4 -
15.4 MB
5 -7. Case Study Introduction - Digit Recognition (Contd.).mp4 -
40.7 MB
6 -8. Summary on GenAI.mp4 -
39.4 MB
1 -9. Introduction to LLMs.mp4 -
12.6 MB
2 -10. Understanding language is not easy.mp4 -
13.2 MB
3 -11. LLM Demo.mp4 -
17.3 MB
4 -12. What does an LLM do.mp4 -
18.7 MB
5 -13. Applications of an LLM.mp4 -
4.4 MB
1 -14. Introduction to the architecture used in LLM.mp4 -
36.9 MB
2 -15. Fully Connected Networks.mp4 -
133.0 MB
3 -16. Neural Networks are Function Approximators.mp4 -
62.7 MB
4 -17. Introduction to RNN.mp4 -
55.1 MB
5 -18. RNN - A deep dive.mp4 -
70.4 MB
6 -18.1 - Pretraining vs Finetuning.mp4 -
55.7 MB
1 -19. Introduction to Transformers - Tokenization.mp4 -
39.1 MB
2 -20. Python demo on Tokenization.mp4 -
36.4 MB
3 -21. Embedding - Words in the vector space.mp4 -
97.4 MB
4 -22. Overview on the working of the Encoder-Decoder.mp4 -
83.4 MB
5 -23. Self Attention - Full Explanation on the QKV Matrix.mp4 -
118.6 MB
6 -24. Embedding - Demo.mp4 -
50.7 MB
1 -25. Lab 1 - Building a chabot using Huggingface.mp4 -
92.2 MB
2 -26. Inferencing Parameters - top p, top k, temperature.mp4 -
57.6 MB
3 -27. Demo on Inferencing Parameters.mp4 -
33.2 MB
4 -28. Lab 2 - Sentiment Analysis.mp4 -
66.1 MB
5 -29. Lab 3 - Building a simple translator.mp4 -
45.1 MB
1 -30. Evaluation Metrics - The BLEU Score.mp4 -
75.8 MB
2 -31. Evaluation Metrics - The ROUGE score.mp4 -
35.4 MB
3 -32. In Context Learning - Zero shot, few shot, One shot Inferences.mp4 -
68.7 MB
Bonus Resources.txt -
70 bytes
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