.
01 Introduction to NLP and Deep Learning\CS224n笔记1 自然语言处理与深度学习简介.url 119B
01 Introduction to NLP and Deep Learning\Lecture 1 Natural Language Processing with Deep Learning.mp4 692.2MB
01 Introduction to NLP and Deep Learning\Lecture 1 Natural Language Processing with Deep Learning.srt 102.5KB
01 Introduction to NLP and Deep Learning\cs224n-2017-lecture1.docx 2.8MB
01 Introduction to NLP and Deep Learning\cs224n-2017-lecture1.pdf 11.9MB
01 Introduction to NLP and Deep Learning\cs224n-2017-notes1.pdf 349.7KB
01 Introduction to NLP and Deep Learning\cs229-cvxopt.pdf 164.9KB
01 Introduction to NLP and Deep Learning\cs229-linalg.pdf 200.5KB
01 Introduction to NLP and Deep Learning\cs229-prob.pdf 285.5KB
02 Word Vector Representations word2vec\5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf 109.4KB
02 Word Vector Representations word2vec\A SIMPLE BUT TOUGH-TO-BEAT BASELINE FOR SENTENCE EMBEDDINGS.pdf 316.1KB
02 Word Vector Representations word2vec\CS224n笔记2 词的向量表示：word2vec.url 156B
02 Word Vector Representations word2vec\Efficient Estimation of Word Representations in Vector Space.pdf 223.4KB
02 Word Vector Representations word2vec\Lecture 2 Word Vector Representations_ word2vec.mp4 813.4MB
02 Word Vector Representations word2vec\Lecture 2 Word Vector Representations_ word2vec.srt 97.3KB
02 Word Vector Representations word2vec\cs224n-2017-lecture2-highlight.pdf 563.4KB
02 Word Vector Representations word2vec\cs224n-2017-lecture2.pdf 2.9MB
03 Advanced Word Vector Representations\CS224n笔记3 高级词向量表示.url 163B
03 Advanced Word Vector Representations\Evaluation methods for unsupervised word embeddings.pdf 280.1KB
03 Advanced Word Vector Representations\Improving Distributional Similarity with Lessons Learned from Word Embeddings.pdf 284.3KB
03 Advanced Word Vector Representations\Lecture 3 GloVe_ Global Vectors for Word Representation.mp4 551.5MB
03 Advanced Word Vector Representations\Lecture 3 GloVe_ Global Vectors for Word Representation.srt 113.3KB
03 Advanced Word Vector Representations\Linear Algebraic Structure of Word Senses, with Applications to Polysemy.pdf 662.4KB
03 Advanced Word Vector Representations\cs224n-2017-lecture3-highlight.pdf 182.0KB
03 Advanced Word Vector Representations\cs224n-2017-lecture3.pdf 6.4MB
03 Advanced Word Vector Representations\cs224n-2017-notes2.pdf 469.4KB
03 Advanced Word Vector Representations\glove.pdf 2.5MB
04 Word Window Classification and Neural Networks\A Neural Probabilistic Language Model.pdf 136.8KB
04 Word Window Classification and Neural Networks\CS224n笔记4 Word Window分类与神经网络.url 173B
04 Word Window Classification and Neural Networks\Lecture 4 Word Window Classification and Neural Networks.mp4 375.1MB
04 Word Window Classification and Neural Networks\Lecture 4 Word Window Classification and Neural Networks.srt 107.6KB
04 Word Window Classification and Neural Networks\Natural Language Processing (almost) from Scratch.pdf 370.9KB
04 Word Window Classification and Neural Networks\backprop_old.pdf 342.8KB
04 Word Window Classification and Neural Networks\cs224n-2017-lecture4.pdf 3.4MB
04 Word Window Classification and Neural Networks\cs224n-2017-notes3.pdf 613.3KB
04 Word Window Classification and Neural Networks\cs224n-2017-review-differential-calculus.pdf 139.0KB
05 Backpropagation and Project Advice\A Primer on Neural Network Models for Natural Language Processing.pdf 701.7KB
05 Backpropagation and Project Advice\Bag of Tricks for Efficient Text Classification.pdf 69.9KB
05 Backpropagation and Project Advice\CS224n笔记5 反向传播与项目指导.url 161B
05 Backpropagation and Project Advice\Lecture 5 Backpropagation and Project Advice.mp4 418.8MB
05 Backpropagation and Project Advice\Lecture 5 Backpropagation and Project Advice.srt 107.9KB
05 Backpropagation and Project Advice\cs224n-2017-lecture5-highlight.pdf 196.6KB
05 Backpropagation and Project Advice\cs224n-2017-lecture5.pdf 3.6MB
06 Dependency Parsing\(Synthesis Lectures on Human Language Technologies) Sandra Kubler, Ryan McDonald, Joakim Nivre, Graeme Hirst-Dependency parsing-Morgan and Claypool Publishers (2009).pdf
06 Dependency Parsing\CS224n笔记6 句法分析.url 145B
06 Dependency Parsing\Globally Normalized Transition-Based Neural Networks.pdf 168.0KB
06 Dependency Parsing\Improving Distributional Similarity with Lessons Learned from Word Embeddings.pdf 281.7KB
06 Dependency Parsing\Incrementality in Deterministic Dependency Parsing.pdf 119.8KB
06 Dependency Parsing\Lecture 6 Dependency Parsing.mp4 721.6MB
06 Dependency Parsing\Universal Dependencies A cross-linguistic typology.pdf 163.4KB
06 Dependency Parsing\cs224n-2017-lecture6-highlight.pdf 907.6KB
06 Dependency Parsing\cs224n-2017-lecture6.pdf 3.3MB
06 Dependency Parsing\cs224n-2017-notes4.pdf 185.3KB
07 Introduction to TensorFlow\CS224n笔记7 TensorFlow入门.url 137B
07 Introduction to TensorFlow\Lecture 7 Introduction to TensorFlow.mp4 293.4MB
07 Introduction to TensorFlow\Visual Dialog.pdf 7.4MB
07 Introduction to TensorFlow\cs224n-2017-lecture7-highlight.pdf 2.0MB
07 Introduction to TensorFlow\cs224n-2017-tensorflow-notes.pdf 290.5KB
07 Introduction to TensorFlow\cs224n-2017-tensorflow.pdf 2.3MB
08 Recurrent Neural Networks and Language Models\CS224n笔记8 RNN和语言模型.url 150B
08 Recurrent Neural Networks and Language Models\Lecture 8 Recurrent Neural Networks and Language Models.mp4 745.6MB
08 Recurrent Neural Networks and Language Models\Structured Training for Neural Network Transition-Based Parsing.pdf 664.1KB
08 Recurrent Neural Networks and Language Models\cs224n-2017-lecture8-highlight.pdf 537.9KB
08 Recurrent Neural Networks and Language Models\cs224n-2017-lecture8.pdf 3.5MB
09 Machine translation and advanced recurrent LSTMs and GRUs\CS224n笔记9 机器翻译和高级LSTM及GRU.url 138B
09 Machine translation and advanced recurrent LSTMs and GRUs\DATA NOISING AS SMOOTHING IN NEURAL NETWORK LANGUAGE MODELS.pdf 391.3KB
09 Machine translation and advanced recurrent LSTMs and GRUs\Exploring the Limits of Language Modeling.pdf 327.4KB
09 Machine translation and advanced recurrent LSTMs and GRUs\Lecture 9 Machine Translation and Advanced Recurrent LSTMs and GRUs.mp4 587.2MB
09 Machine translation and advanced recurrent LSTMs and GRUs\SUBWORD LANGUAGE MODELING WITH NEURAL NETWORKS.pdf 56.1KB
09 Machine translation and advanced recurrent LSTMs and GRUs\cs224n-2017-lecture9-highlight.pdf 1.6MB
09 Machine translation and advanced recurrent LSTMs and GRUs\cs224n-2017-lecture9.pdf 8.1MB
09 Machine translation and advanced recurrent LSTMs and GRUs\cs224n-2017-notes5.pdf 1.1MB
10 Neural Machine Translation and Models with Attention\CS224n笔记10 NMT与Attention.url 154B
10 Neural Machine Translation and Models with Attention\Effective Approaches to Attention-based Neural Machine Translation.pdf 160.1KB
10 Neural Machine Translation and Models with Attention\Google’s Multilingual Neural Machine Translation System- Enabling Zero-Shot Translation.pdf 2.5MB
10 Neural Machine Translation and Models with Attention\Lecture 10 Neural Machine Translation and Models with Attention.mp4 365.3MB
10 Neural Machine Translation and Models with Attention\NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE.pdf 434.1KB
10 Neural Machine Translation and Models with Attention\Sequence to Sequence Learning with Neural Networks.pdf 109.5KB
10 Neural Machine Translation and Models with Attention\cs224n-2017-lecture10-highlight.pdf 381.6KB
10 Neural Machine Translation and Models with Attention\cs224n-2017-lecture10.pdf 14.0MB
11 Gated recurrent units and further topics in NMT\Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models.pdf 152.1KB
11 Gated recurrent units and further topics in NMT\CS224n笔记11 GRU和NMT的进一步话题.url 134B
11 Gated recurrent units and further topics in NMT\Lecture 11 Gated Recurrent Units and Further Topics in NMT.mp4 745.9MB
11 Gated recurrent units and further topics in NMT\Lip Reading Sentences in the Wild.pdf 2.0MB
11 Gated recurrent units and further topics in NMT\Neural Machine Translation of Rare Words with Subword Units.pdf 188.7KB
11 Gated recurrent units and further topics in NMT\On Using Very Large Target Vocabulary for Neural Machine Translation.pdf 319.9KB
11 Gated recurrent units and further topics in NMT\Pointing the Unknown Words.pdf 395.1KB
11 Gated recurrent units and further topics in NMT\cs224n-2017-lecture11-highlight.pdf 1.2MB
11 Gated recurrent units and further topics in NMT\cs224n-2017-lecture11.pdf 15.3MB
11 Gated recurrent units and further topics in NMT\cs224n-2017-notes6.pdf 580.1KB
12 End-to-end models for Speech Processing\CS224n笔记12 语音识别的end-to-end模型.url 141B
12 End-to-end models for Speech Processing\Lecture 12 End-to-End Models for Speech Processing.mp4 266.2MB
12 End-to-end models for Speech Processing\Lecture 12 End-to-End Models for Speech Processing.srt 105.4KB
12 End-to-end models for Speech Processing\cs224n-2017-lecture12.pdf 28.9MB
13 Convolutional Neural Networks\A Convolutional Neural Network for Modelling Sentences.pdf 335.0KB
13 Convolutional Neural Networks\CS224n笔记13 卷积神经网络.url 156B
13 Convolutional Neural Networks\Character-Aware Neural Language Models.pdf 469.2KB
13 Convolutional Neural Networks\Convolutional Neural Networks for Sentence Classification.pdf 176.3KB
13 Convolutional Neural Networks\Lecture 13 Convolutional Neural Networks.mp4 661.0MB
13 Convolutional Neural Networks\Lecture 13 Convolutional Neural Networks.srt 117.2KB
13 Convolutional Neural Networks\cs224n-2017-lecture13-CNNs.pdf 6.8MB
13 Convolutional Neural Networks\cs224n-2017-lecture13-highlight.pdf 1.5MB
14 Tree Recursive Neural Networks and Constituency Parsing\CS224n笔记14 Tree RNN与短语句法分析.url 182B
14 Tree Recursive Neural Networks and Constituency Parsing\Deep Reinforcement Learning for Dialogue Generation.pdf 5.0MB
14 Tree Recursive Neural Networks and Constituency Parsing\Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks.pdf 412.2KB
14 Tree Recursive Neural Networks and Constituency Parsing\Lecture 14- Tree Recursive Neural Networks and Constituency Parsing.mp4 350.5MB
14 Tree Recursive Neural Networks and Constituency Parsing\Parsing with Compositional Vector Grammars.pdf 549.0KB
14 Tree Recursive Neural Networks and Constituency Parsing\Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank.pdf 1.2MB
14 Tree Recursive Neural Networks and Constituency Parsing\cs224n-2017-lecture14-TreeRNNs.pdf 5.8MB
14 Tree Recursive Neural Networks and Constituency Parsing\cs224n-2017-lecture14-highlight.pdf 423.3KB
14 Tree Recursive Neural Networks and Constituency Parsing\cs224n-2017-notes7.pdf 1.3MB
15 Coreference Resolution\CS224n笔记15 指代消解.url 149B
15 Coreference Resolution\Deep Reinforcement Learning for Mention-Ranking Coreference Models.pdf 1.1MB
15 Coreference Resolution\Easy Victories and Uphill Battles in Coreference Resolution.pdf 267.0KB
15 Coreference Resolution\Lecture 15 Coreference Resolution.mp4 741.1MB
15 Coreference Resolution\cs224n-2017-lecture15.pdf 10.5MB
16 Dynamic Neural Networks for Question Answering\CS224n笔记16 DMN与问答系统.url 149B
16 Dynamic Neural Networks for Question Answering\Learning Program Embeddings to Propagate Feedback on Student Code.pdf 749.7KB
16 Dynamic Neural Networks for Question Answering\Lecture 16 Dynamic Neural Networks for Question Answering.mp4 459.2MB
16 Dynamic Neural Networks for Question Answering\cs224n-2017-lecture16-DMN-QA.pdf 7.1MB
16 Dynamic Neural Networks for Question Answering\cs224n-2017-lecture16-highlight.pdf 1.4MB
16 Dynamic Neural Networks for Question Answering\cs224n-2017-notes8.pdf 173.1KB
17 Issues in NLP and Possible Architectures for NLP\CS224n笔记17 NLP存在的问题与未来的架构.url 151B
17 Issues in NLP and Possible Architectures for NLP\Learning to Compose Neural Networks for Question Answering.pdf 3.0MB
17 Issues in NLP and Possible Architectures for NLP\Lecture 17 Issues in NLP and Possible Architectures for NLP.mp4 312.3MB
17 Issues in NLP and Possible Architectures for NLP\cs224n-2017-lecture17-highlight.pdf 1.3MB
17 Issues in NLP and Possible Architectures for NLP\cs224n-2017-lecture17.pdf 11.1MB
18 Tackling the Limits of Deep Learning for NLP\CS224n笔记18 挑战深度学习与自然语言处理的极限.url 160B
18 Tackling the Limits of Deep Learning for NLP\Hybrid computing using a neural network with dynamic external memory.pdf 2.6MB
18 Tackling the Limits of Deep Learning for NLP\Lecture 18 Tackling the Limits of Deep Learning for NLP.mp4 469.9MB
18 Tackling the Limits of Deep Learning for NLP\Neural Turing Machines.pdf 1.3MB
18 Tackling the Limits of Deep Learning for NLP\cs224n-2017-lecture18-highlight.pdf 1.0MB
18 Tackling the Limits of Deep Learning for NLP\cs224n-2017-lecture18.pdf 16.8MB
Midterm Review\Review Session- Midterm Review.mp4 542.5MB
Midterm Review\cs224n-2017-gradient-notes.pdf 226.6KB
Midterm Review\cs224n-midterm-review.pdf 2.7MB
Midterm Review\cs224n-practice-midterm-1.pdf 386.0KB
Midterm Review\cs224n-practice-midterm-2.pdf 327.1KB
README.url 122B
assignments\LICENSE 34.3KB
assignments\README.md 3.9KB
assignments\assignment1\Makefile 183B
assignments\assignment1\assignment1.pdf 205.3KB
assignments\assignment1\assignment1_soln.pdf 296.2KB
assignments\assignment1\collect_submission.sh 79B
assignments\assignment1\get_datasets.sh 623B
assignments\assignment1\q1_softmax.py 2.8KB
assignments\assignment1\q1_softmax.pyc 3.7KB
assignments\assignment1\q2_gradcheck.py 2.4KB
assignments\assignment1\q2_gradcheck.pyc 2.7KB
assignments\assignment1\q2_neural.py 2.8KB
assignments\assignment1\q2_sigmoid.py 1.7KB
assignments\assignment1\q2_sigmoid.pyc 2.5KB
assignments\assignment1\q3_run.py 2.2KB
assignments\assignment1\q3_sgd.py 3.7KB
assignments\assignment1\q3_sgd.pyc 4.7KB
assignments\assignment1\q3_word2vec.py 9.4KB
assignments\assignment1\q3_word2vec.pyc 10.7KB
assignments\assignment1\q3_word_vectors.png 34.4KB
assignments\assignment1\q4_dev_conf.png 29.6KB
assignments\assignment1\q4_dev_pred.txt 116.7KB
assignments\assignment1\q4_reg_v_acc.png 39.4KB
assignments\assignment1\q4_sentiment.py 7.8KB
assignments\assignment1\requirements.txt 31B
assignments\assignment1\saved_params_10000.npy 8.8MB
assignments\assignment1\saved_params_15000.npy 8.8MB
assignments\assignment1\saved_params_20000.npy 8.8MB
assignments\assignment1\saved_params_25000.npy 8.8MB
assignments\assignment1\saved_params_30000.npy 8.8MB
assignments\assignment1\saved_params_35000.npy 8.8MB
assignments\assignment1\saved_params_40000.npy 8.8MB
assignments\assignment1\saved_params_5000.npy 8.6MB
assignments\assignment1\utils\__init__.pyc 213B
assignments\assignment1\utils\datasets\glove.6B.50d.txt 163.4MB
assignments\assignment1\utils\datasets\stanfordSentimentTreebank\README.txt 2.3KB
assignments\assignment1\utils\datasets\stanfordSentimentTreebank\SOStr.txt 1.2MB
assignments\assignment1\utils\datasets\stanfordSentimentTreebank\STree.txt 1.2MB
assignments\assignment1\utils\datasets\stanfordSentimentTreebank\datasetSentences.txt 1.2MB
assignments\assignment1\utils\datasets\stanfordSentimentTreebank\datasetSplit.txt 81.8KB
assignments\assignment1\utils\datasets\stanfordSentimentTreebank\dictionary.txt 11.5MB
assignments\assignment1\utils\datasets\stanfordSentimentTreebank\original_rt_snippets.txt 1.1MB
assignments\assignment1\utils\datasets\stanfordSentimentTreebank\sentiment_labels.txt 3.1MB
assignments\assignment1\utils\glove.py 733B
assignments\assignment1\utils\glove.pyc 1.0KB
assignments\assignment1\utils\treebank.py 7.4KB
assignments\assignment1\utils\treebank.pyc 9.2KB
assignments\assignment2\assignment2-soln.pdf 325.8KB
assignments\assignment2\assignment2.pdf 315.6KB
assignments\assignment2\data\dev.conll 1.2MB
assignments\assignment2\data\dev.gold.conll 1.2MB
assignments\assignment2\data\en-cw.txt 57.7MB
assignments\assignment2\data\test.conll 1.8MB
assignments\assignment2\data\test.gold.conll 1.8MB
assignments\assignment2\data\train.conll 29.5MB
assignments\assignment2\data\train.gold.conll 29.5MB
assignments\assignment2\data\weights\checkpoint 85B
assignments\assignment2\data\weights\parser.weights.data-00000-of-00001 26.8MB
assignments\assignment2\data\weights\parser.weights.index 673B
assignments\assignment2\data\weights\parser.weights.meta 7.6MB
assignments\assignment2\model.py 4.0KB
assignments\assignment2\model.pyc 5.7KB
assignments\assignment2\q1_classifier.py 7.6KB
assignments\assignment2\q1_softmax.py 4.0KB
assignments\assignment2\q1_softmax.pyc 4.6KB
assignments\assignment2\q2_initialization.py 1.7KB
assignments\assignment2\q2_initialization.pyc 2.4KB
assignments\assignment2\q2_parser_model.py 11.2KB
assignments\assignment2\q2_parser_transitions.py 7.9KB
assignments\assignment2\q2_parser_transitions.pyc 9.4KB
assignments\assignment2\q2_test.predicted.pkl 571.7KB
assignments\assignment2\utils\__init__.pyc 213B
assignments\assignment2\utils\general_utils.py 6.1KB
assignments\assignment2\utils\general_utils.pyc 6.9KB
assignments\assignment2\utils\parser_utils.py 15.2KB
assignments\assignment2\utils\parser_utils.pyc 17.0KB
assignments\assignment3\assignment3-soln.pdf 344.6KB
assignments\assignment3\assignment3.pdf 224.0KB
assignments\assignment3\data\dev.conll 393.6KB
assignments\assignment3\data\test.masked 338.0KB
assignments\assignment3\data\tiny.conll 72.0KB
assignments\assignment3\data\train.conll 1.5MB
assignments\assignment3\data\vocab.txt 815.7KB
assignments\assignment3\data\wordVectors.txt 45.5MB
assignments\assignment3\data_util.py 5.9KB
assignments\assignment3\data_util.pyc 9.8KB
assignments\assignment3\defs.py 291B
assignments\assignment3\defs.pyc 798B
assignments\assignment3\make_submission.sh 475B
assignments\assignment3\model.py 4.0KB
assignments\assignment3\model.pyc 5.7KB
assignments\assignment3\ner_model.py 5.3KB
assignments\assignment3\ner_model.pyc 6.2KB
assignments\assignment3\q1_window.py 22.0KB
assignments\assignment3\q2_rnn.py 26.2KB
assignments\assignment3\q2_rnn_cell.py 5.0KB
assignments\assignment3\q2_rnn_cell.pyc 6.0KB
assignments\assignment3\q3-0-dynamics.png 24.6KB
assignments\assignment3\q3-1-dynamics.png 25.1KB
assignments\assignment3\q3-clip-gru.png 38.6KB
assignments\assignment3\q3-clip-rnn.png 33.4KB
assignments\assignment3\q3-noclip-gru.png 36.5KB
assignments\assignment3\q3-noclip-rnn.png 33.7KB
assignments\assignment3\q3_gru.py 13.9KB
assignments\assignment3\q3_gru_cell.py 6.6KB
assignments\assignment3\q3_gru_cell.pyc 7.0KB
assignments\assignment3\requirements.txt 28B
assignments\assignment3\results\gru\20170703_112643\checkpoint 83B
assignments\assignment3\results\gru\20170703_112643\features.pkl 229.0KB
assignments\assignment3\results\gru\20170703_112643\gru_predictions.conll 508.6KB
assignments\assignment3\results\gru\20170703_112643\log 8.6KB
assignments\assignment3\results\gru\20170703_112643\model.weights.data-00000-of-00001 11.9MB
assignments\assignment3\results\gru\20170703_112643\model.weights.index 1.3KB
assignments\assignment3\results\gru\20170703_112643\model.weights.meta 7.1MB
assignments\assignment3\results\gru\20170703_112643\results.txt 856.8KB
assignments\assignment3\results\gru\20170703_123247\checkpoint 83B
assignments\assignment3\results\gru\20170703_123247\features.pkl 229.0KB
assignments\assignment3\results\gru\20170703_123247\gru_predictions.conll 508.3KB
assignments\assignment3\results\gru\20170703_123247\log 8.6KB
assignments\assignment3\results\gru\20170703_123247\model.weights.data-00000-of-00001 11.9MB
assignments\assignment3\results\gru\20170703_123247\model.weights.index 1.3KB
assignments\assignment3\results\gru\20170703_123247\model.weights.meta 7.1MB
assignments\assignment3\results\gru\20170703_123247\results.txt 856.8KB
assignments\assignment3\results\rnn\20170702_203928\checkpoint 83B
assignments\assignment3\results\rnn\20170702_203928\features.pkl 229.0KB
assignments\assignment3\results\rnn\20170702_203928\log 8.7KB
assignments\assignment3\results\rnn\20170702_203928\model.weights.data-00000-of-00001 2.1MB
assignments\assignment3\results\rnn\20170702_203928\model.weights.index 625B
assignments\assignment3\results\rnn\20170702_203928\model.weights.meta 4.2MB
assignments\assignment3\results\rnn\20170702_203928\results.txt 856.8KB
assignments\assignment3\results\rnn\20170702_203928\rnn_predictions.conll 508.3KB
assignments\assignment3\results\rnn\20170702_210024\features.pkl 229.0KB
assignments\assignment3\results\rnn\20170702_210024\log 48B
assignments\assignment3\results\rnn\20170702_210148\checkpoint 83B
assignments\assignment3\results\rnn\20170702_210148\features.pkl 229.0KB
assignments\assignment3\results\rnn\20170702_210148\log 9.7KB
assignments\assignment3\results\rnn\20170702_210148\model.weights.data-00000-of-00001 2.1MB
assignments\assignment3\results\rnn\20170702_210148\model.weights.index 625B
assignments\assignment3\results\rnn\20170702_210148\model.weights.meta 4.2MB
assignments\assignment3\results\rnn\20170702_210148\results.txt 856.8KB
assignments\assignment3\results\rnn\20170702_210148\rnn_predictions.conll 508.3KB
assignments\assignment3\results\rnn\20170702_213222\checkpoint 83B
assignments\assignment3\results\rnn\20170702_213222\features.pkl 229.0KB
assignments\assignment3\results\rnn\20170702_213222\log 9.7KB
assignments\assignment3\results\rnn\20170702_213222\model.weights.data-00000-of-00001 2.1MB
assignments\assignment3\results\rnn\20170702_213222\model.weights.index 621B
assignments\assignment3\results\rnn\20170702_213222\model.weights.meta 4.2MB
assignments\assignment3\results\rnn\20170702_213222\results.txt 856.8KB
assignments\assignment3\results\rnn\20170702_213222\rnn_predictions.conll 508.5KB
assignments\assignment3\results\rnn\20170702_215952\features.pkl 229.0KB
assignments\assignment3\results\rnn\20170702_215952\log 48B
assignments\assignment3\results\rnn\20170702_220153\features.pkl 229.0KB
assignments\assignment3\results\rnn\20170702_220153\log 48B
assignments\assignment3\results\rnn\20170702_220230\features.pkl 229.0KB
assignments\assignment3\results\rnn\20170702_220230\log 144B
assignments\assignment3\results\rnn\20170702_220336\features.pkl 229.0KB
assignments\assignment3\results\rnn\20170702_220336\log 144B
assignments\assignment3\results\rnn\20170702_220424\features.pkl 229.0KB
assignments\assignment3\results\rnn\20170702_220424\log 144B
assignments\assignment3\results\rnn\20170702_220540\checkpoint 83B
assignments\assignment3\results\rnn\20170702_220540\features.pkl 229.0KB
assignments\assignment3\results\rnn\20170702_220540\log 5.4KB
assignments\assignment3\results\rnn\20170702_220540\model.weights.data-00000-of-00001 2.1MB
assignments\assignment3\results\rnn\20170702_220540\model.weights.index 625B
assignments\assignment3\results\rnn\20170702_220540\model.weights.meta 4.1MB
assignments\assignment3\results\rnn\20170702_223821\features.pkl 229.0KB
assignments\assignment3\results\rnn\20170702_223821\log 144B
assignments\assignment3\results\rnn\20170702_223929\features.pkl 229.0KB
assignments\assignment3\results\rnn\20170702_223929\log 144B
assignments\assignment3\results\rnn\20170702_224010\features.pkl 229.0KB
assignments\assignment3\results\rnn\20170702_224010\log 144B
assignments\assignment3\results\rnn\20170702_224032\features.pkl 229.0KB
assignments\assignment3\results\rnn\20170702_224032\log 144B
assignments\assignment3\results\rnn\20170703_090925\checkpoint 83B
assignments\assignment3\results\rnn\20170703_090925\features.pkl 229.0KB
assignments\assignment3\results\rnn\20170703_090925\log 8.7KB
assignments\assignment3\results\rnn\20170703_090925\model.weights.data-00000-of-00001 7.8MB
assignments\assignment3\results\rnn\20170703_090925\model.weights.index 725B
assignments\assignment3\results\rnn\20170703_090925\model.weights.meta 4.2MB
assignments\assignment3\results\rnn\20170703_090925\results.txt 856.7KB
assignments\assignment3\results\rnn\20170703_090925\rnn_predictions.conll 508.5KB
assignments\assignment3\results\window\20170702_191726\checkpoint 83B
assignments\assignment3\results\window\20170702_191726\features.pkl 229.0KB
assignments\assignment3\results\window\20170702_191726\log 9.8KB
assignments\assignment3\results\window\20170702_191726\model.weights.data-00000-of-00001 6.4MB
assignments\assignment3\results\window\20170702_191726\model.weights.index 613B
assignments\assignment3\results\window\20170702_191726\model.weights.meta 2.0MB
assignments\assignment3\results\window\20170702_191726\results.txt 856.7KB
assignments\assignment3\results\window\20170702_191726\window_predictions.conll 508.5KB
assignments\assignment3\results\window\20170702_192143\checkpoint 83B
assignments\assignment3\results\window\20170702_192143\features.pkl 229.0KB
assignments\assignment3\results\window\20170702_192143\log 9.8KB
assignments\assignment3\results\window\20170702_192143\model.weights.data-00000-of-00001 6.4MB
assignments\assignment3\results\window\20170702_192143\model.weights.index 613B
assignments\assignment3\results\window\20170702_192143\model.weights.meta 2.0MB
assignments\assignment3\results\window\20170702_192143\results.txt 856.7KB
assignments\assignment3\results\window\20170702_192143\window_predictions.conll 508.5KB
assignments\assignment3\results\window\20170702_192351\features.pkl 229.0KB
assignments\assignment3\results\window\20170702_192351\log 205B
assignments\assignment3\results\window\20170702_192948\checkpoint 83B
assignments\assignment3\results\window\20170702_192948\features.pkl 229.0KB
assignments\assignment3\results\window\20170702_192948\log 8.6KB
assignments\assignment3\results\window\20170702_192948\model.weights.data-00000-of-00001 6.4MB
assignments\assignment3\results\window\20170702_192948\model.weights.index 613B
assignments\assignment3\results\window\20170702_192948\model.weights.meta 2.0MB
assignments\assignment3\results\window\20170702_192948\results.txt 856.7KB
assignments\assignment3\results\window\20170702_192948\window_predictions.conll 508.5KB
assignments\assignment3\results\window\20170702_193234\checkpoint 83B
assignments\assignment3\results\window\20170702_193234\features.pkl 229.0KB
assignments\assignment3\results\window\20170702_193234\log 8.7KB
assignments\assignment3\results\window\20170702_193234\model.weights.data-00000-of-00001 6.4MB
assignments\assignment3\results\window\20170702_193234\model.weights.index 613B
assignments\assignment3\results\window\20170702_193234\model.weights.meta 2.0MB
assignments\assignment3\results\window\20170702_193234\results.txt 856.7KB
assignments\assignment3\results\window\20170702_193234\window_predictions.conll 508.5KB
assignments\assignment3\results\window\20170702_193730\checkpoint 83B
assignments\assignment3\results\window\20170702_193730\features.pkl 229.0KB
assignments\assignment3\results\window\20170702_193730\log 8.6KB
assignments\assignment3\results\window\20170702_193730\model.weights.data-00000-of-00001 6.9MB
assignments\assignment3\results\window\20170702_193730\model.weights.index 613B
assignments\assignment3\results\window\20170702_193730\model.weights.meta 2.0MB
assignments\assignment3\results\window\20170702_193730\results.txt 856.7KB
assignments\assignment3\results\window\20170702_193730\window_predictions.conll 508.5KB
assignments\assignment3\results\window\20170702_193912\checkpoint 83B
assignments\assignment3\results\window\20170702_193912\features.pkl 229.0KB
assignments\assignment3\results\window\20170702_193912\log 8.7KB
assignments\assignment3\results\window\20170702_193912\model.weights.data-00000-of-00001 7.3MB
assignments\assignment3\results\window\20170702_193912\model.weights.index 613B
assignments\assignment3\results\window\20170702_193912\model.weights.meta 2.0MB
assignments\assignment3\results\window\20170702_193912\results.txt 856.7KB
assignments\assignment3\results\window\20170702_193912\window_predictions.conll 508.5KB
assignments\assignment3\results\window\20170702_194019\checkpoint 83B
assignments\assignment3\results\window\20170702_194019\features.pkl 229.0KB
assignments\assignment3\results\window\20170702_194019\log 8.7KB
assignments\assignment3\results\window\20170702_194019\model.weights.data-00000-of-00001 6.4MB
assignments\assignment3\results\window\20170702_194019\model.weights.index 613B
assignments\assignment3\results\window\20170702_194019\model.weights.meta 2.0MB
assignments\assignment3\results\window\20170702_194019\results.txt 856.7KB
assignments\assignment3\results\window\20170702_194019\window_predictions.conll 508.6KB
assignments\assignment3\util.py 18.1KB
assignments\assignment3\util.pyc 21.8KB
cs224n-winter17-notes\README.md 90B
cs224n-winter17-notes\gradient-notes.pdf 225.9KB
cs224n-winter17-notes\gradient-notes.tex 15.4KB
cs224n-winter17-notes\notes1.aux 3.6KB
cs224n-winter17-notes\notes1.blg 1.3KB
cs224n-winter17-notes\notes1.log 39.6KB
cs224n-winter17-notes\notes1.out 735B
cs224n-winter17-notes\notes1.pdf 348.6KB
cs224n-winter17-notes\notes1.synctex.gz 133.9KB
cs224n-winter17-notes\notes1.tex 37.6KB
cs224n-winter17-notes\notes1\fig\CBOW.png 97.9KB
cs224n-winter17-notes\notes1\fig\Skip-Gram.png 109.6KB
cs224n-winter17-notes\notes1\fig\manwoman.jpg 97.1KB
cs224n-winter17-notes\notes1\fig\sigmoid.png 25.1KB
cs224n-winter17-notes\notes1\fig\tree.png 35.3KB
cs224n-winter17-notes\notes1\reference.bib 2.6KB
cs224n-winter17-notes\notes2.aux 8.0KB
cs224n-winter17-notes\notes2.log 40.0KB
cs224n-winter17-notes\notes2.out 442B
cs224n-winter17-notes\notes2.pdf 479.0KB
cs224n-winter17-notes\notes2.synctex.gz 113.7KB
cs224n-winter17-notes\notes2.tex 33.3KB
cs224n-winter17-notes\notes2\Resources\ImageBlocks.pptx 45.8KB
cs224n-winter17-notes\notes2\fig\DataSize.png 82.2KB
cs224n-winter17-notes\notes2\fig\IntrinsicEval.png 54.8KB
cs224n-winter17-notes\notes2\fig\LinearBoundary.png 34.4KB
cs224n-winter17-notes\notes2\fig\LinearBoundary2.png 39.4KB
cs224n-winter17-notes\notes2\fig\NonlinearBoundary.png 43.1KB
cs224n-winter17-notes\notes2\fig\TrainTime.png 86.6KB
cs224n-winter17-notes\notes2\fig\hyperparam.png 89.4KB
cs224n-winter17-notes\notes2\fig\pretraining.png 35.1KB
cs224n-winter17-notes\notes2\fig\retraining.png 36.4KB
cs224n-winter17-notes\notes2\fig\window.png 19.5KB
cs224n-winter17-notes\notes3.aux 7.6KB
cs224n-winter17-notes\notes3.log 51.8KB
cs224n-winter17-notes\notes3.out 404B
cs224n-winter17-notes\notes3.pdf 592.7KB
cs224n-winter17-notes\notes3.synctex.gz 153.5KB
cs224n-winter17-notes\notes3.tex 46.3KB
cs224n-winter17-notes\notes3\Resources\ImageBlocks.pptx 45.8KB
cs224n-winter17-notes\notes3\Resources\NNet.pptx 55.2KB
cs224n-winter17-notes\notes3\fig\421nnet.png 62.3KB
cs224n-winter17-notes\notes3\fig\ErrorSignal.png 38.8KB
cs224n-winter17-notes\notes3\fig\ErrorSignal2.png 20.1KB
cs224n-winter17-notes\notes3\fig\ErrorSignal3.png 36.8KB
cs224n-winter17-notes\notes3\fig\Error_Surf.png 185.3KB
cs224n-winter17-notes\notes3\fig\NonlinearBoundary.png 43.1KB
cs224n-winter17-notes\notes3\fig\SimpleFF.png 50.3KB
cs224n-winter17-notes\notes3\fig\SingleLayerNeuralNetwork.png 22.3KB
cs224n-winter17-notes\notes3\fig\dropout.png 133.0KB
cs224n-winter17-notes\notes3\fig\graph_hardtanh.png 6.2KB
cs224n-winter17-notes\notes3\fig\graph_leaky.png 6.0KB
cs224n-winter17-notes\notes3\fig\graph_relu.png 5.9KB
cs224n-winter17-notes\notes3\fig\graph_sigmoid.png 6.6KB
cs224n-winter17-notes\notes3\fig\graph_softsign.png 6.9KB
cs224n-winter17-notes\notes3\fig\graph_tanh.png 6.7KB
cs224n-winter17-notes\notes3\fig\sigmoidneuron.png 41.5KB
cs224n-winter17-notes\notes4.aux 2.0KB
cs224n-winter17-notes\notes4.log 53.4KB
cs224n-winter17-notes\notes4.out 270B
cs224n-winter17-notes\notes4.pdf 185.7KB
cs224n-winter17-notes\notes4.synctex.gz 42.5KB
cs224n-winter17-notes\notes4.tex 14.1KB
cs224n-winter17-notes\notes4\fig\dep_tree.png 53.0KB
cs224n-winter17-notes\notes4\fig\nn.tex 5.1KB
cs224n-winter17-notes\notes4\fig\transitions.png 46.9KB
cs224n-winter17-notes\notes5.aux 6.8KB
cs224n-winter17-notes\notes5.log 56.1KB
cs224n-winter17-notes\notes5.out 651B
cs224n-winter17-notes\notes5.pdf 1.1MB
cs224n-winter17-notes\notes5.synctex.gz 107.4KB
cs224n-winter17-notes\notes5.tex 32.4KB
cs224n-winter17-notes\notes5\fig\GRU.png 133.9KB
cs224n-winter17-notes\notes5\fig\LSTM.png 164.4KB
cs224n-winter17-notes\notes5\fig\bengio_03.png 87.0KB
cs224n-winter17-notes\notes5\fig\birnn.pdf 147.7KB
cs224n-winter17-notes\notes5\fig\cliping.pdf 268.3KB
cs224n-winter17-notes\notes5\fig\deepbirnn.pdf 156.4KB
cs224n-winter17-notes\notes5\fig\en_decoder.png 56.8KB
cs224n-winter17-notes\notes5\fig\nn.tex 5.1KB
cs224n-winter17-notes\notes5\fig\rnn.pdf 44.4KB
cs224n-winter17-notes\notes5\fig\rnn_loop.pdf 17.4KB
cs224n-winter17-notes\notes5\fig\rnn_node.pdf 20.8KB
cs224n-winter17-notes\notes5\fig\rnn_translate.pdf 43.1KB
cs224n-winter17-notes\notes5\fig\two_layer.pdf 22.6KB
cs224n-winter17-notes\notes5\reference.bib 315B
cs224n-winter17-notes\notes6.aux 6.6KB
cs224n-winter17-notes\notes6.log 56.4KB
cs224n-winter17-notes\notes6.out 1.4KB
cs224n-winter17-notes\notes6.pdf 580.6KB
cs224n-winter17-notes\notes6.synctex.gz 140.3KB
cs224n-winter17-notes\notes6.tex 45.5KB
cs224n-winter17-notes\notes6\BPE.png 93.5KB
cs224n-winter17-notes\notes6\BiEncoder.png 11.9KB
cs224n-winter17-notes\notes6\Decoder.png 12.4KB
cs224n-winter17-notes\notes6\Encoder.png 10.3KB
cs224n-winter17-notes\notes6\alignment.png 20.5KB
cs224n-winter17-notes\notes6\candidate_list.png 92.8KB
cs224n-winter17-notes\notes6\google_example.png 24.3KB
cs224n-winter17-notes\notes6\hybrid.png 48.3KB
cs224n-winter17-notes\notes6\longsentences.png 97.9KB
cs224n-winter17-notes\notes6\partition.png 135.9KB
cs224n-winter17-notes\notes6\pointer.png 207.2KB
cs224n-winter17-notes\notes7.aux 6.1KB
cs224n-winter17-notes\notes7.log 49.7KB
cs224n-winter17-notes\notes7.out 558B
cs224n-winter17-notes\notes7.pdf 1.3MB
cs224n-winter17-notes\notes7.synctex.gz 87.6KB
cs224n-winter17-notes\notes7.tex 34.0KB
cs224n-winter17-notes\notes7\Resources\ImageBlocks.pptx 45.8KB
cs224n-winter17-notes\notes7\Resources\NNet.pptx 55.2KB
cs224n-winter17-notes\notes7\fig\2d_convolution.png 217.1KB
cs224n-winter17-notes\notes7\fig\CNN-alternates.png 508.2KB
cs224n-winter17-notes\notes7\fig\ConstituencyParsing.png 49.6KB
cs224n-winter17-notes\notes7\fig\denpendency.png 43.1KB
cs224n-winter17-notes\notes7\fig\img1.png 127.8KB
cs224n-winter17-notes\notes7\fig\img10.png 76.1KB
cs224n-winter17-notes\notes7\fig\img11.png 41.3KB
cs224n-winter17-notes\notes7\fig\img2.png 143.7KB
cs224n-winter17-notes\notes7\fig\img3.png 47.1KB
cs224n-winter17-notes\notes7\fig\img4.png 90.2KB
cs224n-winter17-notes\notes7\fig\img5.png 78.1KB
cs224n-winter17-notes\notes7\fig\img6.png 100.0KB
cs224n-winter17-notes\notes7\fig\img7.png 138.6KB
cs224n-winter17-notes\notes7\fig\img8.png 48.2KB
cs224n-winter17-notes\notes7\fig\img9.png 99.7KB
cs224n-winter17-notes\notes7\fig\narrow_vs_wide.png 94.7KB
cs224n-winter17-notes\notes7\fig\nonsense.png 40.9KB
cs224n-winter17-notes\notes7\fig\single-conv-complete.png 108.3KB
cs224n-winter17-notes\notes7\fig\single-conv.png 61.8KB
cs224n-winter17-notes\notes8.aux 1.8KB
cs224n-winter17-notes\notes8.log 44.6KB
cs224n-winter17-notes\notes8.out 405B
cs224n-winter17-notes\notes8.pdf 173.1KB
cs224n-winter17-notes\notes8.synctex.gz 26.6KB
cs224n-winter17-notes\notes8.tex 9.9KB
cs224n-winter17-notes\notes8\fig\BiGRU.png 31.1KB
cs224n-winter17-notes\notes8\fig\DMN.png 52.8KB
cs224n-winter17-notes\review-differential-calculus.pdf 139.0KB
cs224n-winter17-notes\review-differential-calculus.tex 21.8KB
cs224n-winter17-notes\sty\code_snippet.sty 1.3KB
cs224n-winter17-notes\sty\kbordermatrix.sty 5.4KB
cs224n-winter17-notes\tensorflow.pdf 291.2KB
cs224n-winter17-notes\tensorflow.tex 15.6KB
cs224n-winter17-notes\tensorflow\fig\tensorFlow.png 208.4KB
cs224n-winter17-notes\tensorflow\nonsense.svg 44.8KB
cs224n-winter17-notes\tensorflow\reference.bib 315B
cs224n-winter17-notes\update_overleaf.sh 466B

CS224n Natural Language Processing with Deep Learning.part01.rar = 0CS224n Natural Language Processing with Deep Learning.part01.rar
CS224n Natural Language Processing with Deep Learning.part01.rar - http://rapidstone.com/19qR/CS224n_Natural_Language_Processing_with_Deep_Learning.part01.ra = 224n Natural Language Processing with Deep Learning.part01.rar
CS224n Natural Language Processing with Deep Learning.part02.rar = 0CS224n Natural Language Processing with Deep Learning.part02.rar
CS224n Natural Language Processing with Deep Learning.part02.rar - http://rapidstone.com/19qS/CS224n_Natural_Language_Processing_with_Deep_Learning.part02.ra = 224n Natural Language Processing with Deep Learning.part02.rar
CS224n Natural Language Processing with Deep Learning.part03.rar = 0CS224n Natural Language Processing with Deep Learning.part03.rar
CS224n Natural Language Processing with Deep Learning.part03.rar - http://rapidstone.com/19qT/CS224n_Natural_Language_Processing_with_Deep_Learning.part03.ra = 224n Natural Language Processing with Deep Learning.part03.rar
CS224n Natural Language Processing with Deep Learning.part04.rar = 0CS224n Natural Language Processing with Deep Learning.part04.rar
CS224n Natural Language Processing with Deep Learning.part04.rar - http://rapidstone.com/Jq0/CS224n_Natural_Language_Processing_with_Deep_Learning.part04.ra = 224n Natural Language Processing with Deep Learning.part04.rar
CS224n Natural Language Processing with Deep Learning.part05.rar = 0CS224n Natural Language Processing with Deep Learning.part05.rar
CS224n Natural Language Processing with Deep Learning.part05.rar - http://rapidstone.com/Jq1/CS224n_Natural_Language_Processing_with_Deep_Learning.part05.ra = 224n Natural Language Processing with Deep Learning.part05.rar
CS224n Natural Language Processing with Deep Learning.part06.rar = 0CS224n Natural Language Processing with Deep Learning.part06.rar
CS224n Natural Language Processing with Deep Learning.part06.rar - http://rapidstone.com/Jq2/CS224n_Natural_Language_Processing_with_Deep_Learning.part06.ra = 224n Natural Language Processing with Deep Learning.part06.rar
CS224n Natural Language Processing with Deep Learning.part07.rar = 0CS224n Natural Language Processing with Deep Learning.part07.rar
CS224n Natural Language Processing with Deep Learning.part07.rar - http://rapidstone.com/Jq3/CS224n_Natural_Language_Processing_with_Deep_Learning.part07.ra = 224n Natural Language Processing with Deep Learning.part07.rar
CS224n Natural Language Processing with Deep Learning.part08.rar = 0CS224n Natural Language Processing with Deep Learning.part08.rar
CS224n Natural Language Processing with Deep Learning.part08.rar - http://rapidstone.com/Jq4/CS224n_Natural_Language_Processing_with_Deep_Learning.part08.ra = 224n Natural Language Processing with Deep Learning.part08.rar
CS224n Natural Language Processing with Deep Learning.part09.rar = 0CS224n Natural Language Processing with Deep Learning.part09.rar
CS224n Natural Language Processing with Deep Learning.part09.rar - http://rapidstone.com/Jq5/CS224n_Natural_Language_Processing_with_Deep_Learning.part09.ra = 224n Natural Language Processing with Deep Learning.part09.rar
CS224n Natural Language Processing with Deep Learning.part10.rar = 0CS224n Natural Language Processing with Deep Learning.part10.rar
CS224n Natural Language Processing with Deep Learning.part10.rar - http://rapidstone.com/Jq6/CS224n_Natural_Language_Processing_with_Deep_Learning.part10.ra = 224n Natural Language Processing with Deep Learning.part10.rar
CS224n Natural Language Processing with Deep Learning.part11.rar = 0CS224n Natural Language Processing with Deep Learning.part11.rar
CS224n Natural Language Processing with Deep Learning.part11.rar - http://rapidstone.com/Jq7/CS224n_Natural_Language_Processing_with_Deep_Learning.part11.ra = 224n Natural Language Processing with Deep Learning.part11.rar
CS224n Natural Language Processing with Deep Learning.part12.rar = 0CS224n Natural Language Processing with Deep Learning.part12.rar
CS224n Natural Language Processing with Deep Learning.part12.rar - http://rapidstone.com/Jq8/CS224n_Natural_Language_Processing_with_Deep_Learning.part12.ra = 224n Natural Language Processing with Deep Learning.part12.rar
CS224n Natural Language Processing with Deep Learning.part13.rar = 0CS224n Natural Language Processing with Deep Learning.part13.rar
CS224n Natural Language Processing with Deep Learning.part13.rar - http://rapidstone.com/Jq9/CS224n_Natural_Language_Processing_with_Deep_Learning.part13.ra = 224n Natural Language Processing with Deep Learning.part13.rar
CS224n Natural Language Processing with Deep Learning.part14.rar = 0CS224n Natural Language Processing with Deep Learning.part14.rar
CS224n Natural Language Processing with Deep Learning.part14.rar - http://rapidstone.com/Jqa/CS224n_Natural_Language_Processing_with_Deep_Learning.part14.ra = 224n Natural Language Processing with Deep Learning.part14.rar
CS224n Natural Language Processing with Deep Learning.part15.rar = 0CS224n Natural Language Processing with Deep Learning.part15.rar
CS224n Natural Language Processing with Deep Learning.part15.rar - http://rapidstone.com/Jqb/CS224n_Natural_Language_Processing_with_Deep_Learning.part15.ra = 224n Natural Language Processing with Deep Learning.part15.rar
CS224n Natural Language Processing with Deep Learning.part16.rar = 0CS224n Natural Language Processing with Deep Learning.part16.rar
CS224n Natural Language Processing with Deep Learning.part16.rar - http://rapidstone.com/Jqc/CS224n_Natural_Language_Processing_with_Deep_Learning.part16.ra = 224n Natural Language Processing with Deep Learning.part16.rar
CS224n Natural Language Processing with Deep Learning.part17.rar = 0CS224n Natural Language Processing with Deep Learning.part17.rar
CS224n Natural Language Processing with Deep Learning.part17.rar - http://rapidstone.com/Jqd/CS224n_Natural_Language_Processing_with_Deep_Learning.part17.ra = 224n Natural Language Processing with Deep Learning.part17.rar
CS224n Natural Language Processing with Deep Learning.part18.rar = 0CS224n Natural Language Processing with Deep Learning.part18.rar
CS224n Natural Language Processing with Deep Learning.part18.rar - http://rapidstone.com/Jqe/CS224n_Natural_Language_Processing_with_Deep_Learning.part18.ra = 224n Natural Language Processing with Deep Learning.part18.rar
CS224n Natural Language Processing with Deep Learning.part19.rar = 0CS224n Natural Language Processing with Deep Learning.part19.rar
CS224n Natural Language Processing with Deep Learning.part19.rar - http://rapidstone.com/Jqf/CS224n_Natural_Language_Processing_with_Deep_Learning.part19.ra = 224n Natural Language Processing with Deep Learning.part19.rar
CS224n Natural Language Processing with Deep Learning.part20.rar = 0CS224n Natural Language Processing with Deep Learning.part20.rar
CS224n Natural Language Processing with Deep Learning.part20.rar - http://rapidstone.com/Jqg/CS224n_Natural_Language_Processing_with_Deep_Learning.part20.ra = 224n Natural Language Processing with Deep Learning.part20.rar
CS224n Natural Language Processing with Deep Learning.part21.rar = 0CS224n Natural Language Processing with Deep Learning.part21.rar
CS224n Natural Language Processing with Deep Learning.part21.rar - http://rapidstone.com/Jqi/CS224n_Natural_Language_Processing_with_Deep_Learning.part21.ra = 224n Natural Language Processing with Deep Learning.part21.rar
CS224n Natural Language Processing with Deep Learning.part22.rar = 0CS224n Natural Language Processing with Deep Learning.part22.rar
CS224n Natural Language Processing with Deep Learning.part22.rar - http://rapidstone.com/Jqj/CS224n_Natural_Language_Processing_with_Deep_Learning.part22.ra = 224n Natural Language Processing with Deep Learning.part22.rar
CS224n Natural Language Processing with Deep Learning.part23.rar = 0CS224n Natural Language Processing with Deep Learning.part23.rar
CS224n Natural Language Processing with Deep Learning.part23.rar - http://rapidstone.com/Jqh/CS224n_Natural_Language_Processing_with_Deep_Learning.part23.ra = 224n Natural Language Processing with Deep Learning.part23.rar

PW: