| 001 Anaconda Environment Setup.mp4 | 52.6 MB | ||
| 001 Anaconda Environment Setup_en.srt | 20.1 KB | ||
| 001 Beginner's Corner Section Introduction.mp4 | 49.7 MB | ||
| 001 Beginner's Corner Section Introduction_en.srt | 15.1 KB | ||
| 001 Data Links.html | 307.2 B | ||
| 001 Fine-Tuning Section Introduction.mp4 | 20.2 MB | ||
| 001 Fine-Tuning Section Introduction_en.srt | 6.1 KB | ||
| 001 How to Code by Yourself (part 1).mp4 | 71.8 MB | ||
| 001 How to Code by Yourself (part 1)_en.srt | 23.1 KB | ||
| 001 How to Succeed in this Course (Long Version).mp4 | 17.9 MB | ||
| 001 How to Succeed in this Course (Long Version)_en.srt | 14.5 KB | ||
| 001 Introduction.mp4 | 34.6 MB | ||
| 001 Introduction_en.srt | 5.6 KB | ||
| 001 Theory Section Introduction.mp4 | 17.1 MB | ||
| 001 Theory Section Introduction_en.srt | 6.9 KB | ||
| 001 Token Classification Section Introduction.mp4 | 35.8 MB | ||
| 001 Token Classification Section Introduction_en.srt | 9.6 KB | ||
| 001 Translation Section Introduction.mp4 | 18.2 MB | ||
| 001 Translation Section Introduction_en.srt | 6.4 KB | ||
| 001 What is the Appendix.mp4 | 16.4 MB | ||
| 001 What is the Appendix_en.srt | 3.9 KB | ||
| 001 Where to get the code and data.mp4 | 40.6 MB | ||
| 001 Where to get the code and data_en.srt | 10.5 KB | ||
| 002 BONUS.mp4 | 37.8 MB | ||
| 002 BONUS_en.srt | 7.9 KB | ||
| 002 Basic Self-Attention.mp4 | 37 MB | ||
| 002 Basic Self-Attention_en.srt | 12.4 KB | ||
| 002 Data & Tokenizer (Code Preparation).mp4 | 24.5 MB | ||
| 002 Data & Tokenizer (Code Preparation)_en.srt | 7.5 KB | ||
| 002 From RNNs to Attention and Transformers - Intuition.mp4 | 78.2 MB | ||
| 002 From RNNs to Attention and Transformers - Intuition_en.srt | 24 KB | ||
| 002 How to Code by Yourself (part 2).mp4 | 49.1 MB | ||
| 002 How to Code by Yourself (part 2)_en.srt | 13.2 KB | ||
| 002 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 | 43.6 MB | ||
| 002 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow_en.srt | 15.8 KB | ||
| 002 How to use Github & Extra Coding Tips (Optional).mp4 | 50.8 MB | ||
| 002 How to use Github & Extra Coding Tips (Optional)_en.srt | 12.9 KB | ||
| 002 Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 | 39 MB | ||
| 002 Is this for Beginners or Experts Academic or Practical Fast or slow-paced_en.srt | 32.7 KB | ||
| 002 Outline.mp4 | 50.7 MB | ||
| 002 Outline_en.srt | 13.5 KB | ||
| 002 Text Preprocessing and Tokenization Review.mp4 | 63.2 MB | ||
| 002 Text Preprocessing and Tokenization Review_en.srt | 18.2 KB | ||
| 003 Are You Beginner, Intermediate, or Advanced All are OK!.mp4 | 26.7 MB | ||
| 003 Are You Beginner, Intermediate, or Advanced All are OK!_en.srt | 7.1 KB | ||
| 003 Data & Tokenizer (Code).mp4 | 34.1 MB | ||
| 003 Data & Tokenizer (Code)_en.srt | 6.4 KB | ||
| 003 Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 | 79.7 MB | ||
| 003 Machine Learning and AI Prerequisite Roadmap (pt 1)_en.srt | 17.1 KB | ||
| 003 Models and Tokenizers.mp4 | 64.6 MB | ||
| 003 Models and Tokenizers_en.srt | 20.6 KB | ||
| 003 Proof that using Jupyter Notebook is the same as not using it.mp4 | 69.4 MB | ||
| 003 Proof that using Jupyter Notebook is the same as not using it_en.srt | 14.9 KB | ||
| 003 Self-Attention & Scaled Dot-Product Attention.mp4 | 64.3 MB | ||
| 003 Self-Attention & Scaled Dot-Product Attention_en.srt | 23.9 KB | ||
| 003 Sentiment Analysis.mp4 | 53.6 MB | ||
| 003 Sentiment Analysis_en.srt | 14.5 KB | ||
| 004 Aside Seq2Seq Basics (Optional).mp4 | 37.1 MB | ||
| 004 Aside Seq2Seq Basics (Optional)_en.srt | 15.3 KB | ||
| 004 Attention Efficiency.mp4 | 21.6 MB | ||
| 004 Attention Efficiency_en.srt | 5.9 KB | ||
| 004 Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 | 108.2 MB | ||
| 004 Machine Learning and AI Prerequisite Roadmap (pt 2)_en.srt | 23.9 KB | ||
| 004 Models and Tokenizers in Python.mp4 | 84.3 MB | ||
| 004 Models and Tokenizers in Python_en.srt | 14.1 KB | ||
| 004 Sentiment Analysis in Python.mp4 | 97 MB | ||
| 004 Sentiment Analysis in Python_en.srt | 21.1 KB | ||
| 004 Target Alignment (Code Preparation).mp4 | 43 MB | ||
| 004 Target Alignment (Code Preparation)_en.srt | 13.8 KB | ||
| 005 Attention Mask.mp4 | 15.1 MB | ||
| 005 Attention Mask_en.srt | 5 KB | ||
| 005 Create Tokenized Dataset (Code Preparation).mp4 | 18.3 MB | ||
| 005 Create Tokenized Dataset (Code Preparation)_en.srt | 5 KB | ||
| 005 Model Inputs (Code Preparation).mp4 | 32.4 MB | ||
| 005 Model Inputs (Code Preparation)_en.srt | 11.2 KB | ||
| 005 Text Generation.mp4 | 57.1 MB | ||
| 005 Text Generation_en.srt | 15.4 KB | ||
| 005 Transfer Learning & Fine-Tuning (pt 1).mp4 | 59.8 MB | ||
| 005 Transfer Learning & Fine-Tuning (pt 1)_en.srt | 12.7 KB | ||
| 006 Model Inputs (Code).mp4 | 51.4 MB | ||
| 006 Model Inputs (Code)_en.srt | 7.8 KB | ||
| 006 Multi-Head Attention.mp4 | 33.7 MB | ||
| 006 Multi-Head Attention_en.srt | 9.4 KB | ||
| 006 Target Alignment (Code).mp4 | 61.6 MB | ||
| 006 Target Alignment (Code)_en.srt | 11.9 KB | ||
| 006 Text Generation in Python.mp4 | 86.3 MB | ||
| 006 Text Generation in Python_en.srt | 14.9 KB | ||
| 006 Transfer Learning & Fine-Tuning (pt 2).mp4 | 49.3 MB | ||
| 006 Transfer Learning & Fine-Tuning (pt 2)_en.srt | 14.6 KB | ||
| 007 Data Collator (Code Preparation).mp4 | 22.1 MB | ||
| 007 Data Collator (Code Preparation)_en.srt | 4.9 KB | ||
| 007 Masked Language Modeling (Article Spinner).mp4 | 67.3 MB | ||
| 007 Masked Language Modeling (Article Spinner)_en.srt | 16.1 KB | ||
| 007 Transfer Learning & Fine-Tuning (pt 3).mp4 | 56.7 MB | ||
| 007 Transfer Learning & Fine-Tuning (pt 3)_en.srt | 13.7 KB | ||
| 007 Transformer Block.mp4 | 29.5 MB | ||
| 007 Transformer Block_en.srt | 9.5 KB | ||
| 007 Translation Metrics (BLEU Score & BERT Score) (Code Preparation).mp4 | 19.3 MB | ||
| 007 Translation Metrics (BLEU Score & BERT Score) (Code Preparation)_en.srt | 5 KB | ||
| 008 Data Collator (Code).mp4 | 16.9 MB | ||
| 008 Data Collator (Code)_en.srt | 3.7 KB | ||
| 008 Fine-Tuning Sentiment Analysis and the GLUE Benchmark.mp4 | 58.4 MB | ||
| 008 Fine-Tuning Sentiment Analysis and the GLUE Benchmark_en.srt | 16.9 KB | ||
| 008 Masked Language Modeling (Article Spinner) in Python.mp4 | 67.1 MB | ||
| 008 Masked Language Modeling (Article Spinner) in Python_en.srt | 9.2 KB | ||
| 008 Positional Encodings.mp4 | 29 MB | ||
| 008 Positional Encodings_en.srt | 9.5 KB | ||
| 008 Translation Metrics (BLEU Score & BERT Score) (Code).mp4 | 43.3 MB | ||
| 008 Translation Metrics (BLEU Score & BERT Score) (Code)_en.srt | 6.3 KB | ||
| 009 Encoder Architecture.mp4 | 25.2 MB | ||
| 009 Encoder Architecture_en.srt | 8.6 KB | ||
| 009 Fine-Tuning Sentiment Analysis in Python.mp4 | 130.8 MB | ||
| 009 Fine-Tuning Sentiment Analysis in Python_en.srt | 19.3 KB | ||
| 009 Metrics (Code Preparation).mp4 | 33.4 MB | ||
| 009 Metrics (Code Preparation)_en.srt | 9.1 KB | ||
| 009 Named Entity Recognition (NER).mp4 | 22 MB | ||
| 009 Named Entity Recognition (NER)_en.srt | 6.2 KB | ||
| 009 Train & Evaluate (Code Preparation).mp4 | 21.3 MB | ||
| 009 Train & Evaluate (Code Preparation)_en.srt | 5.6 KB | ||
| 010 Fine-Tuning Transformers with Custom Dataset.mp4 | 106.9 MB | ||
| 010 Fine-Tuning Transformers with Custom Dataset_en.srt | 15.1 KB | ||
| 010 Metrics (Code).mp4 | 39.3 MB | ||
| 010 Metrics (Code)_en.srt | 6.1 KB | ||
| 010 Named Entity Recognition (NER) in Python.mp4 | 70.2 MB | ||
| 010 Named Entity Recognition (NER) in Python_en.srt | 9.6 KB | ||
| 010 Train & Evaluate (Code).mp4 | 35.7 MB | ||
| 010 Train & Evaluate (Code)_en.srt | 4.6 KB | ||
| 011 Hugging Face AutoConfig.mp4 | 40.9 MB | ||
| 011 Hugging Face AutoConfig_en.srt | 6 KB | ||
| 011 Model and Trainer (Code Preparation).mp4 | 10.8 MB | ||
| 011 Model and Trainer (Code Preparation)_en.srt | 2.9 KB | ||
| 011 Text Summarization.mp4 | 24.1 MB | ||
| 011 Text Summarization_en.srt | 7.1 KB | ||
| 011 Translation Section Summary.mp4 | 9.8 MB | ||
| 011 Translation Section Summary_en.srt | 3.3 KB | ||
| 012 Fine-Tuning with Multiple Inputs (Textual Entailment).mp4 | 28.4 MB | ||
| 012 Fine-Tuning with Multiple Inputs (Textual Entailment)_en.srt | 10.3 KB | ||
| 012 Model and Trainer (Code).mp4 | 22.2 MB | ||
| 012 Model and Trainer (Code)_en.srt | 3.1 KB | ||
| 012 Text Summarization in Python.mp4 | 45.5 MB | ||
| 012 Text Summarization in Python_en.srt | 7.6 KB | ||
| 013 Fine-Tuning Transformers with Multiple Inputs in Python.mp4 | 56.7 MB | ||
| 013 Fine-Tuning Transformers with Multiple Inputs in Python_en.srt | 6.6 KB | ||
| 013 Neural Machine Translation.mp4 | 28.1 MB | ||
| 013 Neural Machine Translation_en.srt | 8.1 KB | ||
| 013 POS Tagging & Custom Datasets (Exercise Prompt).mp4 | 21.3 MB | ||
| 013 POS Tagging & Custom Datasets (Exercise Prompt)_en.srt | 6.8 KB | ||
| 014 Fine-Tuning Section Summary.mp4 | 15.8 MB | ||
| 014 Fine-Tuning Section Summary_en.srt | 4.1 KB | ||
| 014 Neural Machine Translation in Python.mp4 | 64.1 MB | ||
| 014 Neural Machine Translation in Python_en.srt | 9.7 KB | ||
| 014 POS Tagging & Custom Datasets (Solution).mp4 | 115.1 MB | ||
| 014 POS Tagging & Custom Datasets (Solution)_en.srt | 17.9 KB | ||
| 015 Question Answering.mp4 | 40.1 MB | ||
| 015 Question Answering_en.srt | 10 KB | ||
| 015 Token Classification Section Summary.mp4 | 8 MB | ||
| 015 Token Classification Section Summary_en.srt | 2.6 KB | ||
| 016 Question Answering in Python.mp4 | 48.2 MB | ||
| 016 Question Answering in Python_en.srt | 7 KB | ||
| 017 Zero-Shot Classification.mp4 | 30.1 MB | ||
| 017 Zero-Shot Classification_en.srt | 7.6 KB | ||
| 018 Zero-Shot Classification in Python.mp4 | 87.6 MB | ||
| 018 Zero-Shot Classification in Python_en.srt | 16.4 KB | ||
| 019 Beginner's Corner Section Summary.mp4 | 23.2 MB | ||
| 019 Beginner's Corner Section Summary_en.srt | 6.4 KB | ||
| [CourseClub.Me].url | 102.4 B | ||
| [FreeCourseSite.com].url | 102.4 B | ||
| [GigaCourse.Com].url | 0 B | ||
| external-assets-links.txt | 204.8 B | ||
| ▲ 179 total files | |||
Udemy - Data Science: Transformers for Natural Language Processing [FCS]
BERT, GPT, Deep Learning, Machine Learning, & NLP with Hugging Face, Attention in Python, Tensorflow, PyTorch, & Keras
Created by Lazy Programmer Team, Lazy Programmer Inc.
Last updated 11/2022
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