1.21 GB | 00:26:41 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English
Files Included :
Appendix A Competitions, discussion, and blog (11.14 MB)
Appendix A Kaggle primer (20.78 MB)
Appendix B Introduction to fundamental deep learning tools (18.12 MB)
Appendix B Keras, fast ai, and Transformers by Hugging Face (11.42 MB)
Appendix B PyTorch (5.13 MB)
Appendix B TensorFlow (9.53 MB)
Chapter 1 A brief history of NLP advances (51.71 MB)
Chapter 1 Summary (7.07 MB)
Chapter 1 Transfer learning in computer vision (26.74 MB)
Chapter 1 Understanding NLP in the context of AI (54.35 MB)
Chapter 1 What is transfer learning (32.3 MB)
Chapter 1 Why is NLP transfer learning an exciting topic to study now (8.51 MB)
Chapter 10 Adapters (10.71 MB)
Chapter 10 ALBERT, adapters, and multitask adaptation strategies (40.66 MB)
Chapter 10 Multitask fine-tuning (38.3 MB)
Chapter 10 Summary (1.53 MB)
Chapter 11 Conclusions (67.63 MB)
Chapter 11 Ethical and environmental considerations (24.37 MB)
Chapter 11 Final words (3.05 MB)
Chapter 11 Future of transfer learning in NLP (22.34 MB)
Chapter 11 Other emerging research trends (53.56 MB)
Chapter 11 Staying up-to-date (21.2 MB)
Chapter 11 Summary (6.42 MB)
Chapter 2 Generalized linear models (15.41 MB)
Chapter 2 Getting started with baselines Data preprocessing (61.91 MB)
Chapter 2 Preprocessing movie sentiment classification example data (9.75 MB)
Chapter 2 Summary (2.81 MB)
Chapter 3 Getting started with baselines Benchmarking and optimization (26.89 MB)
Chapter 3 Neural network models (38.91 MB)
Chapter 3 Optimizing performance (17.9 MB)
Chapter 3 Summary (3.9 MB)
Chapter 4 Domain adaptation (22.77 MB)
Chapter 4 Multitask learning (18.46 MB)
Chapter 4 Semisupervised learning with higher-level representations (12.17 MB)
Chapter 4 Shallow transfer learning for NLP (45.75 MB)
Chapter 4 Summary (4.62 MB)
Chapter 5 Preprocessing data for recurrent neural network deep transfer learning experiments (40.83 MB)
Chapter 5 Preprocessing fact-checking example data (10.11 MB)
Chapter 5 Summary (1.5 MB)
Chapter 6 Deep transfer learning for NLP with recurrent neural networks (36.87 MB)
Chapter 6 Embeddings from Language Models (ELMo) (18.42 MB)
Chapter 6 Summary (4.1 MB)
Chapter 6 Universal Language Model Fine-Tuning (ULMFiT) (14.01 MB)
Chapter 7 Deep transfer learning for NLP with the transformer and GPT (80.66 MB)
Chapter 7 Summary (2.36 MB)
Chapter 7 The Generative Pretrained Transformer (43.77 MB)
Chapter 8 Cross-lingual learning with multilingual BERT (mBERT) (26.1 MB)
Chapter 8 Deep transfer learning for NLP with BERT and multilingual BERT (55.06 MB)
Chapter 8 Summary (2.77 MB)
Chapter 9 Knowledge distillation (30.43 MB)
Chapter 9 Summary (1.39 MB)
Chapter 9 ULMFiT and knowledge distillation adaptation strategies (42.27 MB)
Part 1 Introduction and overview (1.59 MB)
Part 2 Shallow transfer learning and deep transfer learning with recurrent neural networks (RNNs) (1.25 MB)
Part 3 Deep transfer learning with transformers and adaptation strategies (2.46 MB)
Appendix A Kaggle primer (20.78 MB)
Appendix B Introduction to fundamental deep learning tools (18.12 MB)
Appendix B Keras, fast ai, and Transformers by Hugging Face (11.42 MB)
Appendix B PyTorch (5.13 MB)
Appendix B TensorFlow (9.53 MB)
Chapter 1 A brief history of NLP advances (51.71 MB)
Chapter 1 Summary (7.07 MB)
Chapter 1 Transfer learning in computer vision (26.74 MB)
Chapter 1 Understanding NLP in the context of AI (54.35 MB)
Chapter 1 What is transfer learning (32.3 MB)
Chapter 1 Why is NLP transfer learning an exciting topic to study now (8.51 MB)
Chapter 10 Adapters (10.71 MB)
Chapter 10 ALBERT, adapters, and multitask adaptation strategies (40.66 MB)
Chapter 10 Multitask fine-tuning (38.3 MB)
Chapter 10 Summary (1.53 MB)
Chapter 11 Conclusions (67.63 MB)
Chapter 11 Ethical and environmental considerations (24.37 MB)
Chapter 11 Final words (3.05 MB)
Chapter 11 Future of transfer learning in NLP (22.34 MB)
Chapter 11 Other emerging research trends (53.56 MB)
Chapter 11 Staying up-to-date (21.2 MB)
Chapter 11 Summary (6.42 MB)
Chapter 2 Generalized linear models (15.41 MB)
Chapter 2 Getting started with baselines Data preprocessing (61.91 MB)
Chapter 2 Preprocessing movie sentiment classification example data (9.75 MB)
Chapter 2 Summary (2.81 MB)
Chapter 3 Getting started with baselines Benchmarking and optimization (26.89 MB)
Chapter 3 Neural network models (38.91 MB)
Chapter 3 Optimizing performance (17.9 MB)
Chapter 3 Summary (3.9 MB)
Chapter 4 Domain adaptation (22.77 MB)
Chapter 4 Multitask learning (18.46 MB)
Chapter 4 Semisupervised learning with higher-level representations (12.17 MB)
Chapter 4 Shallow transfer learning for NLP (45.75 MB)
Chapter 4 Summary (4.62 MB)
Chapter 5 Preprocessing data for recurrent neural network deep transfer learning experiments (40.83 MB)
Chapter 5 Preprocessing fact-checking example data (10.11 MB)
Chapter 5 Summary (1.5 MB)
Chapter 6 Deep transfer learning for NLP with recurrent neural networks (36.87 MB)
Chapter 6 Embeddings from Language Models (ELMo) (18.42 MB)
Chapter 6 Summary (4.1 MB)
Chapter 6 Universal Language Model Fine-Tuning (ULMFiT) (14.01 MB)
Chapter 7 Deep transfer learning for NLP with the transformer and GPT (80.66 MB)
Chapter 7 Summary (2.36 MB)
Chapter 7 The Generative Pretrained Transformer (43.77 MB)
Chapter 8 Cross-lingual learning with multilingual BERT (mBERT) (26.1 MB)
Chapter 8 Deep transfer learning for NLP with BERT and multilingual BERT (55.06 MB)
Chapter 8 Summary (2.77 MB)
Chapter 9 Knowledge distillation (30.43 MB)
Chapter 9 Summary (1.39 MB)
Chapter 9 ULMFiT and knowledge distillation adaptation strategies (42.27 MB)
Part 1 Introduction and overview (1.59 MB)
Part 2 Shallow transfer learning and deep transfer learning with recurrent neural networks (RNNs) (1.25 MB)
Part 3 Deep transfer learning with transformers and adaptation strategies (2.46 MB)
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