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)]
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