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[FreeCourseSite.com] Udemy - Data Science Transformers for Natural Language Processing

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视频 2023-3-27 15:35 2024-11-23 04:40 246 3.69 GB 84
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FreeCourseSite com Udemy - Data Science Transformers for Natural Language Processing

文件列表
  1. 01 - Welcome/001 Introduction.mp434.6MB
  2. 01 - Welcome/002 Outline.mp450.65MB
  3. 02 - Getting Setup/001 Where to get the code and data.mp440.57MB
  4. 02 - Getting Setup/002 How to use Github & Extra Coding Tips (Optional).mp450.83MB
  5. 02 - Getting Setup/003 Are You Beginner, Intermediate, or Advanced All are OK!.mp426.73MB
  6. 03 - Beginner's Corner/001 Beginner's Corner Section Introduction.mp449.75MB
  7. 03 - Beginner's Corner/002 From RNNs to Attention and Transformers - Intuition.mp478.2MB
  8. 03 - Beginner's Corner/003 Sentiment Analysis.mp453.6MB
  9. 03 - Beginner's Corner/004 Sentiment Analysis in Python.mp497.04MB
  10. 03 - Beginner's Corner/005 Text Generation.mp457.1MB
  11. 03 - Beginner's Corner/006 Text Generation in Python.mp486.34MB
  12. 03 - Beginner's Corner/007 Masked Language Modeling (Article Spinner).mp467.29MB
  13. 03 - Beginner's Corner/008 Masked Language Modeling (Article Spinner) in Python.mp467.09MB
  14. 03 - Beginner's Corner/009 Named Entity Recognition (NER).mp422.02MB
  15. 03 - Beginner's Corner/010 Named Entity Recognition (NER) in Python.mp470.23MB
  16. 03 - Beginner's Corner/011 Text Summarization.mp424.15MB
  17. 03 - Beginner's Corner/012 Text Summarization in Python.mp445.47MB
  18. 03 - Beginner's Corner/013 Neural Machine Translation.mp428.1MB
  19. 03 - Beginner's Corner/014 Neural Machine Translation in Python.mp464.1MB
  20. 03 - Beginner's Corner/015 Question Answering.mp440.1MB
  21. 03 - Beginner's Corner/016 Question Answering in Python.mp448.15MB
  22. 03 - Beginner's Corner/017 Zero-Shot Classification.mp430.12MB
  23. 03 - Beginner's Corner/018 Zero-Shot Classification in Python.mp487.61MB
  24. 03 - Beginner's Corner/019 Beginner's Corner Section Summary.mp423.18MB
  25. 04 - Fine-Tuning (Intermediate)/001 Fine-Tuning Section Introduction.mp420.16MB
  26. 04 - Fine-Tuning (Intermediate)/002 Text Preprocessing and Tokenization Review.mp463.16MB
  27. 04 - Fine-Tuning (Intermediate)/003 Models and Tokenizers.mp464.57MB
  28. 04 - Fine-Tuning (Intermediate)/004 Models and Tokenizers in Python.mp484.26MB
  29. 04 - Fine-Tuning (Intermediate)/005 Transfer Learning & Fine-Tuning (pt 1).mp459.83MB
  30. 04 - Fine-Tuning (Intermediate)/006 Transfer Learning & Fine-Tuning (pt 2).mp449.31MB
  31. 04 - Fine-Tuning (Intermediate)/007 Transfer Learning & Fine-Tuning (pt 3).mp456.66MB
  32. 04 - Fine-Tuning (Intermediate)/008 Fine-Tuning Sentiment Analysis and the GLUE Benchmark.mp458.43MB
  33. 04 - Fine-Tuning (Intermediate)/009 Fine-Tuning Sentiment Analysis in Python.mp4130.77MB
  34. 04 - Fine-Tuning (Intermediate)/010 Fine-Tuning Transformers with Custom Dataset.mp4106.85MB
  35. 04 - Fine-Tuning (Intermediate)/011 Hugging Face AutoConfig.mp440.86MB
  36. 04 - Fine-Tuning (Intermediate)/012 Fine-Tuning with Multiple Inputs (Textual Entailment).mp428.42MB
  37. 04 - Fine-Tuning (Intermediate)/013 Fine-Tuning Transformers with Multiple Inputs in Python.mp456.66MB
  38. 04 - Fine-Tuning (Intermediate)/014 Fine-Tuning Section Summary.mp415.78MB
  39. 05 - Named Entity Recognition (NER) and POS Tagging/001 Token Classification Section Introduction.mp435.82MB
  40. 05 - Named Entity Recognition (NER) and POS Tagging/002 Data & Tokenizer (Code Preparation).mp419.33MB
  41. 05 - Named Entity Recognition (NER) and POS Tagging/003 Data & Tokenizer (Code).mp442.75MB
  42. 05 - Named Entity Recognition (NER) and POS Tagging/004 Target Alignment (Code Preparation).mp443.02MB
  43. 05 - Named Entity Recognition (NER) and POS Tagging/005 Create Tokenized Dataset (Code Preparation).mp418.33MB
  44. 05 - Named Entity Recognition (NER) and POS Tagging/006 Target Alignment (Code).mp461.64MB
  45. 05 - Named Entity Recognition (NER) and POS Tagging/007 Data Collator (Code Preparation).mp422.09MB
  46. 05 - Named Entity Recognition (NER) and POS Tagging/008 Data Collator (Code).mp416.94MB
  47. 05 - Named Entity Recognition (NER) and POS Tagging/009 Metrics (Code Preparation).mp433.44MB
  48. 05 - Named Entity Recognition (NER) and POS Tagging/010 Metrics (Code).mp439.33MB
  49. 05 - Named Entity Recognition (NER) and POS Tagging/011 Model and Trainer (Code Preparation).mp410.79MB
  50. 05 - Named Entity Recognition (NER) and POS Tagging/012 Model and Trainer (Code).mp422.18MB
  51. 05 - Named Entity Recognition (NER) and POS Tagging/013 POS Tagging & Custom Datasets (Exercise Prompt).mp421.33MB
  52. 05 - Named Entity Recognition (NER) and POS Tagging/014 POS Tagging & Custom Datasets (Solution).mp4115.13MB
  53. 05 - Named Entity Recognition (NER) and POS Tagging/015 Token Classification Section Summary.mp48.04MB
  54. 06 - Seq2Seq and Neural Machine Translation/001 Translation Section Introduction.mp418.2MB
  55. 06 - Seq2Seq and Neural Machine Translation/002 Data & Tokenizer (Code Preparation).mp424.52MB
  56. 06 - Seq2Seq and Neural Machine Translation/003 Data & Tokenizer (Code).mp434.12MB
  57. 06 - Seq2Seq and Neural Machine Translation/004 Aside Seq2Seq Basics (Optional).mp437.06MB
  58. 06 - Seq2Seq and Neural Machine Translation/005 Model Inputs (Code Preparation).mp432.41MB
  59. 06 - Seq2Seq and Neural Machine Translation/006 Model Inputs (Code).mp451.41MB
  60. 06 - Seq2Seq and Neural Machine Translation/007 Translation Metrics (BLEU Score & BERT Score) (Code Preparation).mp419.27MB
  61. 06 - Seq2Seq and Neural Machine Translation/008 Translation Metrics (BLEU Score & BERT Score) (Code).mp443.32MB
  62. 06 - Seq2Seq and Neural Machine Translation/009 Train & Evaluate (Code Preparation).mp421.27MB
  63. 06 - Seq2Seq and Neural Machine Translation/010 Train & Evaluate (Code).mp435.74MB
  64. 06 - Seq2Seq and Neural Machine Translation/011 Translation Section Summary.mp49.77MB
  65. 07 - Transformers and Attention Theory (Advanced)/001 Theory Section Introduction.mp417.14MB
  66. 07 - Transformers and Attention Theory (Advanced)/002 Basic Self-Attention.mp436.97MB
  67. 07 - Transformers and Attention Theory (Advanced)/003 Self-Attention & Scaled Dot-Product Attention.mp464.27MB
  68. 07 - Transformers and Attention Theory (Advanced)/004 Attention Efficiency.mp421.56MB
  69. 07 - Transformers and Attention Theory (Advanced)/005 Attention Mask.mp415.1MB
  70. 07 - Transformers and Attention Theory (Advanced)/006 Multi-Head Attention.mp433.71MB
  71. 07 - Transformers and Attention Theory (Advanced)/007 Transformer Block.mp429.5MB
  72. 07 - Transformers and Attention Theory (Advanced)/008 Positional Encodings.mp429.03MB
  73. 07 - Transformers and Attention Theory (Advanced)/009 Encoder Architecture.mp425.21MB
  74. 09 - Setting Up Your Environment FAQ/001 Anaconda Environment Setup.mp452.63MB
  75. 09 - Setting Up Your Environment FAQ/002 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp443.61MB
  76. 10 - Extra Help With Python Coding for Beginners FAQ/001 How to Code by Yourself (part 1).mp471.84MB
  77. 10 - Extra Help With Python Coding for Beginners FAQ/002 How to Code by Yourself (part 2).mp449.14MB
  78. 10 - Extra Help With Python Coding for Beginners FAQ/003 Proof that using Jupyter Notebook is the same as not using it.mp469.42MB
  79. 11 - Effective Learning Strategies for Machine Learning FAQ/001 How to Succeed in this Course (Long Version).mp417.87MB
  80. 11 - Effective Learning Strategies for Machine Learning FAQ/002 Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp438.95MB
  81. 11 - Effective Learning Strategies for Machine Learning FAQ/003 Machine Learning and AI Prerequisite Roadmap (pt 1).mp479.66MB
  82. 11 - Effective Learning Strategies for Machine Learning FAQ/004 Machine Learning and AI Prerequisite Roadmap (pt 2).mp4108.16MB
  83. 12 - Appendix FAQ Finale/001 What is the Appendix.mp416.37MB
  84. 12 - Appendix FAQ Finale/002 BONUS.mp437.83MB
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