| 1. Welcome | |||
| 1. Introduction.mp4 | 34.6 MB | ||
| 1. Introduction.srt | 5.63 KB | ||
| 2. Outline.mp4 | 50.65 MB | ||
| 2. Outline.srt | 13.51 KB | ||
| 10. Extras | |||
| 1. Data Links.html | 256 B | ||
| 11. Setting Up Your Environment FAQ | |||
| 1. Anaconda Environment Setup.mp4 | 52.64 MB | ||
| 1. Anaconda Environment Setup.srt | 20.13 KB | ||
| 2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 | 43.61 MB | ||
| 2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt | 15.81 KB | ||
| GetFreeCourses.Co.url | 116 B | ||
| 12. Extra Help With Python Coding for Beginners FAQ | |||
| 1. How to Code by Yourself (part 1).mp4 | 71.84 MB | ||
| 1. How to Code by Yourself (part 1).srt | 23.13 KB | ||
| 2. How to Code by Yourself (part 2).mp4 | 49.14 MB | ||
| 2. How to Code by Yourself (part 2).srt | 13.24 KB | ||
| 3. Proof that using Jupyter Notebook is the same as not using it.mp4 | 69.42 MB | ||
| 3. Proof that using Jupyter Notebook is the same as not using it.srt | 14.92 KB | ||
| 13. Effective Learning Strategies for Machine Learning FAQ | |||
| 1. How to Succeed in this Course (Long Version).mp4 | 17.87 MB | ||
| 1. How to Succeed in this Course (Long Version).srt | 14.55 KB | ||
| 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 | 38.96 MB | ||
| 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt | 32.69 KB | ||
| 3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 | 79.66 MB | ||
| 3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt | 17.14 KB | ||
| 4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 | 108.17 MB | ||
| 4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt | 23.93 KB | ||
| GetFreeCourses.Co.url | 116 B | ||
| 14. Appendix FAQ Finale | |||
| 1. What is the Appendix.mp4 | 16.37 MB | ||
| 1. What is the Appendix.srt | 3.88 KB | ||
| 2. BONUS.mp4 | 39.92 MB | ||
| 2. BONUS.srt | 7.94 KB | ||
| 2. Getting Setup | |||
| 1. Get Your Hands Dirty, Practical Coding Experience, Data Links.mp4 | 43.55 MB | ||
| 1. Get Your Hands Dirty, Practical Coding Experience, Data Links.srt | 11.99 KB | ||
| 1.1 Data Links.html | 157 B | ||
| 1.2 Github Link.html | 145 B | ||
| 2. How to use Github & Extra Coding Tips (Optional).mp4 | 63.9 MB | ||
| 2. How to use Github & Extra Coding Tips (Optional).srt | 15.71 KB | ||
| 3. Where to get the code, notebooks, and data.mp4 | 17.77 MB | ||
| 3. Where to get the code, notebooks, and data.srt | 4.29 KB | ||
| 3.1 Code Link.html | 125 B | ||
| 3.2 Data Links.html | 157 B | ||
| 3.3 Github Link.html | 145 B | ||
| 4. Are You Beginner, Intermediate, or Advanced All are OK!.mp4 | 26.74 MB | ||
| 4. Are You Beginner, Intermediate, or Advanced All are OK!.srt | 7.14 KB | ||
| 5. How to Succeed in This Course.mp4 | 41.19 MB | ||
| 5. How to Succeed in This Course.srt | 13.03 KB | ||
| 3. Beginner's Corner | |||
| 1. Beginner's Corner Section Introduction.mp4 | 49.75 MB | ||
| 1. Beginner's Corner Section Introduction.srt | 15.06 KB | ||
| 10. Named Entity Recognition (NER) in Python.mp4 | 70.24 MB | ||
| 10. Named Entity Recognition (NER) in Python.srt | 9.63 KB | ||
| 11. Text Summarization.mp4 | 24.15 MB | ||
| 11. Text Summarization.srt | 7.08 KB | ||
| 12. Text Summarization in Python.mp4 | 45.47 MB | ||
| 12. Text Summarization in Python.srt | 7.55 KB | ||
| 13. Neural Machine Translation.mp4 | 28.1 MB | ||
| 13. Neural Machine Translation.srt | 8.15 KB | ||
| 14. Neural Machine Translation in Python.mp4 | 64.1 MB | ||
| 14. Neural Machine Translation in Python.srt | 9.72 KB | ||
| 15. Question Answering.mp4 | 40.1 MB | ||
| 15. Question Answering.srt | 10.03 KB | ||
| 16. Question Answering in Python.mp4 | 48.15 MB | ||
| 16. Question Answering in Python.srt | 6.97 KB | ||
| 17. Zero-Shot Classification.mp4 | 30.12 MB | ||
| 17. Zero-Shot Classification.srt | 7.6 KB | ||
| 18. Zero-Shot Classification in Python.mp4 | 87.61 MB | ||
| 18. Zero-Shot Classification in Python.srt | 16.41 KB | ||
| 19. Beginner's Corner Section Summary.mp4 | 23.18 MB | ||
| 19. Beginner's Corner Section Summary.srt | 6.39 KB | ||
| 2. From RNNs to Attention and Transformers - Intuition.mp4 | 78.2 MB | ||
| 2. From RNNs to Attention and Transformers - Intuition.srt | 24.01 KB | ||
| 20. Suggestion Box.mp4 | 27.17 MB | ||
| 20. Suggestion Box.srt | 4.82 KB | ||
| 3. Sentiment Analysis.mp4 | 53.6 MB | ||
| 3. Sentiment Analysis.srt | 14.55 KB | ||
| 4. Sentiment Analysis in Python.mp4 | 97.05 MB | ||
| 4. Sentiment Analysis in Python.srt | 21.12 KB | ||
| 5. Text Generation.mp4 | 57.1 MB | ||
| 5. Text Generation.srt | 15.45 KB | ||
| 6. Text Generation in Python.mp4 | 86.35 MB | ||
| 6. Text Generation in Python.srt | 14.92 KB | ||
| 7. Masked Language Modeling (Article Spinner).mp4 | 67.29 MB | ||
| 7. Masked Language Modeling (Article Spinner).srt | 16.12 KB | ||
| 8. Masked Language Modeling (Article Spinner) in Python.mp4 | 67.09 MB | ||
| 8. Masked Language Modeling (Article Spinner) in Python.srt | 9.24 KB | ||
| 9. Named Entity Recognition (NER).mp4 | 22.02 MB | ||
| 9. Named Entity Recognition (NER).srt | 6.24 KB | ||
| 4. Fine-Tuning (Intermediate) | |||
| 1. Fine-Tuning Section Introduction.mp4 | 20.16 MB | ||
| 1. Fine-Tuning Section Introduction.srt | 6.14 KB | ||
| 10. Fine-Tuning Transformers with Custom Dataset.mp4 | 106.85 MB | ||
| 10. Fine-Tuning Transformers with Custom Dataset.srt | 15.1 KB | ||
| 11. Hugging Face AutoConfig.mp4 | 40.86 MB | ||
| 11. Hugging Face AutoConfig.srt | 6.03 KB | ||
| 12. Fine-Tuning with Multiple Inputs (Textual Entailment).mp4 | 28.42 MB | ||
| 12. Fine-Tuning with Multiple Inputs (Textual Entailment).srt | 10.32 KB | ||
| 13. Fine-Tuning Transformers with Multiple Inputs in Python.mp4 | 56.66 MB | ||
| 13. Fine-Tuning Transformers with Multiple Inputs in Python.srt | 6.62 KB | ||
| 14. Fine-Tuning Section Summary.mp4 | 15.78 MB | ||
| 14. Fine-Tuning Section Summary.srt | 4.11 KB | ||
| 2. Text Preprocessing and Tokenization Review.mp4 | 63.16 MB | ||
| 2. Text Preprocessing and Tokenization Review.srt | 18.22 KB | ||
| 3. Models and Tokenizers.mp4 | 64.57 MB | ||
| 3. Models and Tokenizers.srt | 20.6 KB | ||
| 4. Models and Tokenizers in Python.mp4 | 84.26 MB | ||
| 4. Models and Tokenizers in Python.srt | 14.12 KB | ||
| 5. Transfer Learning & Fine-Tuning (pt 1).mp4 | 59.83 MB | ||
| 5. Transfer Learning & Fine-Tuning (pt 1).srt | 12.69 KB | ||
| 6. Transfer Learning & Fine-Tuning (pt 2).mp4 | 49.31 MB | ||
| 6. Transfer Learning & Fine-Tuning (pt 2).srt | 14.58 KB | ||
| 7. Transfer Learning & Fine-Tuning (pt 3).mp4 | 56.66 MB | ||
| 7. Transfer Learning & Fine-Tuning (pt 3).srt | 13.67 KB | ||
| 8. Fine-Tuning Sentiment Analysis and the GLUE Benchmark.mp4 | 58.43 MB | ||
| 8. Fine-Tuning Sentiment Analysis and the GLUE Benchmark.srt | 16.85 KB | ||
| 9. Fine-Tuning Sentiment Analysis in Python.mp4 | 130.77 MB | ||
| 9. Fine-Tuning Sentiment Analysis in Python.srt | 19.29 KB | ||
| GetFreeCourses.Co.url | 116 B | ||
| 5. Named Entity Recognition (NER) and POS Tagging (Intermediate) | |||
| 1. Token Classification Section Introduction.mp4 | 35.82 MB | ||
| 1. Token Classification Section Introduction.srt | 9.59 KB | ||
| 10. Metrics (Code).mp4 | 39.34 MB | ||
| 10. Metrics (Code).srt | 6.09 KB | ||
| 11. Model and Trainer (Code Preparation).mp4 | 10.79 MB | ||
| 11. Model and Trainer (Code Preparation).srt | 2.91 KB | ||
| 12. Model and Trainer (Code).mp4 | 22.18 MB | ||
| 12. Model and Trainer (Code).srt | 3.12 KB | ||
| 13. POS Tagging & Custom Datasets (Exercise Prompt).mp4 | 21.33 MB | ||
| 13. POS Tagging & Custom Datasets (Exercise Prompt).srt | 6.85 KB | ||
| 14. POS Tagging & Custom Datasets (Solution).mp4 | 115.13 MB | ||
| 14. POS Tagging & Custom Datasets (Solution).srt | 17.9 KB | ||
| 15. Token Classification Section Summary.mp4 | 8.04 MB | ||
| 15. Token Classification Section Summary.srt | 2.6 KB | ||
| 2. Data & Tokenizer (Code Preparation).mp4 | 19.34 MB | ||
| 2. Data & Tokenizer (Code Preparation).srt | 6.81 KB | ||
| 3. Data & Tokenizer (Code).mp4 | 42.75 MB | ||
| 3. Data & Tokenizer (Code).srt | 9.2 KB | ||
| 4. Target Alignment (Code Preparation).mp4 | 43.02 MB | ||
| 4. Target Alignment (Code Preparation).srt | 13.76 KB | ||
| 5. Create Tokenized Dataset (Code Preparation).mp4 | 18.34 MB | ||
| 5. Create Tokenized Dataset (Code Preparation).srt | 5 KB | ||
| 6. Target Alignment (Code).mp4 | 61.65 MB | ||
| 6. Target Alignment (Code).srt | 11.86 KB | ||
| 7. Data Collator (Code Preparation).mp4 | 22.1 MB | ||
| 7. Data Collator (Code Preparation).srt | 4.92 KB | ||
| 8. Data Collator (Code).mp4 | 16.94 MB | ||
| 8. Data Collator (Code).srt | 3.7 KB | ||
| 9. Metrics (Code Preparation).mp4 | 33.44 MB | ||
| 9. Metrics (Code Preparation).srt | 9.13 KB | ||
| 6. Seq2Seq and Neural Machine Translation (Intermediate) | |||
| 1. Translation Section Introduction.mp4 | 18.2 MB | ||
| 1. Translation Section Introduction.srt | 6.35 KB | ||
| 10. Train & Evaluate (Code Preparation).mp4 | 21.27 MB | ||
| 10. Train & Evaluate (Code Preparation).srt | 5.62 KB | ||
| 11. Train & Evaluate (Code).mp4 | 35.74 MB | ||
| 11. Train & Evaluate (Code).srt | 4.64 KB | ||
| 12. Translation Section Summary.mp4 | 9.77 MB | ||
| 12. Translation Section Summary.srt | 3.3 KB | ||
| 2. Data & Tokenizer (Code Preparation).mp4 | 24.53 MB | ||
| 2. Data & Tokenizer (Code Preparation).srt | 7.5 KB | ||
| 3. Things Move Fast.mp4 | 6.09 MB | ||
| 3. Things Move Fast.srt | 2.36 KB | ||
| 4. Data & Tokenizer (Code).mp4 | 34.12 MB | ||
| 4. Data & Tokenizer (Code).srt | 6.44 KB | ||
| 5. Aside Seq2Seq Basics (Optional).mp4 | 37.06 MB | ||
| 5. Aside Seq2Seq Basics (Optional).srt | 15.25 KB | ||
| 6. Model Inputs (Code Preparation).mp4 | 32.41 MB | ||
| 6. Model Inputs (Code Preparation).srt | 11.21 KB | ||
| 7. Model Inputs (Code).mp4 | 51.41 MB | ||
| 7. Model Inputs (Code).srt | 7.78 KB | ||
| 8. Translation Metrics (BLEU Score & BERT Score) (Code Preparation).mp4 | 19.28 MB | ||
| 8. Translation Metrics (BLEU Score & BERT Score) (Code Preparation).srt | 4.96 KB | ||
| 9. Translation Metrics (BLEU Score & BERT Score) (Code).mp4 | 43.32 MB | ||
| 9. Translation Metrics (BLEU Score & BERT Score) (Code).srt | 6.33 KB | ||
| 7. Question-Answering (Advanced) | |||
| 1. Question-Answering Section Introduction.mp4 | 21.52 MB | ||
| 1. Question-Answering Section Introduction.srt | 6.11 KB | ||
| 10. Question-Answering Metrics.mp4 | 16.5 MB | ||
| 10. Question-Answering Metrics.srt | 4.71 KB | ||
| 11. Question-Answering Metrics in Python.mp4 | 22.88 MB | ||
| 11. Question-Answering Metrics in Python.srt | 2.98 KB | ||
| 12. From Logits to Answers.mp4 | 95.56 MB | ||
| 12. From Logits to Answers.srt | 27.71 KB | ||
| 13. From Logits to Answers in Python.mp4 | 120.62 MB | ||
| 13. From Logits to Answers in Python.srt | 16.89 KB | ||
| 14. Computing Metrics.mp4 | 24.94 MB | ||
| 14. Computing Metrics.srt | 6.62 KB | ||
| 15. Computing Metrics in Python.mp4 | 44.26 MB | ||
| 15. Computing Metrics in Python.srt | 6.08 KB | ||
| 16. Train and Evaluate.mp4 | 14.07 MB | ||
| 16. Train and Evaluate.srt | 3.32 KB | ||
| 17. Train and Evaluate in Python.mp4 | 37.8 MB | ||
| 17. Train and Evaluate in Python.srt | 4.67 KB | ||
| 18. Question-Answering Section Summary.mp4 | 14.24 MB | ||
| 18. Question-Answering Section Summary.srt | 5.04 KB | ||
| 2. Exploring the Dataset (SQuAD).mp4 | 20.16 MB | ||
| 2. Exploring the Dataset (SQuAD).srt | 5.68 KB | ||
| 3. Exploring the Dataset (SQuAD) in Python.mp4 | 39.85 MB | ||
| 3. Exploring the Dataset (SQuAD) in Python.srt | 4.66 KB | ||
| 4. Using the Tokenizer.mp4 | 34.52 MB | ||
| 4. Using the Tokenizer.srt | 10.82 KB | ||
| 5. Using the Tokenizer in Python.mp4 | 72.13 MB | ||
| 5. Using the Tokenizer in Python.srt | 13.08 KB | ||
| 6. Aligning the Targets.mp4 | 69.08 MB | ||
| 6. Aligning the Targets.srt | 19.33 KB | ||
| 7. Aligning the Targets in Python.mp4 | 103.33 MB | ||
| 7. Aligning the Targets in Python.srt | 18.83 KB | ||
| 8. Applying the Tokenizer.mp4 | 45.05 MB | ||
| 8. Applying the Tokenizer.srt | 12.29 KB | ||
| 9. Applying the Tokenizer in Python.mp4 | 76.5 MB | ||
| 9. Applying the Tokenizer in Python.srt | 12.03 KB | ||
| GetFreeCourses.Co.url | 116 B | ||
| 8. Transformers and Attention Theory (Advanced) | |||
| 1. Theory Section Introduction.mp4 | 17.14 MB | ||
| 1. Theory Section Introduction.srt | 6.88 KB | ||
| 10. Decoder Architecture.mp4 | 49.61 MB | ||
| 10. Decoder Architecture.srt | 14.61 KB | ||
| 11. Encoder-Decoder Architecture.mp4 | 39.7 MB | ||
| 11. Encoder-Decoder Architecture.srt | 11.36 KB | ||
| 12. BERT.mp4 | 23.25 MB | ||
| 12. BERT.srt | 6.12 KB | ||
| 13. GPT.mp4 | 31.17 MB | ||
| 13. GPT.srt | 8.65 KB | ||
| 14. GPT-2.mp4 | 29.67 MB | ||
| 14. GPT-2.srt | 8.27 KB | ||
| 15. GPT-3.mp4 | 23.99 MB | ||
| 15. GPT-3.srt | 6.57 KB | ||
| 16. Theory Section Summary.mp4 | 21 MB | ||
| 16. Theory Section Summary.srt | 6.29 KB | ||
| 2. Basic Self-Attention.mp4 | 36.97 MB | ||
| 2. Basic Self-Attention.srt | 12.44 KB | ||
| 3. Self-Attention & Scaled Dot-Product Attention.mp4 | 64.27 MB | ||
| 3. Self-Attention & Scaled Dot-Product Attention.srt | 23.94 KB | ||
| 4. Attention Efficiency.mp4 | 21.57 MB | ||
| 4. Attention Efficiency.srt | 5.86 KB | ||
| 5. Attention Mask.mp4 | 15.11 MB | ||
| 5. Attention Mask.srt | 5.04 KB | ||
| 6. Multi-Head Attention.mp4 | 33.71 MB | ||
| 6. Multi-Head Attention.srt | 9.35 KB | ||
| 7. Transformer Block.mp4 | 29.5 MB | ||
| 7. Transformer Block.srt | 9.53 KB | ||
| 8. Positional Encodings.mp4 | 29.03 MB | ||
| 8. Positional Encodings.srt | 9.46 KB | ||
| 9. Encoder Architecture.mp4 | 25.21 MB | ||
| 9. Encoder Architecture.srt | 8.63 KB | ||
| 9. Implement Transformers From Scratch (Advanced) | |||
| 1. Implementation Section Introduction.mp4 | 25.6 MB | ||
| 1. Implementation Section Introduction.srt | 8.49 KB | ||
| 10. How to Train a Causal Language Model From Scratch.mp4 | 120.37 MB | ||
| 10. How to Train a Causal Language Model From Scratch.srt | 20.12 KB | ||
| 11. Implement a Seq2Seq Transformer From Scratch for Language Translation (pt 1).mp4 | 93.99 MB | ||
| 11. Implement a Seq2Seq Transformer From Scratch for Language Translation (pt 1).srt | 13.39 KB | ||
| 12. Implement a Seq2Seq Transformer From Scratch for Language Translation (pt 2).mp4 | 95.19 MB | ||
| 12. Implement a Seq2Seq Transformer From Scratch for Language Translation (pt 2).srt | 14.97 KB | ||
| 13. Implement a Seq2Seq Transformer From Scratch for Language Translation (pt 3).mp4 | 108.62 MB | ||
| 13. Implement a Seq2Seq Transformer From Scratch for Language Translation (pt 3).srt | 17.47 KB | ||
| 14. Implementation Section Summary.mp4 | 10.59 MB | ||
| 14. Implementation Section Summary.srt | 1.99 KB | ||
| 2. Encoder Implementation Plan & Outline.mp4 | 23.02 MB | ||
| 2. Encoder Implementation Plan & Outline.srt | 8.37 KB | ||
| 3. How to Implement Multihead Attention From Scratch.mp4 | 93.41 MB | ||
| 3. How to Implement Multihead Attention From Scratch.srt | 15.5 KB | ||
| 4. How to Implement the Transformer Block From Scratch.mp4 | 14.94 MB | ||
| 4. How to Implement the Transformer Block From Scratch.srt | 2.36 KB | ||
| 5. How to Implement Positional Encoding From Scratch.mp4 | 35.87 MB | ||
| 5. How to Implement Positional Encoding From Scratch.srt | 6.26 KB | ||
| 6. How to Implement Transformer Encoder From Scratch.mp4 | 27.03 MB | ||
| 6. How to Implement Transformer Encoder From Scratch.srt | 4.82 KB | ||
| 7. Train and Evaluate Encoder From Scratch.mp4 | 89.33 MB | ||
| 7. Train and Evaluate Encoder From Scratch.srt | 12.3 KB | ||
| 8. How to Implement Causal Self-Attention From Scratch.mp4 | 39.2 MB | ||
| 8. How to Implement Causal Self-Attention From Scratch.srt | 5.68 KB | ||
| 9. How to Implement a Transformer Decoder (GPT) From Scratch.mp4 | 27.3 MB | ||
| 9. How to Implement a Transformer Decoder (GPT) From Scratch.srt | 4.86 KB | ||
| Download Paid Udemy Courses For Free.url | 116 B | ||
| GetFreeCourses.Co.url | 116 B | ||
| How you can help GetFreeCourses.Co.txt | 182 B | ||
| ▲ 267 total files | |||
Data Science: Transformers for Natural Language Processing
ChatGPT, GPT-4, BERT, Deep Learning, Machine Learning, & NLP with Hugging Face, Attention in Python, Tensorflow, PyTorch
Udemy Link - https://www.udemy.com/course/data-science-transformers-nlp/
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