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[] Udemy - Data Science Transformers for Natural Language Processing

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文件列表

  1. 4. Fine-Tuning (Intermediate)/9. Fine-Tuning Sentiment Analysis in Python.mp4 131MB
  2. 7. Question-Answering (Advanced)/13. From Logits to Answers in Python.mp4 121MB
  3. 9. Implement Transformers From Scratch (Advanced)/10. How to Train a Causal Language Model From Scratch.mp4 120MB
  4. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/14. POS Tagging & Custom Datasets (Solution).mp4 115MB
  5. 9. Implement Transformers From Scratch (Advanced)/13. Implement a Seq2Seq Transformer From Scratch for Language Translation (pt 3).mp4 109MB
  6. 13. Effective Learning Strategies for Machine Learning FAQ/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 108MB
  7. 4. Fine-Tuning (Intermediate)/10. Fine-Tuning Transformers with Custom Dataset.mp4 107MB
  8. 7. Question-Answering (Advanced)/7. Aligning the Targets in Python.mp4 103MB
  9. 3. Beginner's Corner/4. Sentiment Analysis in Python.mp4 97MB
  10. 7. Question-Answering (Advanced)/12. From Logits to Answers.mp4 96MB
  11. 9. Implement Transformers From Scratch (Advanced)/12. Implement a Seq2Seq Transformer From Scratch for Language Translation (pt 2).mp4 95MB
  12. 9. Implement Transformers From Scratch (Advanced)/11. Implement a Seq2Seq Transformer From Scratch for Language Translation (pt 1).mp4 94MB
  13. 9. Implement Transformers From Scratch (Advanced)/3. How to Implement Multihead Attention From Scratch.mp4 93MB
  14. 9. Implement Transformers From Scratch (Advanced)/7. Train and Evaluate Encoder From Scratch.mp4 89MB
  15. 3. Beginner's Corner/18. Zero-Shot Classification in Python.mp4 88MB
  16. 3. Beginner's Corner/6. Text Generation in Python.mp4 86MB
  17. 4. Fine-Tuning (Intermediate)/4. Models and Tokenizers in Python.mp4 84MB
  18. 13. Effective Learning Strategies for Machine Learning FAQ/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 80MB
  19. 3. Beginner's Corner/2. From RNNs to Attention and Transformers - Intuition.mp4 78MB
  20. 7. Question-Answering (Advanced)/9. Applying the Tokenizer in Python.mp4 77MB
  21. 7. Question-Answering (Advanced)/5. Using the Tokenizer in Python.mp4 72MB
  22. 12. Extra Help With Python Coding for Beginners FAQ/1. How to Code by Yourself (part 1).mp4 72MB
  23. 3. Beginner's Corner/10. Named Entity Recognition (NER) in Python.mp4 70MB
  24. 12. Extra Help With Python Coding for Beginners FAQ/3. Proof that using Jupyter Notebook is the same as not using it.mp4 69MB
  25. 7. Question-Answering (Advanced)/6. Aligning the Targets.mp4 69MB
  26. 3. Beginner's Corner/7. Masked Language Modeling (Article Spinner).mp4 67MB
  27. 3. Beginner's Corner/8. Masked Language Modeling (Article Spinner) in Python.mp4 67MB
  28. 4. Fine-Tuning (Intermediate)/3. Models and Tokenizers.mp4 65MB
  29. 8. Transformers and Attention Theory (Advanced)/3. Self-Attention & Scaled Dot-Product Attention.mp4 64MB
  30. 3. Beginner's Corner/14. Neural Machine Translation in Python.mp4 64MB
  31. 2. Getting Setup/2. How to use Github & Extra Coding Tips (Optional).mp4 64MB
  32. 4. Fine-Tuning (Intermediate)/2. Text Preprocessing and Tokenization Review.mp4 63MB
  33. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/6. Target Alignment (Code).mp4 62MB
  34. 4. Fine-Tuning (Intermediate)/5. Transfer Learning & Fine-Tuning (pt 1).mp4 60MB
  35. 4. Fine-Tuning (Intermediate)/8. Fine-Tuning Sentiment Analysis and the GLUE Benchmark.mp4 58MB
  36. 3. Beginner's Corner/5. Text Generation.mp4 57MB
  37. 4. Fine-Tuning (Intermediate)/7. Transfer Learning & Fine-Tuning (pt 3).mp4 57MB
  38. 4. Fine-Tuning (Intermediate)/13. Fine-Tuning Transformers with Multiple Inputs in Python.mp4 57MB
  39. 3. Beginner's Corner/3. Sentiment Analysis.mp4 54MB
  40. 11. Setting Up Your Environment FAQ/1. Anaconda Environment Setup.mp4 53MB
  41. 6. Seq2Seq and Neural Machine Translation (Intermediate)/7. Model Inputs (Code).mp4 51MB
  42. 1. Welcome/2. Outline.mp4 51MB
  43. 3. Beginner's Corner/1. Beginner's Corner Section Introduction.mp4 50MB
  44. 8. Transformers and Attention Theory (Advanced)/10. Decoder Architecture.mp4 50MB
  45. 4. Fine-Tuning (Intermediate)/6. Transfer Learning & Fine-Tuning (pt 2).mp4 49MB
  46. 12. Extra Help With Python Coding for Beginners FAQ/2. How to Code by Yourself (part 2).mp4 49MB
  47. 3. Beginner's Corner/16. Question Answering in Python.mp4 48MB
  48. 3. Beginner's Corner/12. Text Summarization in Python.mp4 45MB
  49. 7. Question-Answering (Advanced)/8. Applying the Tokenizer.mp4 45MB
  50. 7. Question-Answering (Advanced)/15. Computing Metrics in Python.mp4 44MB
  51. 11. Setting Up Your Environment FAQ/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 44MB
  52. 2. Getting Setup/1. Get Your Hands Dirty, Practical Coding Experience, Data Links.mp4 44MB
  53. 6. Seq2Seq and Neural Machine Translation (Intermediate)/9. Translation Metrics (BLEU Score & BERT Score) (Code).mp4 43MB
  54. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/4. Target Alignment (Code Preparation).mp4 43MB
  55. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/3. Data & Tokenizer (Code).mp4 43MB
  56. 2. Getting Setup/5. How to Succeed in This Course.mp4 41MB
  57. 4. Fine-Tuning (Intermediate)/11. Hugging Face AutoConfig.mp4 41MB
  58. 3. Beginner's Corner/15. Question Answering.mp4 40MB
  59. 14. Appendix FAQ Finale/2. BONUS.mp4 40MB
  60. 7. Question-Answering (Advanced)/3. Exploring the Dataset (SQuAD) in Python.mp4 40MB
  61. 8. Transformers and Attention Theory (Advanced)/11. Encoder-Decoder Architecture.mp4 40MB
  62. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/10. Metrics (Code).mp4 39MB
  63. 9. Implement Transformers From Scratch (Advanced)/8. How to Implement Causal Self-Attention From Scratch.mp4 39MB
  64. 13. Effective Learning Strategies for Machine Learning FAQ/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 39MB
  65. 7. Question-Answering (Advanced)/17. Train and Evaluate in Python.mp4 38MB
  66. 6. Seq2Seq and Neural Machine Translation (Intermediate)/5. Aside Seq2Seq Basics (Optional).mp4 37MB
  67. 8. Transformers and Attention Theory (Advanced)/2. Basic Self-Attention.mp4 37MB
  68. 9. Implement Transformers From Scratch (Advanced)/5. How to Implement Positional Encoding From Scratch.mp4 36MB
  69. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/1. Token Classification Section Introduction.mp4 36MB
  70. 6. Seq2Seq and Neural Machine Translation (Intermediate)/11. Train & Evaluate (Code).mp4 36MB
  71. 1. Welcome/1. Introduction.mp4 35MB
  72. 7. Question-Answering (Advanced)/4. Using the Tokenizer.mp4 35MB
  73. 6. Seq2Seq and Neural Machine Translation (Intermediate)/4. Data & Tokenizer (Code).mp4 34MB
  74. 8. Transformers and Attention Theory (Advanced)/6. Multi-Head Attention.mp4 34MB
  75. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/9. Metrics (Code Preparation).mp4 33MB
  76. 6. Seq2Seq and Neural Machine Translation (Intermediate)/6. Model Inputs (Code Preparation).mp4 32MB
  77. 8. Transformers and Attention Theory (Advanced)/13. GPT.mp4 31MB
  78. 3. Beginner's Corner/17. Zero-Shot Classification.mp4 30MB
  79. 8. Transformers and Attention Theory (Advanced)/14. GPT-2.mp4 30MB
  80. 8. Transformers and Attention Theory (Advanced)/7. Transformer Block.mp4 29MB
  81. 8. Transformers and Attention Theory (Advanced)/8. Positional Encodings.mp4 29MB
  82. 4. Fine-Tuning (Intermediate)/12. Fine-Tuning with Multiple Inputs (Textual Entailment).mp4 28MB
  83. 3. Beginner's Corner/13. Neural Machine Translation.mp4 28MB
  84. 9. Implement Transformers From Scratch (Advanced)/9. How to Implement a Transformer Decoder (GPT) From Scratch.mp4 27MB
  85. 3. Beginner's Corner/20. Suggestion Box.mp4 27MB
  86. 9. Implement Transformers From Scratch (Advanced)/6. How to Implement Transformer Encoder From Scratch.mp4 27MB
  87. 2. Getting Setup/4. Are You Beginner, Intermediate, or Advanced All are OK!.mp4 27MB
  88. 9. Implement Transformers From Scratch (Advanced)/1. Implementation Section Introduction.mp4 26MB
  89. 8. Transformers and Attention Theory (Advanced)/9. Encoder Architecture.mp4 25MB
  90. 7. Question-Answering (Advanced)/14. Computing Metrics.mp4 25MB
  91. 6. Seq2Seq and Neural Machine Translation (Intermediate)/2. Data & Tokenizer (Code Preparation).mp4 25MB
  92. 3. Beginner's Corner/11. Text Summarization.mp4 24MB
  93. 8. Transformers and Attention Theory (Advanced)/15. GPT-3.mp4 24MB
  94. 8. Transformers and Attention Theory (Advanced)/12. BERT.mp4 23MB
  95. 3. Beginner's Corner/19. Beginner's Corner Section Summary.mp4 23MB
  96. 9. Implement Transformers From Scratch (Advanced)/2. Encoder Implementation Plan & Outline.mp4 23MB
  97. 7. Question-Answering (Advanced)/11. Question-Answering Metrics in Python.mp4 23MB
  98. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/12. Model and Trainer (Code).mp4 22MB
  99. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/7. Data Collator (Code Preparation).mp4 22MB
  100. 3. Beginner's Corner/9. Named Entity Recognition (NER).mp4 22MB
  101. 8. Transformers and Attention Theory (Advanced)/4. Attention Efficiency.mp4 22MB
  102. 7. Question-Answering (Advanced)/1. Question-Answering Section Introduction.mp4 22MB
  103. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/13. POS Tagging & Custom Datasets (Exercise Prompt).mp4 21MB
  104. 6. Seq2Seq and Neural Machine Translation (Intermediate)/10. Train & Evaluate (Code Preparation).mp4 21MB
  105. 8. Transformers and Attention Theory (Advanced)/16. Theory Section Summary.mp4 21MB
  106. 4. Fine-Tuning (Intermediate)/1. Fine-Tuning Section Introduction.mp4 20MB
  107. 7. Question-Answering (Advanced)/2. Exploring the Dataset (SQuAD).mp4 20MB
  108. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/2. Data & Tokenizer (Code Preparation).mp4 19MB
  109. 6. Seq2Seq and Neural Machine Translation (Intermediate)/8. Translation Metrics (BLEU Score & BERT Score) (Code Preparation).mp4 19MB
  110. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/5. Create Tokenized Dataset (Code Preparation).mp4 18MB
  111. 6. Seq2Seq and Neural Machine Translation (Intermediate)/1. Translation Section Introduction.mp4 18MB
  112. 13. Effective Learning Strategies for Machine Learning FAQ/1. How to Succeed in this Course (Long Version).mp4 18MB
  113. 2. Getting Setup/3. Where to get the code, notebooks, and data.mp4 18MB
  114. 8. Transformers and Attention Theory (Advanced)/1. Theory Section Introduction.mp4 17MB
  115. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/8. Data Collator (Code).mp4 17MB
  116. 7. Question-Answering (Advanced)/10. Question-Answering Metrics.mp4 16MB
  117. 14. Appendix FAQ Finale/1. What is the Appendix.mp4 16MB
  118. 4. Fine-Tuning (Intermediate)/14. Fine-Tuning Section Summary.mp4 16MB
  119. 8. Transformers and Attention Theory (Advanced)/5. Attention Mask.mp4 15MB
  120. 9. Implement Transformers From Scratch (Advanced)/4. How to Implement the Transformer Block From Scratch.mp4 15MB
  121. 7. Question-Answering (Advanced)/18. Question-Answering Section Summary.mp4 14MB
  122. 7. Question-Answering (Advanced)/16. Train and Evaluate.mp4 14MB
  123. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/11. Model and Trainer (Code Preparation).mp4 11MB
  124. 9. Implement Transformers From Scratch (Advanced)/14. Implementation Section Summary.mp4 11MB
  125. 6. Seq2Seq and Neural Machine Translation (Intermediate)/12. Translation Section Summary.mp4 10MB
  126. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/15. Token Classification Section Summary.mp4 8MB
  127. 6. Seq2Seq and Neural Machine Translation (Intermediate)/3. Things Move Fast.mp4 6MB
  128. 13. Effective Learning Strategies for Machine Learning FAQ/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 33KB
  129. 7. Question-Answering (Advanced)/12. From Logits to Answers.srt 28KB
  130. 3. Beginner's Corner/2. From RNNs to Attention and Transformers - Intuition.srt 24KB
  131. 8. Transformers and Attention Theory (Advanced)/3. Self-Attention & Scaled Dot-Product Attention.srt 24KB
  132. 13. Effective Learning Strategies for Machine Learning FAQ/4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt 24KB
  133. 12. Extra Help With Python Coding for Beginners FAQ/1. How to Code by Yourself (part 1).srt 23KB
  134. 3. Beginner's Corner/4. Sentiment Analysis in Python.srt 21KB
  135. 4. Fine-Tuning (Intermediate)/3. Models and Tokenizers.srt 21KB
  136. 11. Setting Up Your Environment FAQ/1. Anaconda Environment Setup.srt 20KB
  137. 9. Implement Transformers From Scratch (Advanced)/10. How to Train a Causal Language Model From Scratch.srt 20KB
  138. 7. Question-Answering (Advanced)/6. Aligning the Targets.srt 19KB
  139. 4. Fine-Tuning (Intermediate)/9. Fine-Tuning Sentiment Analysis in Python.srt 19KB
  140. 7. Question-Answering (Advanced)/7. Aligning the Targets in Python.srt 19KB
  141. 4. Fine-Tuning (Intermediate)/2. Text Preprocessing and Tokenization Review.srt 18KB
  142. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/14. POS Tagging & Custom Datasets (Solution).srt 18KB
  143. 9. Implement Transformers From Scratch (Advanced)/13. Implement a Seq2Seq Transformer From Scratch for Language Translation (pt 3).srt 17KB
  144. 13. Effective Learning Strategies for Machine Learning FAQ/3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt 17KB
  145. 7. Question-Answering (Advanced)/13. From Logits to Answers in Python.srt 17KB
  146. 4. Fine-Tuning (Intermediate)/8. Fine-Tuning Sentiment Analysis and the GLUE Benchmark.srt 17KB
  147. 3. Beginner's Corner/18. Zero-Shot Classification in Python.srt 16KB
  148. 3. Beginner's Corner/7. Masked Language Modeling (Article Spinner).srt 16KB
  149. 11. Setting Up Your Environment FAQ/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 16KB
  150. 2. Getting Setup/2. How to use Github & Extra Coding Tips (Optional).srt 16KB
  151. 9. Implement Transformers From Scratch (Advanced)/3. How to Implement Multihead Attention From Scratch.srt 15KB
  152. 3. Beginner's Corner/5. Text Generation.srt 15KB
  153. 6. Seq2Seq and Neural Machine Translation (Intermediate)/5. Aside Seq2Seq Basics (Optional).srt 15KB
  154. 4. Fine-Tuning (Intermediate)/10. Fine-Tuning Transformers with Custom Dataset.srt 15KB
  155. 3. Beginner's Corner/1. Beginner's Corner Section Introduction.srt 15KB
  156. 9. Implement Transformers From Scratch (Advanced)/12. Implement a Seq2Seq Transformer From Scratch for Language Translation (pt 2).srt 15KB
  157. 12. Extra Help With Python Coding for Beginners FAQ/3. Proof that using Jupyter Notebook is the same as not using it.srt 15KB
  158. 3. Beginner's Corner/6. Text Generation in Python.srt 15KB
  159. 8. Transformers and Attention Theory (Advanced)/10. Decoder Architecture.srt 15KB
  160. 4. Fine-Tuning (Intermediate)/6. Transfer Learning & Fine-Tuning (pt 2).srt 15KB
  161. 3. Beginner's Corner/3. Sentiment Analysis.srt 15KB
  162. 13. Effective Learning Strategies for Machine Learning FAQ/1. How to Succeed in this Course (Long Version).srt 15KB
  163. 4. Fine-Tuning (Intermediate)/4. Models and Tokenizers in Python.srt 14KB
  164. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/4. Target Alignment (Code Preparation).srt 14KB
  165. 4. Fine-Tuning (Intermediate)/7. Transfer Learning & Fine-Tuning (pt 3).srt 14KB
  166. 1. Welcome/2. Outline.srt 14KB
  167. 9. Implement Transformers From Scratch (Advanced)/11. Implement a Seq2Seq Transformer From Scratch for Language Translation (pt 1).srt 13KB
  168. 12. Extra Help With Python Coding for Beginners FAQ/2. How to Code by Yourself (part 2).srt 13KB
  169. 7. Question-Answering (Advanced)/5. Using the Tokenizer in Python.srt 13KB
  170. 2. Getting Setup/5. How to Succeed in This Course.srt 13KB
  171. 4. Fine-Tuning (Intermediate)/5. Transfer Learning & Fine-Tuning (pt 1).srt 13KB
  172. 8. Transformers and Attention Theory (Advanced)/2. Basic Self-Attention.srt 12KB
  173. 9. Implement Transformers From Scratch (Advanced)/7. Train and Evaluate Encoder From Scratch.srt 12KB
  174. 7. Question-Answering (Advanced)/8. Applying the Tokenizer.srt 12KB
  175. 7. Question-Answering (Advanced)/9. Applying the Tokenizer in Python.srt 12KB
  176. 2. Getting Setup/1. Get Your Hands Dirty, Practical Coding Experience, Data Links.srt 12KB
  177. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/6. Target Alignment (Code).srt 12KB
  178. 8. Transformers and Attention Theory (Advanced)/11. Encoder-Decoder Architecture.srt 11KB
  179. 6. Seq2Seq and Neural Machine Translation (Intermediate)/6. Model Inputs (Code Preparation).srt 11KB
  180. 7. Question-Answering (Advanced)/4. Using the Tokenizer.srt 11KB
  181. 4. Fine-Tuning (Intermediate)/12. Fine-Tuning with Multiple Inputs (Textual Entailment).srt 10KB
  182. 3. Beginner's Corner/15. Question Answering.srt 10KB
  183. 3. Beginner's Corner/14. Neural Machine Translation in Python.srt 10KB
  184. 3. Beginner's Corner/10. Named Entity Recognition (NER) in Python.srt 10KB
  185. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/1. Token Classification Section Introduction.srt 10KB
  186. 8. Transformers and Attention Theory (Advanced)/7. Transformer Block.srt 10KB
  187. 8. Transformers and Attention Theory (Advanced)/8. Positional Encodings.srt 9KB
  188. 8. Transformers and Attention Theory (Advanced)/6. Multi-Head Attention.srt 9KB
  189. 3. Beginner's Corner/8. Masked Language Modeling (Article Spinner) in Python.srt 9KB
  190. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/3. Data & Tokenizer (Code).srt 9KB
  191. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/9. Metrics (Code Preparation).srt 9KB
  192. 8. Transformers and Attention Theory (Advanced)/13. GPT.srt 9KB
  193. 8. Transformers and Attention Theory (Advanced)/9. Encoder Architecture.srt 9KB
  194. 9. Implement Transformers From Scratch (Advanced)/1. Implementation Section Introduction.srt 8KB
  195. 9. Implement Transformers From Scratch (Advanced)/2. Encoder Implementation Plan & Outline.srt 8KB
  196. 8. Transformers and Attention Theory (Advanced)/14. GPT-2.srt 8KB
  197. 3. Beginner's Corner/13. Neural Machine Translation.srt 8KB
  198. 14. Appendix FAQ Finale/2. BONUS.srt 8KB
  199. 6. Seq2Seq and Neural Machine Translation (Intermediate)/7. Model Inputs (Code).srt 8KB
  200. 3. Beginner's Corner/17. Zero-Shot Classification.srt 8KB
  201. 3. Beginner's Corner/12. Text Summarization in Python.srt 8KB
  202. 6. Seq2Seq and Neural Machine Translation (Intermediate)/2. Data & Tokenizer (Code Preparation).srt 8KB
  203. 2. Getting Setup/4. Are You Beginner, Intermediate, or Advanced All are OK!.srt 7KB
  204. 3. Beginner's Corner/11. Text Summarization.srt 7KB
  205. 3. Beginner's Corner/16. Question Answering in Python.srt 7KB
  206. 8. Transformers and Attention Theory (Advanced)/1. Theory Section Introduction.srt 7KB
  207. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/13. POS Tagging & Custom Datasets (Exercise Prompt).srt 7KB
  208. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/2. Data & Tokenizer (Code Preparation).srt 7KB
  209. 7. Question-Answering (Advanced)/14. Computing Metrics.srt 7KB
  210. 4. Fine-Tuning (Intermediate)/13. Fine-Tuning Transformers with Multiple Inputs in Python.srt 7KB
  211. 8. Transformers and Attention Theory (Advanced)/15. GPT-3.srt 7KB
  212. 6. Seq2Seq and Neural Machine Translation (Intermediate)/4. Data & Tokenizer (Code).srt 6KB
  213. 3. Beginner's Corner/19. Beginner's Corner Section Summary.srt 6KB
  214. 6. Seq2Seq and Neural Machine Translation (Intermediate)/1. Translation Section Introduction.srt 6KB
  215. 6. Seq2Seq and Neural Machine Translation (Intermediate)/9. Translation Metrics (BLEU Score & BERT Score) (Code).srt 6KB
  216. 8. Transformers and Attention Theory (Advanced)/16. Theory Section Summary.srt 6KB
  217. 9. Implement Transformers From Scratch (Advanced)/5. How to Implement Positional Encoding From Scratch.srt 6KB
  218. 3. Beginner's Corner/9. Named Entity Recognition (NER).srt 6KB
  219. 4. Fine-Tuning (Intermediate)/1. Fine-Tuning Section Introduction.srt 6KB
  220. 8. Transformers and Attention Theory (Advanced)/12. BERT.srt 6KB
  221. 7. Question-Answering (Advanced)/1. Question-Answering Section Introduction.srt 6KB
  222. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/10. Metrics (Code).srt 6KB
  223. 7. Question-Answering (Advanced)/15. Computing Metrics in Python.srt 6KB
  224. 4. Fine-Tuning (Intermediate)/11. Hugging Face AutoConfig.srt 6KB
  225. 8. Transformers and Attention Theory (Advanced)/4. Attention Efficiency.srt 6KB
  226. 7. Question-Answering (Advanced)/2. Exploring the Dataset (SQuAD).srt 6KB
  227. 9. Implement Transformers From Scratch (Advanced)/8. How to Implement Causal Self-Attention From Scratch.srt 6KB
  228. 1. Welcome/1. Introduction.srt 6KB
  229. 6. Seq2Seq and Neural Machine Translation (Intermediate)/10. Train & Evaluate (Code Preparation).srt 6KB
  230. 7. Question-Answering (Advanced)/18. Question-Answering Section Summary.srt 5KB
  231. 8. Transformers and Attention Theory (Advanced)/5. Attention Mask.srt 5KB
  232. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/5. Create Tokenized Dataset (Code Preparation).srt 5KB
  233. 6. Seq2Seq and Neural Machine Translation (Intermediate)/8. Translation Metrics (BLEU Score & BERT Score) (Code Preparation).srt 5KB
  234. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/7. Data Collator (Code Preparation).srt 5KB
  235. 9. Implement Transformers From Scratch (Advanced)/9. How to Implement a Transformer Decoder (GPT) From Scratch.srt 5KB
  236. 3. Beginner's Corner/20. Suggestion Box.srt 5KB
  237. 9. Implement Transformers From Scratch (Advanced)/6. How to Implement Transformer Encoder From Scratch.srt 5KB
  238. 7. Question-Answering (Advanced)/10. Question-Answering Metrics.srt 5KB
  239. 7. Question-Answering (Advanced)/17. Train and Evaluate in Python.srt 5KB
  240. 7. Question-Answering (Advanced)/3. Exploring the Dataset (SQuAD) in Python.srt 5KB
  241. 6. Seq2Seq and Neural Machine Translation (Intermediate)/11. Train & Evaluate (Code).srt 5KB
  242. 2. Getting Setup/3. Where to get the code, notebooks, and data.srt 4KB
  243. 4. Fine-Tuning (Intermediate)/14. Fine-Tuning Section Summary.srt 4KB
  244. 14. Appendix FAQ Finale/1. What is the Appendix.srt 4KB
  245. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/8. Data Collator (Code).srt 4KB
  246. 7. Question-Answering (Advanced)/16. Train and Evaluate.srt 3KB
  247. 6. Seq2Seq and Neural Machine Translation (Intermediate)/12. Translation Section Summary.srt 3KB
  248. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/12. Model and Trainer (Code).srt 3KB
  249. 7. Question-Answering (Advanced)/11. Question-Answering Metrics in Python.srt 3KB
  250. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/11. Model and Trainer (Code Preparation).srt 3KB
  251. 5. Named Entity Recognition (NER) and POS Tagging (Intermediate)/15. Token Classification Section Summary.srt 3KB
  252. 9. Implement Transformers From Scratch (Advanced)/4. How to Implement the Transformer Block From Scratch.srt 2KB
  253. 6. Seq2Seq and Neural Machine Translation (Intermediate)/3. Things Move Fast.srt 2KB
  254. 9. Implement Transformers From Scratch (Advanced)/14. Implementation Section Summary.srt 2KB
  255. 10. Extras/1. Data Links.html 256B
  256. 2. Getting Setup/1.1 Data Links.html 157B
  257. 2. Getting Setup/3.2 Data Links.html 157B
  258. 2. Getting Setup/1.2 Github Link.html 145B
  259. 2. Getting Setup/3.3 Github Link.html 145B
  260. 0. Websites you may like/[FreeCourseSite.com].url 127B
  261. 13. Effective Learning Strategies for Machine Learning FAQ/0. Websites you may like/[FreeCourseSite.com].url 127B
  262. 4. Fine-Tuning (Intermediate)/0. Websites you may like/[FreeCourseSite.com].url 127B
  263. 8. Transformers and Attention Theory (Advanced)/0. Websites you may like/[FreeCourseSite.com].url 127B
  264. 2. Getting Setup/3.1 Code Link.html 125B
  265. 0. Websites you may like/[CourseClub.Me].url 122B
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