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RAR-files |
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600,000,000 |
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| packt.deep.learning.using.keras.a.complete.and.compact.guide.for.beginners-xqzt.r06 |
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| packt.deep.learning.using.keras.a.complete.and.compact.guide.for.beginners-xqzt.r07 |
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| packt.deep.learning.using.keras.a.complete.and.compact.guide.for.beginners-xqzt.r09 |
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241ED813 |
| packt.deep.learning.using.keras.a.complete.and.compact.guide.for.beginners-xqzt.r10 |
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| Total size: |
6,636,395,757 |
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|
| Archived
files |
|
01.01-course_introduction_and_table_of_contents.mkv
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160,299,337 |
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02.01-introduction_to_ai_(artificial_intelligence)_and_machine_learning.mkv
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47,157,723 |
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02.02-introduction_to_deep_learning.mkv
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66,551,104 |
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03.01-installing_anaconda.mkv
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99,467,514 |
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04.01-assignment.mkv
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67,220,133 |
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|
04.02-flow_control-part_1.mkv
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48,048,539 |
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04.03-flow_control-part_2.mkv
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39,457,267 |
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04.04-list_and_tuples.mkv
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47,903,788 |
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04.05-dictionary_and_functions-part_1.mkv
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50,888,121 |
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04.06-dictionary_and_functions-part_2.mkv
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35,374,399 |
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05.01-numpy_basics-part_1.mkv
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38,573,140 |
86D0A29B |
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05.02-numpy_basics-part_2.mkv
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51,033,832 |
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06.01-matplotlib_basics-part_1.mkv
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45,191,712 |
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06.02-matplotlib_basics-part_2.mkv
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39,015,060 |
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07.01-pandas_basics-part_1.mkv
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57,469,714 |
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07.02-pandas_basics-part_2.mkv
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42,241,158 |
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08.01-installing_deep_learning_libraries.mkv
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52,024,688 |
508C6F6A |
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09.01-basic_structure.mkv
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|
58,765,722 |
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10.01-introduction.mkv
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44,155,981 |
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11.01-popular_types_of_activation_functions.mkv
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72,136,899 |
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12.01-popular_types_of_loss_functions.mkv
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77,888,465 |
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13.01-popular_optimizers.mkv
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73,884,579 |
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14.01-popular_neural_network_types.mkv
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65,143,899 |
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15.01-step_1-fetch_and_load_dataset.mkv
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95,278,996 |
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15.02-step_2_and_3-eda_(exploratory_data_analysis)_and_data_preparation-part_1.mkv
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141,274,228 |
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15.03-step_2_and_3-eda_and_data_preparation-part_2.mkv
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118,072,388 |
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15.04-step_4-defining_the_keras_model-part_1.mkv
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52,633,727 |
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15.05-step_4-defining_the_keras_model-part_2.mkv
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57,241,344 |
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15.06-step_5_and_6-compile_and_fit_model.mkv
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102,008,412 |
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15.07-step_7-visualize_training_and_metrics.mkv
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80,139,872 |
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15.08-step_8-prediction_using_the_model.mkv
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47,191,520 |
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16.01-heart_disease_binary_classification_model-introduction.mkv
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41,786,165 |
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16.02-step_1-fetch_and_load_data.mkv
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78,420,158 |
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16.03-step_2_and_3-eda_and_data_preparation-part_1.mkv
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69,474,783 |
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16.04-step_2_and_3-eda_and_data_preparation-part_2.mkv
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75,578,941 |
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16.05-step_4-defining_the_model.mkv
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68,880,790 |
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16.06-step_5_and_6-compile_fit_and_plot_the_model.mkv
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64,903,490 |
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16.07-step_7-predicting_heart_disease_using_model.mkv
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47,484,934 |
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17.01-introduction.mkv
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29,709,311 |
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17.02-step_1-fetch_and_load_data.mkv
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48,513,930 |
520ACF7D |
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17.03-step_2_and_3-eda_and_data_visualization.mkv
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|
113,424,011 |
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17.04-step_4-defining_the_model.mkv
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72,887,180 |
B03EBA68 |
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17.05-step_5_and_6-compile_fit_and_plot_the_model.mkv
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73,143,285 |
30990D3C |
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17.06-step_7-predicting_wine_quality_using_model.mkv
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46,180,201 |
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17.07-serialize_and_save_trained_model_for_later_use.mkv
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49,684,091 |
BF381F2D |
|
18.01-digital_image.mkv
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|
73,503,387 |
23B94B4F |
|
18.02-basic_image_processing_using_keras_functions-part_1.mkv
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|
68,870,174 |
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|
18.03-basic_image_processing_using_keras_functions-part_2.mkv
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|
67,836,046 |
061DC35C |
|
18.04-basic_image_processing_using_keras_functions-part_3.mkv
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|
45,344,377 |
628456BC |
|
19.01-keras_single_image_augmentation-part_1.mkv
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|
99,609,551 |
064B895A |
|
19.02-keras_single_image_augmentation-part_2.mkv
[26a070942521bfe6]
|
86,876,355 |
A5459EE6 |
|
19.03-keras_directory_image_augmentation.mkv
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|
100,222,798 |
A9C46572 |
|
19.04-keras_data_frame_augmentation.mkv
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99,747,620 |
0AE502F1 |
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20.01-cnn_(convolutional_neural_networks)_basics.mkv
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109,793,837 |
61744A70 |
|
20.02-stride_padding_and_flattening_concepts_of_cnn.mkv
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|
87,545,295 |
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21.01-fetch_load_and_prepare_data.mkv
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87,497,727 |
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21.02-create_test_and_train_folders.mkv
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53,185,201 |
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21.03-defining_the_model-part_1.mkv
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51,054,927 |
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21.04-defining_the_model-part_2.mkv
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82,296,878 |
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21.05-defining_the_model-part_3.mkv
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37,240,009 |
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21.06-training_and_visualization.mkv
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112,313,071 |
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21.07-save_model_for_later_use.mkv
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30,906,374 |
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21.08-load_saved_model_and_predict.mkv
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92,234,721 |
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21.09-improving_model-optimization_techniques.mkv
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31,148,627 |
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21.10-dropout_regularization.mkv
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|
64,217,722 |
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21.11-padding_and_filter_optimization.mkv
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78,729,745 |
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21.12-augmentation_optimization.mkv
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57,762,356 |
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21.13-hyper_parameter_tuning-part_1.mkv
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82,246,185 |
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21.14-hyper_parameter_tuning-part_2.mkv
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129,611,752 |
190E4864 |
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22.01-vgg_introduction.mkv
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79,853,104 |
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23.01-vgg16_and_vgg19_prediction-part_1.mkv
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|
95,120,709 |
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23.02-vgg16_and_vgg19_prediction-part_2.mkv
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45,816,724 |
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24.01-resnet50_prediction.mkv
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77,359,725 |
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25.01-vgg16-part_1.mkv
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73,645,889 |
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25.02-vgg16-part_2.mkv
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109,114,377 |
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|
26.01-vgg16_transfer_learning_flower_prediction.mkv
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27,068,256 |
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27.01-preparing_and_uploading_dataset.mkv
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83,095,862 |
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27.02-training_and_prediction.mkv
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212,974,288 |
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28.01-training_and_prediction.mkv
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29.01-training_and_prediction.mkv
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|
68,022,227 |
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9781803231587_Code.zip |
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| Total size: |
6,636,388,213 |
|
|
