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Department of Computer Science and Technology – Course pages 2017–18: Deep learning for natural language processing – Course materials

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Search Advanced search A–Z Contact us Department of Computer Science and Technology Computer Laboratory Teaching Courses 2017–18 Deep learning for natural language processing Course materials Advanced Operating Systems Advanced topics in mobile and sensor systems and data modelling Affective Computing Algebraic Path Problems Category Theory, Type Theory and Logic Chip Multiprocessors Computer Security: Principles and Foundations Computer Vision Interactive Formal Verification Introduction to Natural Language Syntax and Parsing Introduction to networking and systems measurements Large-scale data processing and optimisation Machine Learning and Algorithms for Data Mining Machine Learning for Language Processing Modern Compiler Design Multicore Semantics and Programming Network Architectures Overview of Natural Language Processing Probabilistic Machine Learning Research Skills Programme Research Students Lectures Special topic MT Advanced Functional Programming Advanced Topics in Comput

Computer Laboratory Teaching Courses 2017–18 Deep learning for natural language processing Course materials Course pages 2017–18 Deep learning for natural language processing Lectures 1. Introduction to Neural Networks for NLP (Clark) [PDF] 2. Feedforward Neural Networks for NLP (Clark) [PDF] 3. Training and Optimization (Clark) [PDF] 4. Word Embeddings (Hill) [PDF] 5. Recurrent Neural Networks (Hill) [PDF] 7. Tensorflow (Clark) [PDF] 8. Long Short Term Memory (Hill) [PDF] 9. Conditional Language Modeling (Dyer) [PDF] 10. Better Conditional Language Modeling (Dyer) [PDF] 11.…

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