|
RAR-files |
| linkedin.learning.recurrent.neural.networks-xqzt.rar |
50,000,000 |
B299EF30 |
| linkedin.learning.recurrent.neural.networks-xqzt.r00 |
50,000,000 |
1B5A8F1E |
| linkedin.learning.recurrent.neural.networks-xqzt.r01 |
50,000,000 |
3BD0712C |
| linkedin.learning.recurrent.neural.networks-xqzt.r02 |
5,713,104 |
DF99D455 |
|
| Total size: |
155,713,104 |
|
|
| Archived
files |
|
01.01-getting_started_with_rnns.mkv
[88cb17867feabf02]
|
2,833,494 |
454B97AD |
|
01.02-scope_and_prerequisites_for_the_course.mkv
[ca19ebf00727ba7e]
|
3,426,010 |
CB75A699 |
|
01.03-setting_up_exercise_files.mkv
[1bc18182cb8ca7d]
|
6,731,111 |
46BDAA20 |
|
02.01-a_review_of_deep_learning.mkv
[61e4fd1c41ad5467]
|
3,591,555 |
227CC881 |
|
02.02-why_sequence_models.mkv
[905f07aa776b9d15]
|
3,256,786 |
D7D171F3 |
|
02.03-a_recurrent_neural_network.mkv
[c45e6791255f2a6b]
|
3,952,118 |
9D2DF796 |
|
02.04-types_of_rnns.mkv
[539dd76d7b47e503]
|
3,482,981 |
4D7F4F99 |
|
02.05-applications_of_rnns.mkv
[20121d243704dcce]
|
2,468,066 |
15397A66 |
|
03.01-training_rnn_models.mkv
[ad14f58f67d5dc04]
|
2,203,347 |
6792D111 |
|
03.02-forward_propagation_with_rnn.mkv
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|
3,646,758 |
DA6E77A2 |
|
03.03-computing_rnn_loss.mkv
[e1c154384a7c18c]
|
1,690,992 |
71521ADA |
|
03.04-backward_propagation_with_rnn.mkv
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|
2,255,789 |
00CFF997 |
|
03.05-predictions_with_rnn.mkv
[4b9363f438a3376c]
|
1,428,575 |
DC44AFCD |
|
04.01-a_simple_rnn_example_predicting_stock_prices.mkv
[869784a0c392f183]
|
3,979,868 |
06AC32A2 |
|
04.02-data_preprocessing_for_rnn.mkv
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|
3,389,666 |
C2D8BEA0 |
|
04.03-preparing_time_series_data_with_lookback.mkv
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|
5,466,264 |
44E3793E |
|
04.04-creating_an_rnn_model.mkv
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|
5,231,112 |
68A7ED94 |
|
04.05-testing_and_predictions_with_rnn.mkv
[24a768e455fd6e39]
|
5,713,339 |
53F99BBA |
|
05.01-the_vanishing_gradient_problem.mkv
[20c0fefad2404740]
|
3,394,474 |
CE3ACB42 |
|
05.02-the_gated_recurrent_unit.mkv
[936a336574f748f3]
|
5,254,478 |
C05AF229 |
|
05.03-long_short-term_memory.mkv
[7dead90ee082eda0]
|
3,336,782 |
715F3C56 |
|
05.04-bidirectional_rnns.mkv
[f1ac9bf41ddb1e09]
|
4,460,728 |
42A1AE10 |
|
06.01-forecasting_service_loads_with_lstm.mkv
[c390426b7d451319]
|
3,362,623 |
9CB62F62 |
|
06.02-time_series_patterns.mkv
[badbd271f950f62b]
|
5,474,816 |
83F2C22E |
|
06.03-preparing_time_series_data_for_lstm.mkv
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|
4,814,631 |
47BF5DB3 |
|
06.04-creating_an_lstm_model.mkv
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|
2,613,919 |
5100407D |
|
06.05-testing_the_lstm_model.mkv
[2104add9c58f2534]
|
5,939,412 |
F3A9A39C |
|
06.06-forecasting_service_loads_predictions.mkv
[a8c398897721b55]
|
7,332,296 |
11E3AB91 |
|
07.01-text_based_models_challenges.mkv
[11cf81358e3b27b8]
|
2,892,684 |
6A2EF080 |
|
07.02-intro_to_word_embeddings.mkv
[885e5a11caf80847]
|
7,036,808 |
B90BEFBB |
|
07.03-pretrained_word_embeddings.mkv
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|
4,449,318 |
D3D16E9E |
|
07.04-text_preprocessing_for_rnn.mkv
[da35eb2a8cc6c383]
|
3,095,038 |
75406530 |
|
07.05-creating_an_embedding_matrix.mkv
[e695a520a486512f]
|
2,115,800 |
3EA9A618 |
|
08.01-spam_detection_example_for_embeddings.mkv
[b3995257fe64c6c]
|
3,672,759 |
9CCC03F5 |
|
08.02-preparing_spam_data_for_training.mkv
[ebdc5a5d8d708faf]
|
6,358,062 |
F8F2146F |
|
08.03-building_the_embedding_matrix.mkv
[7ce6d9aa4816e1bd]
|
7,295,570 |
BA26DF72 |
|
08.04-creating_a_spam_classification_model.mkv
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|
3,885,670 |
89DAAF75 |
|
08.05-predicting_spam_with_lstm_and_word_embeddings.mkv
[852da35183d88e6]
|
2,596,360 |
A833B61A |
|
09.01-next_steps.mkv
[31eea7129af06e8b]
|
1,579,759 |
4ED887DB |
|
| Total size: |
155,709,818 |
|
|
