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  • U: Anonymous
  • D: 2019-08-23 20:18:17
  • C: Unknown

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ReScene version pyReScene Auto 0.7 XQZT File size CRC
Download
16,746
Stored files
5,168 CA3DEBD9
232 D7DEFDD9
RAR-files
pnlp-qie4-xqzt.rar 50,000,000 83090A66
pnlp-qie4-xqzt.r00 50,000,000 066BBC71
pnlp-qie4-xqzt.r01 50,000,000 C563AEE4
pnlp-qie4-xqzt.r02 50,000,000 3B444AC6
pnlp-qie4-xqzt.r03 50,000,000 1BEF23DE
pnlp-qie4-xqzt.r04 50,000,000 3BFB3361
pnlp-qie4-xqzt.r05 50,000,000 12589E7D
pnlp-qie4-xqzt.r06 24,256,984 33F7CAA2
Total size: 374,256,984
Archived files
5 - Performing Sentiment Analysis Using Word Embeddings\32 - Bidirectional RNNs.mp4 [cb82510b7d8bbdfe] 7,040,022 D7F40712
5 - Performing Sentiment Analysis Using Word Embeddings\28 - Numeric Representations of Words.mp4 [fc883c5b7baa320d] 4,602,341 36E67D58
5 - Performing Sentiment Analysis Using Word Embeddings\33 - Data Cleaning and Preparation.mp4 [59e67eef509d14dd] 18,487,473 D5E1DF44
5 - Performing Sentiment Analysis Using Word Embeddings\34 - Designing a Multilayer Bidirectional RNN.mp4 [70744aa186df96f8] 11,873,716 36DE142E
5 - Performing Sentiment Analysis Using Word Embeddings\31 - Multilayer RNNs.mp4 [b5964da4a3c225e5] 2,842,772 526CC570
5 - Performing Sentiment Analysis Using Word Embeddings\29 - Word Embeddings Capture Context and Meaning.mp4 [8904290f7596c67e] 6,751,974 8B5A5D60
5 - Performing Sentiment Analysis Using Word Embeddings\36 - Module Summary.mp4 [cd80f93f657eda7b] 2,081,529 4508B01E
5 - Performing Sentiment Analysis Using Word Embeddings\27 - Module Overview.mp4 [5cee21dca920d16b] 2,464,499 C7156282
5 - Performing Sentiment Analysis Using Word Embeddings\30 - Generating Analogies Using GloVe Embeddings.mp4 [3962e62323a58d94] 17,324,866 A3046244
5 - Performing Sentiment Analysis Using Word Embeddings\35 - Performing Sentiment Analysis Using an RNN.mp4 [28fbdf49f262647f] 8,188,935 F7142CE8
6 - Performing Language Translation Using Sequence-to-Sequence Models\41 - Teacher Forcing.mp4 [e6f29b852699400f] 5,198,117 B1188044
6 - Performing Language Translation Using Sequence-to-Sequence Models\40 - Representing Input and Target Sentences.mp4 [a86b36630e5f796f] 2,830,367 F95658E6
6 - Performing Language Translation Using Sequence-to-Sequence Models\38 - Using Sequences and Vectors with RNNs.mp4 [cd983fb48943af72] 5,700,499 72413178
6 - Performing Language Translation Using Sequence-to-Sequence Models\44 - Designing the Encoder and Decoder.mp4 [4e3f2c08c91c7510] 10,688,567 B2FB498F
6 - Performing Language Translation Using Sequence-to-Sequence Models\39 - Language Translation Using Encoders and Decoders.mp4 [5b766813ef7c7a05] 3,010,272 7D728A3A
6 - Performing Language Translation Using Sequence-to-Sequence Models\37 - Module Overview.mp4 [4c63fa0c57aea193] 2,056,049 77673D09
6 - Performing Language Translation Using Sequence-to-Sequence Models\46 - Translating Sentences.mp4 [f27f9ec08f5b1c9d] 9,724,548 37557A80
6 - Performing Language Translation Using Sequence-to-Sequence Models\45 - Training the Sequence-2-Sequence Model Using Teacher Forcing.mp4 [3fa39d486f09ecb9] 20,993,888 3B7CC803
6 - Performing Language Translation Using Sequence-to-Sequence Models\43 - Preparing Sentence Pairs.mp4 [86644a58993f1ec8] 11,196,067 05953221
6 - Performing Language Translation Using Sequence-to-Sequence Models\42 - Setting up Helper Functions for Language Translation.mp4 [f637ded014372e78] 7,484,848 7C666A16
6 - Performing Language Translation Using Sequence-to-Sequence Models\47 - Summary and Further Study.mp4 [c0bc52a39deb238e] 3,311,407 7B316A6D
1 - Course Overview\01 - Course Overview.mp4 [4d787ab23aa9ec47] 3,512,500 FC6F375E
3 - Performing Binary Text Classification Using Words\16 - Designing an RNN for Binary Text Classification.mp4 [2f887ba546a0e7be] 11,618,067 EB682EEB
3 - Performing Binary Text Classification Using Words\13 - Feeding Text Data into RNNs.mp4 [5aacd5a1a1144eca] 5,398,732 5D3610DD
3 - Performing Binary Text Classification Using Words\15 - Using Torchtext to Process Text Data.mp4 [efb5674774b6ff19] 19,687,493 9265AA16
3 - Performing Binary Text Classification Using Words\19 - Module Summary.mp4 [4a71adf0a34fe54] 1,998,357 A9F93C2F
3 - Performing Binary Text Classification Using Words\18 - Using LSTM Cells and Dropout.mp4 [ee40ea0b332f69eb] 5,858,461 ED4B6A51
3 - Performing Binary Text Classification Using Words\12 - Introducing torchtext to Process Text Data.mp4 [d265ffaf4b496eea] 3,797,044 F53F5ED2
3 - Performing Binary Text Classification Using Words\17 - Training the RNN.mp4 [97e7674073314790] 11,490,450 73E2A49C
3 - Performing Binary Text Classification Using Words\14 - Setup and Data Cleaning.mp4 [a5b20f36714681c9] 8,542,941 D3CDEA3B
3 - Performing Binary Text Classification Using Words\10 - Module Overview.mp4 [275e603cbfbf64e8] 1,893,678 81C1506E
3 - Performing Binary Text Classification Using Words\11 - Word Embeddings to Represent Text Data.mp4 [d18c9e8a5b5ee072] 7,624,418 C299152F
4 - Performing Multi-class Text Classification Using Characters\21 - Language Prediction Based on Names.mp4 [285a0483d2045e26] 3,378,302 93F46617
4 - Performing Multi-class Text Classification Using Characters\22 - Loading and Cleaning Data.mp4 [844dfa6094811edd] 14,751,474 0EBDD333
4 - Performing Multi-class Text Classification Using Characters\20 - Module Overview.mp4 [8f7db08c10288997] 2,147,498 F614E45D
4 - Performing Multi-class Text Classification Using Characters\25 - Predicting Language from Names.mp4 [bc7411590f4466e7] 13,642,423 D0994178
4 - Performing Multi-class Text Classification Using Characters\24 - Designing an RNN for Multiclass Text Classification.mp4 [40cb2780531ee67] 17,115,522 7369A16F
4 - Performing Multi-class Text Classification Using Characters\23 - Helper Functions to One Hot Encode Names.mp4 [5e239650fb66a294] 6,626,247 77B09B72
4 - Performing Multi-class Text Classification Using Characters\26 - Module Summary.mp4 [68bde4dbc706f4d4] 1,964,800 E158D404
2 - Implementing Recurrent Neural Networks (RNNs) in PyTorch\02 - Module Overview.mp4 [291275b1a2b99ac4] 2,084,739 22B49E75
2 - Implementing Recurrent Neural Networks (RNNs) in PyTorch\06 - Back Propagation through Time.mp4 [6d8f549622166e3e] 7,821,570 1D54827D
2 - Implementing Recurrent Neural Networks (RNNs) in PyTorch\07 - Coping with Vanishing and Exploding Gradients.mp4 [17e795b614aa8a78] 10,075,941 155EF9B4
2 - Implementing Recurrent Neural Networks (RNNs) in PyTorch\08 - Long Memory Cells.mp4 [6ed484a893f0e5cd] 10,538,947 7B5DCA44
2 - Implementing Recurrent Neural Networks (RNNs) in PyTorch\05 - Recurrent Neurons.mp4 [35bf8bce4f385bf8] 7,125,623 B95C7A39
2 - Implementing Recurrent Neural Networks (RNNs) in PyTorch\09 - Module Summary.mp4 [a302781ff4c46cef] 2,333,843 0AE225D3
2 - Implementing Recurrent Neural Networks (RNNs) in PyTorch\03 - Prerequisites and Course Outline.mp4 [4da3ea326ce80a58] 2,372,829 9CEC558C
2 - Implementing Recurrent Neural Networks (RNNs) in PyTorch\04 - RNNs for Natural Language Processing.mp4 [5ce117563a2ed931] 5,837,639 7F7D9D8C
natural-language-processing-pytorch.zip 21,156,628 2A490EB7
5 - Performing Sentiment Analysis Using Word Embeddings 0 00000000
6 - Performing Language Translation Using Sequence-to-Sequence Models 0 00000000
1 - Course Overview 0 00000000
3 - Performing Binary Text Classification Using Words 0 00000000
4 - Performing Multi-class Text Classification Using Characters 0 00000000
2 - Implementing Recurrent Neural Networks (RNNs) in PyTorch 0 00000000
Total size: 374,248,922
RAR Recovery
Not Present
Labels UNKNOWN
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Execution time: 0.0755 - Memory usage: 0.63MB
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