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种子名称： [FreeCourseSite.com] Udemy - Data Science Transformers for Natural Language Processing
文件类型： 视频
文件数目： 84个文件
文件大小： 3.69 GB
收录时间： 2023-3-27 15:35
已经下载： 3次
资源热度： 245
最近下载： 2024-11-20 23:24

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

01 - Welcome/001 Introduction.mp434.6MB

01 - Welcome/002 Outline.mp450.65MB

02 - Getting Setup/001 Where to get the code and data.mp440.57MB

02 - Getting Setup/002 How to use Github & Extra Coding Tips (Optional).mp450.83MB

02 - Getting Setup/003 Are You Beginner, Intermediate, or Advanced All are OK!.mp426.73MB

03 - Beginner's Corner/001 Beginner's Corner Section Introduction.mp449.75MB

03 - Beginner's Corner/002 From RNNs to Attention and Transformers - Intuition.mp478.2MB

03 - Beginner's Corner/003 Sentiment Analysis.mp453.6MB

03 - Beginner's Corner/004 Sentiment Analysis in Python.mp497.04MB

03 - Beginner's Corner/005 Text Generation.mp457.1MB

03 - Beginner's Corner/006 Text Generation in Python.mp486.34MB

03 - Beginner's Corner/007 Masked Language Modeling (Article Spinner).mp467.29MB

03 - Beginner's Corner/008 Masked Language Modeling (Article Spinner) in Python.mp467.09MB

03 - Beginner's Corner/009 Named Entity Recognition (NER).mp422.02MB

03 - Beginner's Corner/010 Named Entity Recognition (NER) in Python.mp470.23MB

03 - Beginner's Corner/011 Text Summarization.mp424.15MB

03 - Beginner's Corner/012 Text Summarization in Python.mp445.47MB

03 - Beginner's Corner/013 Neural Machine Translation.mp428.1MB

03 - Beginner's Corner/014 Neural Machine Translation in Python.mp464.1MB

03 - Beginner's Corner/015 Question Answering.mp440.1MB

03 - Beginner's Corner/016 Question Answering in Python.mp448.15MB

03 - Beginner's Corner/017 Zero-Shot Classification.mp430.12MB

03 - Beginner's Corner/018 Zero-Shot Classification in Python.mp487.61MB

03 - Beginner's Corner/019 Beginner's Corner Section Summary.mp423.18MB

04 - Fine-Tuning (Intermediate)/001 Fine-Tuning Section Introduction.mp420.16MB

04 - Fine-Tuning (Intermediate)/002 Text Preprocessing and Tokenization Review.mp463.16MB

04 - Fine-Tuning (Intermediate)/003 Models and Tokenizers.mp464.57MB

04 - Fine-Tuning (Intermediate)/004 Models and Tokenizers in Python.mp484.26MB

04 - Fine-Tuning (Intermediate)/005 Transfer Learning & Fine-Tuning (pt 1).mp459.83MB

04 - Fine-Tuning (Intermediate)/006 Transfer Learning & Fine-Tuning (pt 2).mp449.31MB

04 - Fine-Tuning (Intermediate)/007 Transfer Learning & Fine-Tuning (pt 3).mp456.66MB

04 - Fine-Tuning (Intermediate)/008 Fine-Tuning Sentiment Analysis and the GLUE Benchmark.mp458.43MB

04 - Fine-Tuning (Intermediate)/009 Fine-Tuning Sentiment Analysis in Python.mp4130.77MB

04 - Fine-Tuning (Intermediate)/010 Fine-Tuning Transformers with Custom Dataset.mp4106.85MB

04 - Fine-Tuning (Intermediate)/011 Hugging Face AutoConfig.mp440.86MB

04 - Fine-Tuning (Intermediate)/012 Fine-Tuning with Multiple Inputs (Textual Entailment).mp428.42MB

04 - Fine-Tuning (Intermediate)/013 Fine-Tuning Transformers with Multiple Inputs in Python.mp456.66MB

04 - Fine-Tuning (Intermediate)/014 Fine-Tuning Section Summary.mp415.78MB

05 - Named Entity Recognition (NER) and POS Tagging/001 Token Classification Section Introduction.mp435.82MB

05 - Named Entity Recognition (NER) and POS Tagging/002 Data & Tokenizer (Code Preparation).mp419.33MB

05 - Named Entity Recognition (NER) and POS Tagging/003 Data & Tokenizer (Code).mp442.75MB

05 - Named Entity Recognition (NER) and POS Tagging/004 Target Alignment (Code Preparation).mp443.02MB

05 - Named Entity Recognition (NER) and POS Tagging/005 Create Tokenized Dataset (Code Preparation).mp418.33MB

05 - Named Entity Recognition (NER) and POS Tagging/006 Target Alignment (Code).mp461.64MB

05 - Named Entity Recognition (NER) and POS Tagging/007 Data Collator (Code Preparation).mp422.09MB

05 - Named Entity Recognition (NER) and POS Tagging/008 Data Collator (Code).mp416.94MB

05 - Named Entity Recognition (NER) and POS Tagging/009 Metrics (Code Preparation).mp433.44MB

05 - Named Entity Recognition (NER) and POS Tagging/010 Metrics (Code).mp439.33MB

05 - Named Entity Recognition (NER) and POS Tagging/011 Model and Trainer (Code Preparation).mp410.79MB

05 - Named Entity Recognition (NER) and POS Tagging/012 Model and Trainer (Code).mp422.18MB

05 - Named Entity Recognition (NER) and POS Tagging/013 POS Tagging & Custom Datasets (Exercise Prompt).mp421.33MB

05 - Named Entity Recognition (NER) and POS Tagging/014 POS Tagging & Custom Datasets (Solution).mp4115.13MB

05 - Named Entity Recognition (NER) and POS Tagging/015 Token Classification Section Summary.mp48.04MB

06 - Seq2Seq and Neural Machine Translation/001 Translation Section Introduction.mp418.2MB

06 - Seq2Seq and Neural Machine Translation/002 Data & Tokenizer (Code Preparation).mp424.52MB

06 - Seq2Seq and Neural Machine Translation/003 Data & Tokenizer (Code).mp434.12MB

06 - Seq2Seq and Neural Machine Translation/004 Aside Seq2Seq Basics (Optional).mp437.06MB

06 - Seq2Seq and Neural Machine Translation/005 Model Inputs (Code Preparation).mp432.41MB

06 - Seq2Seq and Neural Machine Translation/006 Model Inputs (Code).mp451.41MB

06 - Seq2Seq and Neural Machine Translation/007 Translation Metrics (BLEU Score & BERT Score) (Code Preparation).mp419.27MB

06 - Seq2Seq and Neural Machine Translation/008 Translation Metrics (BLEU Score & BERT Score) (Code).mp443.32MB

06 - Seq2Seq and Neural Machine Translation/009 Train & Evaluate (Code Preparation).mp421.27MB

06 - Seq2Seq and Neural Machine Translation/010 Train & Evaluate (Code).mp435.74MB

06 - Seq2Seq and Neural Machine Translation/011 Translation Section Summary.mp49.77MB

07 - Transformers and Attention Theory (Advanced)/001 Theory Section Introduction.mp417.14MB

07 - Transformers and Attention Theory (Advanced)/002 Basic Self-Attention.mp436.97MB

07 - Transformers and Attention Theory (Advanced)/003 Self-Attention & Scaled Dot-Product Attention.mp464.27MB

07 - Transformers and Attention Theory (Advanced)/004 Attention Efficiency.mp421.56MB

07 - Transformers and Attention Theory (Advanced)/005 Attention Mask.mp415.1MB

07 - Transformers and Attention Theory (Advanced)/006 Multi-Head Attention.mp433.71MB

07 - Transformers and Attention Theory (Advanced)/007 Transformer Block.mp429.5MB

07 - Transformers and Attention Theory (Advanced)/008 Positional Encodings.mp429.03MB

07 - Transformers and Attention Theory (Advanced)/009 Encoder Architecture.mp425.21MB

09 - Setting Up Your Environment FAQ/001 Anaconda Environment Setup.mp452.63MB

09 - Setting Up Your Environment FAQ/002 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp443.61MB

10 - Extra Help With Python Coding for Beginners FAQ/001 How to Code by Yourself (part 1).mp471.84MB

10 - Extra Help With Python Coding for Beginners FAQ/002 How to Code by Yourself (part 2).mp449.14MB

10 - Extra Help With Python Coding for Beginners FAQ/003 Proof that using Jupyter Notebook is the same as not using it.mp469.42MB

11 - Effective Learning Strategies for Machine Learning FAQ/001 How to Succeed in this Course (Long Version).mp417.87MB

11 - Effective Learning Strategies for Machine Learning FAQ/002 Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp438.95MB

11 - Effective Learning Strategies for Machine Learning FAQ/003 Machine Learning and AI Prerequisite Roadmap (pt 1).mp479.66MB

11 - Effective Learning Strategies for Machine Learning FAQ/004 Machine Learning and AI Prerequisite Roadmap (pt 2).mp4108.16MB

12 - Appendix FAQ Finale/001 What is the Appendix.mp416.37MB

12 - Appendix FAQ Finale/002 BONUS.mp437.83MB