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  • Anonymous
  • 2021-08-29 21:08:56
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ReScene version pyReScene Auto 0.7 XQZT File size CRC
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639 2460BC1C
26,041 DADDFA1F
1,863 460CCB0A
RAR-files
packt.nlp-natural.language.processing.in.python.for.beginners-xqzt.rar 650,000,000 2FB2BBC3
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packt.nlp-natural.language.processing.in.python.for.beginners-xqzt.r21 161,963,896 2B3C470B
Total size: 14,461,963,896
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01.02-introduction_to_the_instructor.mkv [b61569ac7f8fffc] 52,673,464 B590F1F6
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08.01-yelp_reviews_classification_mini_project_introduction.mkv [aeec7cba48fd93d6] 109,174,936 0CDFA2C2
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09.05-tokenization_byte_pair_encoding_on_test_data.mkv [63c5df6592e8fbac] 81,048,999 4B55F29F
09.06-tokenization_byte_pair_encoding_implementation_get_pair_counts.mkv [514ec91a75950484] 67,038,780 9427B790
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09.08-tokenization_byte_pair_encoding_implementation_bfe_training.mkv [71e964fd16603f5f] 57,437,455 2F01B459
09.09-tokenization_byte_pair_encoding_implementation_bfe_encoding.mkv [495e375322e0be92] 51,025,068 C80E003B
09.10-tokenization_byte_pair_encoding_implementation_bfe_encoding_one_pair.mkv [12145c310261d4e2] 90,477,825 DE0ECA8A
09.11-tokenization_byte_pair_encoding_implementation_bfe_encoding_one_pair_1.mkv [7dfafeb9d05b2be0] 94,233,840 60C57C6A
10.01-word_normalization_case_folding.mkv [49f8646ce0dbd790] 30,299,614 0C35F380
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11.04-spelling_correction_minimum_edit_distance_dynamic_programming.mkv [ab835088c0b7a71e] 62,987,325 5959FA61
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11.06-spelling_correction_minimum_edit_distance_implementation.mkv [140b535a46a22c1a] 68,327,004 98EEEB39
11.07-spelling_correction_minimum_edit_distance_implementation_bug_fixing.mkv [a367f0eda5a3303c] 32,004,302 F8271FFC
11.08-spelling_correction_implementation.mkv [b723c71f7c72a520] 68,169,983 78275490
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12.04-language_model_markov_assumption_and_n-grams.mkv [1e5f13654e2a24dd] 66,546,711 305A8830
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12.07-language_model_implementation_update_counts_function.mkv [8ceb51715089e234] 58,254,700 E5712F9C
12.08-language_model_implementation_probability_model_function.mkv [619eca341ee16135] 65,246,897 2ABD079B
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12.10-language_model_implementation_sampling_text.mkv [82ae1e1f2cb38e37] 205,783,536 554D79F3
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13.12-topic_modeling_with_tfidf_1.mkv [c14f93e3fe42e7cf] 38,558,833 38E5F5B5
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13.16-topic_modeling_with_gensim.mkv [e1355705431cfa52] 189,278,785 2A532363
14.01-word_co-occurrence_matrix.mkv [d8c8f52f69bf8c4] 53,981,205 1A1FC2D9
14.02-word_co-occurrence_matrix_versus_document-term_matrix.mkv [1088bd1dacc3ea7f] 54,948,620 BF740105
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14.06-word_co-occurrence_matrix_implementation_final_function.mkv [f743b3662a03ed5f] 129,745,169 7AFC0FE1
14.07-word_co-occurrence_matrix_implementation_handling_memory_issues_on_large_corpora.mkv [ec827bf83e37dfc] 128,538,400 FA95BEFB
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14.11-latent_semantic_analysis.mkv [f57b9a3bd109b228] 34,526,261 F52B6B6E
14.12-latent_semantic_analysis_implementation.mkv [4e000f15f7aa26e0] 80,117,701 F3021706
15.01-cosine_similarity.mkv [26ff80d948f17ac8] 59,001,199 1DB3547D
15.02-cosine_similarity_getting_norms_of_vectors.mkv [6920a7d214e4b0a8] 118,510,120 B39FBBC1
15.03-cosine_similarity_normalizing_vectors.mkv [58dbc4a8f3037ea4] 71,332,790 ED5204A5
15.04-cosine_similarity_with_more_than_one_vector.mkv [61d67fc03f4c30be] 115,655,100 97F1A089
15.05-cosine_similarity_getting_most_similar_words_in_the_vocabulary.mkv [88638b38f838158f] 108,467,275 27E5950F
15.06-cosine_similarity_getting_most_similar_words_in_the_vocabulary_fixing_bug.mkv [285e0e9c8a1567de] 90,930,188 5F518770
15.07-cosine_similarity_word2vec_embeddings.mkv [cd645319bfc5d985] 97,861,179 A87E9E0F
15.08-word_analogies.mkv [a56085187bf6e301] 43,716,485 ED81280D
15.09-words_analogies_implementation_1.mkv [f90e55cad933e095] 66,182,772 A09C3E06
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15.11-word_visualizations.mkv [912c0af8fe5dec2e] 25,344,815 88961616
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16.01-static_and_dynamic_embeddings.mkv [12eb0691d36aad25] 58,463,939 DFA407B0
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16.09-word2vec_loss_function.mkv [25d82dd14d25c6cf] 35,978,001 0A25E5A3
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16.13-word2vec_implementation_driver_function.mkv [88acc15577834b44] 157,030,587 AB43F736
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19.01-dnn_gradient_descent.mkv [2ca116653196a2ea] 76,989,451 FCF77104
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9781803249193_Code.zip 63,863,218 25365CF4
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