Packt Deep Learning Using Keras A Complete and Compact Guide for Beginners-XQZT

Another exquisit release
Packt.Deep.Learning.Using.Keras.A.Complete.and.Compact.Guide.for.Beginners-XQZT
     Title: Deep Learning Using Keras - A Complete and Compact Guide for Beginners
 Publisher: Packt
  Category: Data
      Size: 6354M
     Files: 12F
      Date: 2021-11-19
  Course #: 9781803242835
 Published: Packt
   Updated: N/A
       URL: https://www.packtpub.com/video/data/9781803242835
    Author: Abhilash Nelson
  Duration: 9 hours 34 minutes
 Exer/Code: [X]
Installation:
Unpack that shit, run that shit
Description:
Learn deep learning from scratch using Python and Keras

153M	01.01-course_introduction_and_table_of_contents.mkv
45M	02.01-introduction_to_ai_(artificial_intelligence)_and_machine_learning.mkv
64M	02.02-introduction_to_deep_learning.mkv
95M	03.01-installing_anaconda.mkv
65M	04.01-assignment.mkv
46M	04.02-flow_control-part_1.mkv
38M	04.03-flow_control-part_2.mkv
46M	04.04-list_and_tuples.mkv
49M	04.05-dictionary_and_functions-part_1.mkv
34M	04.06-dictionary_and_functions-part_2.mkv
37M	05.01-numpy_basics-part_1.mkv
49M	05.02-numpy_basics-part_2.mkv
44M	06.01-matplotlib_basics-part_1.mkv
38M	06.02-matplotlib_basics-part_2.mkv
55M	07.01-pandas_basics-part_1.mkv
41M	07.02-pandas_basics-part_2.mkv
50M	08.01-installing_deep_learning_libraries.mkv
57M	09.01-basic_structure.mkv
43M	10.01-introduction.mkv
69M	11.01-popular_types_of_activation_functions.mkv
75M	12.01-popular_types_of_loss_functions.mkv
71M	13.01-popular_optimizers.mkv
63M	14.01-popular_neural_network_types.mkv
91M	15.01-step_1-fetch_and_load_dataset.mkv
135M	15.02-step_2_and_3-eda_(exploratory_data_analysis)_and_data_preparation-part_1.mkv
113M	15.03-step_2_and_3-eda_and_data_preparation-part_2.mkv
51M	15.04-step_4-defining_the_keras_model-part_1.mkv
55M	15.05-step_4-defining_the_keras_model-part_2.mkv
98M	15.06-step_5_and_6-compile_and_fit_model.mkv
77M	15.07-step_7-visualize_training_and_metrics.mkv
46M	15.08-step_8-prediction_using_the_model.mkv
40M	16.01-heart_disease_binary_classification_model-introduction.mkv
75M	16.02-step_1-fetch_and_load_data.mkv
67M	16.03-step_2_and_3-eda_and_data_preparation-part_1.mkv
73M	16.04-step_2_and_3-eda_and_data_preparation-part_2.mkv
66M	16.05-step_4-defining_the_model.mkv
62M	16.06-step_5_and_6-compile_fit_and_plot_the_model.mkv
46M	16.07-step_7-predicting_heart_disease_using_model.mkv
29M	17.01-introduction.mkv
47M	17.02-step_1-fetch_and_load_data.mkv
109M	17.03-step_2_and_3-eda_and_data_visualization.mkv
70M	17.04-step_4-defining_the_model.mkv
70M	17.05-step_5_and_6-compile_fit_and_plot_the_model.mkv
45M	17.06-step_7-predicting_wine_quality_using_model.mkv
48M	17.07-serialize_and_save_trained_model_for_later_use.mkv
71M	18.01-digital_image.mkv
66M	18.02-basic_image_processing_using_keras_functions-part_1.mkv
65M	18.03-basic_image_processing_using_keras_functions-part_2.mkv
44M	18.04-basic_image_processing_using_keras_functions-part_3.mkv
95M	19.01-keras_single_image_augmentation-part_1.mkv
83M	19.02-keras_single_image_augmentation-part_2.mkv
96M	19.03-keras_directory_image_augmentation.mkv
96M	19.04-keras_data_frame_augmentation.mkv
105M	20.01-cnn_(convolutional_neural_networks)_basics.mkv
84M	20.02-stride_padding_and_flattening_concepts_of_cnn.mkv
84M	21.01-fetch_load_and_prepare_data.mkv
51M	21.02-create_test_and_train_folders.mkv
49M	21.03-defining_the_model-part_1.mkv
79M	21.04-defining_the_model-part_2.mkv
36M	21.05-defining_the_model-part_3.mkv
108M	21.06-training_and_visualization.mkv
30M	21.07-save_model_for_later_use.mkv
88M	21.08-load_saved_model_and_predict.mkv
30M	21.09-improving_model-optimization_techniques.mkv
62M	21.10-dropout_regularization.mkv
76M	21.11-padding_and_filter_optimization.mkv
56M	21.12-augmentation_optimization.mkv
79M	21.13-hyper_parameter_tuning-part_1.mkv
124M	21.14-hyper_parameter_tuning-part_2.mkv
77M	22.01-vgg_introduction.mkv
91M	23.01-vgg16_and_vgg19_prediction-part_1.mkv
44M	23.02-vgg16_and_vgg19_prediction-part_2.mkv
74M	24.01-resnet50_prediction.mkv
71M	25.01-vgg16-part_1.mkv
105M	25.02-vgg16-part_2.mkv
26M	26.01-vgg16_transfer_learning_flower_prediction.mkv
80M	27.01-preparing_and_uploading_dataset.mkv
204M	27.02-training_and_prediction.mkv
74M	28.01-training_and_prediction.mkv
65M	29.01-training_and_prediction.mkv
862M	9781803231587_Code.zip
6.2G	total