人工智能
卡内基梅隆大学 - 神经网络 NLP 2020
课程目录
- 1. CMU Neural Nets for NLP 2020 (1) - Introduction
- 2. CMU Neural Nets for NLP 2020 (2) - Language Modeling Efficiency_Training Tri
- 3. CMU Neural Nets for NLP 2020 (3) - Convolutional Neural Networks for Text
- 4. CMU Neural Nets for NLP 2020 (4) - Recurrent Neural Networks
- 5. CMU Neural Nets for NLP 2020 (5) - Efficiency Tricks for Neural Nets
- 6. CMU Neural Nets for NLP 2020 (7) - Attention
- 7. CMU Neural Nets for NLP 2020 (8) - Distributional Semantics and Word Vectors
- 8. CMU Neural Nets for NLP 2020 (9) - Sentence and Contextual Word Representatio
- 9. CMU Neural Nets for NLP 2020 (10) - Debugging Neural Nets (for NLP)
- 10. CMU Neural Nets for NLP 2020 (11) - Structured Prediction with Local Indepen
- 11. CMU Neural Nets for NLP 2020 (12) - Generating Trees Incrementally
- 12. CMU Neural Nets for NLP 2020 (13) - Generating Trees Incrementally
- 13. CMU Neural Nets for NLP 2020 (14) - Search-based Structured Prediction
- 14. CMU Neural Nets for NLP 2020 (15) - Minimum Risk Training and Reinforcement
- 15. CMU Neural Nets for NLP 2020 (16) - Advanced Search Algorithms
- 16. CMU Neural Nets for NLP 2020 (17) - Adversarial Methods
- 17. CMU Neural Nets for NLP 2020 (18) - Models w_ Latent Random Variables
- 18. CMU Neural Nets for NLP 2020 (19) - Unsupervised and Semi-supervised Learnin
- 19. CMU Neural Nets for NLP 2020 (20) - Multitask and Multilingual Learning
- 20. CMU Neural Nets for NLP 2020 (21) - Document Level Models
- 21. CMU Neural Nets for NLP 2020 (22) - Neural Nets Knowledge Bases
- 22. CMU Neural Nets for NLP 2020 (23) - Machine Reading w_ Neural Nets
- 23. CMU Neural Nets for NLP 2020 (24) - Natural Language Generation
- 24. CMU Neural Nets for NLP 2020 (25) - Model Interpretation
课程介绍
神经网络促进了语言建模的快速发展，并且已被用于优化很多其他NLP任务，甚至解决很多过去不容易的新问题。本课程将首先对神经网络进行简要概述，然后主要讲解如何将神经网络应用于自然语言问题。每个部分都将以自然语言任务入手，介绍一个特定的问题或现象，描述为何难以建模，并演示一些旨在解决该问题的模型。在这样做的过程中，该课程将涵盖可用于创建神经网络模型的不同技术，包罗处理大小可变和结构化的句子，有效处理大数据，半监督和无监督学习，结构化预测以及多语言建模。
