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Autorenporträt
Mihai Surdeanu is Associate Professor in the Computer Science Department at the University of Arizona. He works in both academia and industry on NLP systems that process and extract meaning from natural language.
Inhaltsangabe
Preface 1. Introduction 2. The perception 3. Logistic regression 4. Implementing text classfication using perceptron and LR 5. Feed forward neural networks 6. Best practices in deep learning 7. Implementing text classification with feed forward networks 8. Distributional hypothesis and representation learning 9. Implementing text classification using word embedding 10. Recurrent neural networks 11. Implementing POS tagging using RNNs 12. Contexualized embeddings and transformer networks 13. Using transformers with the hugging face library 14. Encoder-decoder methods 15. Implementing encoder-decoder methods 16. Neural architecture for NLP applications Appendix A: Overview of the python language and the key libraries Appendix B: Character endcodings: ASCII and unicode.
Preface 1. Introduction 2. The perception 3. Logistic regression 4. Implementing text classfication using perceptron and LR 5. Feed forward neural networks 6. Best practices in deep learning 7. Implementing text classification with feed forward networks 8. Distributional hypothesis and representation learning 9. Implementing text classification using word embedding 10. Recurrent neural networks 11. Implementing POS tagging using RNNs 12. Contexualized embeddings and transformer networks 13. Using transformers with the hugging face library 14. Encoder-decoder methods 15. Implementing encoder-decoder methods 16. Neural architecture for NLP applications Appendix A: Overview of the python language and the key libraries Appendix B: Character endcodings: ASCII and unicode.
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