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Lecture 1a - Introduction.pdfhttps://patch-diff.githubusercontent.com/bryoco/lectures/blob/master/Lecture%201a%20-%20Introduction.pdf
Lecture 1a - Introduction.pdfhttps://patch-diff.githubusercontent.com/bryoco/lectures/blob/master/Lecture%201a%20-%20Introduction.pdf
Lecture 1b - Deep Neural Networks Are Our Friends.pdfhttps://patch-diff.githubusercontent.com/bryoco/lectures/blob/master/Lecture%201b%20-%20Deep%20Neural%20Networks%20Are%20Our%20Friends.pdf
Lecture 1b - Deep Neural Networks Are Our Friends.pdfhttps://patch-diff.githubusercontent.com/bryoco/lectures/blob/master/Lecture%201b%20-%20Deep%20Neural%20Networks%20Are%20Our%20Friends.pdf
Lecture 2a- Word Level Semantics.pdfhttps://patch-diff.githubusercontent.com/bryoco/lectures/blob/master/Lecture%202a-%20Word%20Level%20Semantics.pdf
Lecture 2a- Word Level Semantics.pdfhttps://patch-diff.githubusercontent.com/bryoco/lectures/blob/master/Lecture%202a-%20Word%20Level%20Semantics.pdf
Lecture 2b - Overview of the Practicals.pdfhttps://patch-diff.githubusercontent.com/bryoco/lectures/blob/master/Lecture%202b%20-%20Overview%20of%20the%20Practicals.pdf
Lecture 2b - Overview of the Practicals.pdfhttps://patch-diff.githubusercontent.com/bryoco/lectures/blob/master/Lecture%202b%20-%20Overview%20of%20the%20Practicals.pdf
Lecture 3 - Language Modelling and RNNs Part 1.pdfhttps://patch-diff.githubusercontent.com/bryoco/lectures/blob/master/Lecture%203%20-%20Language%20Modelling%20and%20RNNs%20Part%201.pdf
Lecture 3 - Language Modelling and RNNs Part 1.pdfhttps://patch-diff.githubusercontent.com/bryoco/lectures/blob/master/Lecture%203%20-%20Language%20Modelling%20and%20RNNs%20Part%201.pdf
Lecture 4 - Language Modelling and RNNs Part 2.pdfhttps://patch-diff.githubusercontent.com/bryoco/lectures/blob/master/Lecture%204%20-%20Language%20Modelling%20and%20RNNs%20Part%202.pdf
Lecture 4 - Language Modelling and RNNs Part 2.pdfhttps://patch-diff.githubusercontent.com/bryoco/lectures/blob/master/Lecture%204%20-%20Language%20Modelling%20and%20RNNs%20Part%202.pdf
Lecture 5 - Text Classification.pdfhttps://patch-diff.githubusercontent.com/bryoco/lectures/blob/master/Lecture%205%20-%20Text%20Classification.pdf
Lecture 5 - Text Classification.pdfhttps://patch-diff.githubusercontent.com/bryoco/lectures/blob/master/Lecture%205%20-%20Text%20Classification.pdf
Lecture 6 - Nvidia RNNs and GPUs.pdfhttps://patch-diff.githubusercontent.com/bryoco/lectures/blob/master/Lecture%206%20-%20Nvidia%20RNNs%20and%20GPUs.pdf
Lecture 6 - Nvidia RNNs and GPUs.pdfhttps://patch-diff.githubusercontent.com/bryoco/lectures/blob/master/Lecture%206%20-%20Nvidia%20RNNs%20and%20GPUs.pdf
README.mdhttps://patch-diff.githubusercontent.com/bryoco/lectures/blob/master/README.md
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https://patch-diff.githubusercontent.com/bryoco/lectures#preamble
Deep Natural Language Processinghttp://www.cs.ox.ac.uk/teaching/courses/2016-2017/dl/
https://patch-diff.githubusercontent.com/bryoco/lectures#lecturers
https://patch-diff.githubusercontent.com/bryoco/lectures#tas
https://patch-diff.githubusercontent.com/bryoco/lectures#timetable
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https://patch-diff.githubusercontent.com/bryoco/lectures#lectures
https://patch-diff.githubusercontent.com/bryoco/lectures#lecture-materials
https://patch-diff.githubusercontent.com/bryoco/lectures#1-lecture-1a---introduction-phil-blunsom
[video]http://media.podcasts.ox.ac.uk/comlab/deep_learning_NLP/2017-01_deep_NLP_1a_intro.mp4
https://patch-diff.githubusercontent.com/bryoco/lectures#2-lecture-1b---deep-neural-networks-are-our-friends-wang-ling
[video]http://media.podcasts.ox.ac.uk/comlab/deep_learning_NLP/2017-01_deep_NLP_1b_friends.mp4
https://patch-diff.githubusercontent.com/bryoco/lectures#3-lecture-2a--word-level-semantics-ed-grefenstette
[video]http://media.podcasts.ox.ac.uk/comlab/deep_learning_NLP/2017-01_deep_NLP_2a_lexical_semantics.mp4
https://patch-diff.githubusercontent.com/bryoco/lectures#reading
https://patch-diff.githubusercontent.com/bryoco/lectures#embeddings-basics
Firth, John R. "A synopsis of linguistic theory, 1930-1955." (1957): 1-32.http://annabellelukin.edublogs.org/files/2013/08/Firth-JR-1962-A-Synopsis-of-Linguistic-Theory-wfihi5.pdf
Curran, James Richard. "From distributional to semantic similarity." (2004).https://www.era.lib.ed.ac.uk/bitstream/handle/1842/563/IP030023.pdf?sequence=2&isAllowed=y
Collobert, Ronan, et al. "Natural language processing (almost) from scratch." Journal of Machine Learning Research 12. Aug (2011): 2493-2537.http://www.jmlr.org/papers/volume12/collobert11a/collobert11a.pdf
Mikolov, Tomas, et al. "Distributed representations of words and phrases and their compositionality." Advances in neural information processing systems. 2013.http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf
https://patch-diff.githubusercontent.com/bryoco/lectures#datasets-and-visualisation
Finkelstein, Lev, et al. "Placing search in context: The concept revisited." Proceedings of the 10th international conference on World Wide Web. ACM, 2001.http://www.iicm.tugraz.at/thesis/cguetl_diss/literatur/Kapitel07/References/Finkelstein_et_al._2002/p116-finkelstein.pdf
Hill, Felix, Roi Reichart, and Anna Korhonen. "Simlex-999: Evaluating semantic models with (genuine) similarity estimation." Computational Linguistics (2016).http://www.aclweb.org/website/old_anthology/J/J15/J15-4004.pdf
Maaten, Laurens van der, and Geoffrey Hinton. "Visualizing data using t-SNE." Journal of Machine Learning Research 9.Nov (2008): 2579-2605.http://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf
https://patch-diff.githubusercontent.com/bryoco/lectures#blog-posts
Deep Learning, NLP, and Representationshttp://colah.github.io/posts/2014-07-NLP-RNNs-Representations/
Visualizing Top Tweeps with t-SNE, in Javascripthttp://karpathy.github.io/2014/07/02/visualizing-top-tweeps-with-t-sne-in-Javascript/
https://patch-diff.githubusercontent.com/bryoco/lectures#further-reading
Hermann, Karl Moritz, and Phil Blunsom. "Multilingual models for compositional distributed semantics." arXiv preprint arXiv:1404.4641 (2014).https://arxiv.org/pdf/1404.4641.pdf
Levy, Omer, and Yoav Goldberg. "Neural word embedding as implicit matrix factorization." Advances in neural information processing systems. 2014.http://u.cs.biu.ac.il/~nlp/wp-content/uploads/Neural-Word-Embeddings-as-Implicit-Matrix-Factorization-NIPS-2014.pdf
Levy, Omer, Yoav Goldberg, and Ido Dagan. "Improving distributional similarity with lessons learned from word embeddings." Transactions of the Association for Computational Linguistics 3 (2015): 211-225.https://www.transacl.org/ojs/index.php/tacl/article/view/570/124
Ling, Wang, et al. "Two/Too Simple Adaptations of Word2Vec for Syntax Problems." HLT-NAACL. 2015.https://www.aclweb.org/anthology/N/N15/N15-1142.pdf
https://patch-diff.githubusercontent.com/bryoco/lectures#4-lecture-2b---overview-of-the-practicals-chris-dyer
[video]http://media.podcasts.ox.ac.uk/comlab/deep_learning_NLP/2017-01_deep_NLP_2b_practicals.mp4
https://patch-diff.githubusercontent.com/bryoco/lectures#5-lecture-3---language-modelling-and-rnns-part-1-phil-blunsom
[video]http://media.podcasts.ox.ac.uk/comlab/deep_learning_NLP/2017-01_deep_NLP_3_modelling_1.mp4
https://patch-diff.githubusercontent.com/bryoco/lectures#reading-1
https://patch-diff.githubusercontent.com/bryoco/lectures#textbook
Deep Learning, Chapter 10http://www.deeplearningbook.org/contents/rnn.html
https://patch-diff.githubusercontent.com/bryoco/lectures#blogs
The Unreasonable Effectiveness of Recurrent Neural Networkshttp://karpathy.github.io/2015/05/21/rnn-effectiveness/
The unreasonable effectiveness of Character-level Language Modelshttp://nbviewer.jupyter.org/gist/yoavg/d76121dfde2618422139
Explaining and illustrating orthogonal initialization for recurrent neural networkshttp://smerity.com/articles/2016/orthogonal_init.html
https://patch-diff.githubusercontent.com/bryoco/lectures#6-lecture-4---language-modelling-and-rnns-part-2-phil-blunsom
[video]http://media.podcasts.ox.ac.uk/comlab/deep_learning_NLP/2017-01_deep_NLP_4_modelling_2.mp4
https://patch-diff.githubusercontent.com/bryoco/lectures#reading-2
https://patch-diff.githubusercontent.com/bryoco/lectures#textbook-1
Deep Learning, Chapter 10http://www.deeplearningbook.org/contents/rnn.html
https://patch-diff.githubusercontent.com/bryoco/lectures#vanishing-gradients-lstms-etc
On the difficulty of training recurrent neural networks. Pascanu et al., ICML 2013.http://jmlr.csail.mit.edu/proceedings/papers/v28/pascanu13.pdf
Long Short-Term Memory. Hochreiter and Schmidhuber, Neural Computation 1997.http://dl.acm.org/citation.cfm?id=1246450
Learning Phrase Representations using RNN EncoderDecoder for Statistical Machine Translation. Cho et al, EMNLP 2014.https://arxiv.org/abs/1406.1078
Understanding LSTM Networkshttp://colah.github.io/posts/2015-08-Understanding-LSTMs/
https://patch-diff.githubusercontent.com/bryoco/lectures#dealing-with-large-vocabularies
A scalable hierarchical distributed language model. Mnih and Hinton, NIPS 2009.https://papers.nips.cc/paper/3583-a-scalable-hierarchical-distributed-language-model.pdf
A fast and simple algorithm for training neural probabilistic language models. Mnih and Teh, ICML 2012.https://www.cs.toronto.edu/~amnih/papers/ncelm.pdf
On Using Very Large Target Vocabulary for Neural Machine Translation. Jean et al., ACL 2015.http://www.aclweb.org/anthology/P15-1001
Exploring the Limits of Language Modeling. Jozefowicz et al., arXiv 2016.https://arxiv.org/abs/1602.02410
Efficient softmax approximation for GPUs. Grave et al., arXiv 2016.https://arxiv.org/abs/1609.04309
Notes on Noise Contrastive Estimation and Negative Sampling. Dyer, arXiv 2014.https://arxiv.org/abs/1410.8251
Pragmatic Neural Language Modelling in Machine Translation. Baltescu and Blunsom, NAACL 2015http://www.aclweb.org/anthology/N15-1083
https://patch-diff.githubusercontent.com/bryoco/lectures#regularisation-and-dropout
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks. Gal and Ghahramani, NIPS 2016.https://arxiv.org/abs/1512.05287
Uncertainty in Deep Learninghttp://mlg.eng.cam.ac.uk/yarin/blog_2248.html
https://patch-diff.githubusercontent.com/bryoco/lectures#other-stuff
Recurrent Highway Networks. Zilly et al., arXiv 2016.https://arxiv.org/abs/1607.03474
Capacity and Trainability in Recurrent Neural Networks. Collins et al., arXiv 2016.https://arxiv.org/abs/1611.09913
https://patch-diff.githubusercontent.com/bryoco/lectures#7-lecture-5---text-classification-karl-moritz-hermann
[video]http://media.podcasts.ox.ac.uk/comlab/deep_learning_NLP/2017-01_deep_NLP_5_text_classification.mp4
https://patch-diff.githubusercontent.com/bryoco/lectures#reading-3
Recurrent Convolutional Neural Networks for Text Classification. Lai et al. AAAI 2015.http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/download/9745/9552
A Convolutional Neural Network for Modelling Sentences, Kalchbrenner et al. ACL 2014.http://www.aclweb.org/anthology/P14-1062
Semantic compositionality through recursive matrix-vector, Socher et al. EMNLP 2012.http://nlp.stanford.edu/pubs/SocherHuvalManningNg_EMNLP2012.pdf
Understanding Convolution Neural Networks For NLPhttp://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/
Distributional Representations for Compositional Semantics, Hermann (2014).https://arxiv.org/abs/1411.3146
https://patch-diff.githubusercontent.com/bryoco/lectures#8-lecture-6---deep-nlp-on-nvidia-gpus-jeremy-appleyard
[video]http://media.podcasts.ox.ac.uk/comlab/deep_learning_NLP/2017-01_deep_NLP_6_nvidia_gpus.mp4
https://patch-diff.githubusercontent.com/bryoco/lectures#reading-4
Optimizing Performance of Recurrent Neural Networks on GPUs. Appleyard et al., arXiv 2016.https://arxiv.org/abs/1604.01946
Persistent RNNs: Stashing Recurrent Weights On-Chip, Diamos et al., ICML 2016http://jmlr.org/proceedings/papers/v48/diamos16.pdf
Efficient softmax approximation for GPUs. Grave et al., arXiv 2016.https://arxiv.org/abs/1609.04309
https://patch-diff.githubusercontent.com/bryoco/lectures#9-lecture-7---coming-soon-conditional-language-models-chris-dyer
https://patch-diff.githubusercontent.com/bryoco/lectures#reading-5
Sutskever et al. (2014) Sequence to Sequence Learning with Neural Networks.https://arxiv.org/abs/1409.3215
https://patch-diff.githubusercontent.com/bryoco/lectures#10-lecture-8---coming-soon-generating-language-with-attention-chris-dyer
https://patch-diff.githubusercontent.com/bryoco/lectures#reading-6
Bahdanau et al. (2015) Neural Machine Translation by Jointly Learning to Align and Translate.https://arxiv.org/abs/1409.0473
Xu et al. (2015) Show, Attend, and Tell: Neural Image Caption Generation with Visual Attention.https://arxiv.org/pdf/1502.03044.pdf
https://patch-diff.githubusercontent.com/bryoco/lectures#piazza
https://piazza.com/ox.ac.uk/winter2017/dnlpht2017/homehttps://piazza.com/ox.ac.uk/winter2017/dnlpht2017/home
https://patch-diff.githubusercontent.com/bryoco/lectures#assessment
https://patch-diff.githubusercontent.com/bryoco/lectures#acknowledgements
DeepMindhttp://www.deepmind.com
The University of Oxford Department of Computer Sciencehttp://www.cs.ox.ac.uk/
Nvidiahttp://www.nvidia.com
Microsoft Azurehttps://azure.microsoft.com
Readme https://patch-diff.githubusercontent.com/bryoco/lectures#readme-ov-file
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https://github.com
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Contacthttps://support.github.com?tags=dotcom-footer

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URLs of crawlers that visited me.