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Edureka - Practical Deep Learning With Python
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Torrent Files List
01-Deep_Learning_Components (Size: 2.66 GB) (Files: 93)
01-Deep_Learning_Components
01-Environment_Set_Up_And_Configuration
01-welcome_to_practical_deep_learning_with_python_instructions.html
02-course_introduction.mp4
03-environment_configuration.mp4
04-system_requirements_and_pre_requisite_for_studying_deep_learning_instructions.html
02-Essentials_Of_Deep_Learning
01-machine_learning_vs_deep_learning.mp4
02-what_is_deep_learning.mp4
03-neural_networks.mp4
04-artificial_neural_network_ann.mp4
05-ann_types_and_applications.mp4
06-forward_propagation.mp4
07-perceptron.mp4
08-learning_rate.mp4
09-what_is_activation_function.mp4
10-activation_function_and_its_types.mp4
11-importance_of_epoch.mp4
12-single_layer_perceptron_define_sigmoid_function.mp4
13-single_layer_perceptron_decision_boundary.mp4
14-learning_rate_in_deep_learning_instructions.html
03-Building_Perceptron_And_Its_Working
01-limitations_of_single_layered_perceptron.mp4
02-multi_layered_perceptron.mp4
03-what_is_backpropagation.mp4
04-backpropagation.mp4
05-demonstration_building_a_simple_neural_network.mp4
06-demonstration_understanding_how_backpropagation_has_worked.mp4
07-demonstration_handwritten_digits_classification_data_preprocessing.mp4
08-demonstration_handwritten_digits_classification_designing_the_model.mp4
09-demonstration_handwritten_digits_classification_optimizing_the_model.mp4
10-hebbian_learning_algorithm_instructions.html
04-Module_Wrap_Up_And_Assessment
01-summary_of_deep_learning_components.mp4
02-Deep_Learning_With_Cnn_Rcnn_And_Faster_Rcnn
01-Convolutional_Neural_Network
01-limitations_of_mlp.mp4
02-mlp_limitations_resolving_the_issue_with_cnn.mp4
03-visual_cortex_and_cnn.mp4
04-convolutional_layer.mp4
05-working_of_convolutional_layer.mp4
06-demonstration_load_and_preprocess_the_data.mp4
07-demonstration_designing_the_model.mp4
08-demonstration_building_the_cnn_model.mp4
09-demonstration_model_accuracy.mp4
10-demonstration_adding_more_layers.mp4
11-demonstration_building_basic_cnn_model_with_new_parameters.mp4
12-demonstration_pre_trained_model.mp4
13-why_convolutions_are_important_instructions.html
02-Tensorflow_Hub_For_Object_Detection_Using_Faster_Rcnn
01-classification_and_object_detection.mp4
02-introduction_to_rcnn.mp4
03-r_cnn_bounding_box_regression.mp4
04-pre_trained_model.mp4
05-fast_regional_cnn.mp4
06-demonstration_creating_base_variables_and_loading_the_model.mp4
07-demonstration_training_the_model_and_visualizing_the_predictions.mp4
08-demonstration_svm_as_a_classifier.mp4
09-svm_classifier_in_object_detection_instructions.html
03-Faster_Rcnn_Recurrent_Convolutional_Neural_Network
01-fast_rcnn_limitations.mp4
02-advent_of_faster_r_cnn.mp4
03-tensorflow_hub.mp4
04-demonstration_object_detection_with_faster_rcnn_pretrained_model_setup.mp4
05-demonstration_object_detection_with_faster_rcnn_building_the_model.mp4
06-faster_r_cnn_architecture_instructions.html
04-Module_Wrap_Up_And_Assessment
01-summary_of_cnn_in_deep_learning.mp4
02-summary_of_faster_rcnn.mp4
03-Deep_Learning_With_Rnn_Lstm_And_Model_Optimization
01-Working_Of_Recurrent_Neural_Networks_Rnn
01-rnn_fundamentals.mp4
02-rnn_architecture.mp4
03-rnn_architecture_workflow.mp4
04-implementing_rnn.mp4
05-demonstration_rnn_dataset_preparation.mp4
06-demonstration_rnn_building_the_model.mp4
07-recurrent_neural_networks_rnns_in_deep_learning_instructions.html
02-Lstm_Architecture
01-basics_of_lstm.mp4
02-lstm_structure.mp4
03-forget_gate_and_input_gate.mp4
04-output_gate.mp4
05-importance_of_lstm_architecture.mp4
06-types_of_lstm.mp4
07-demonstration_next_word_prediction_processing_the_corpus.mp4
08-demonstration_next_word_prediction_layers.mp4
09-demonstration_next_word_prediction_model_compilation_and_prediction.mp4
10-attention_based_lstm_long_short_term_memory_instructions.html
11-capsule_networks_in_deep_learning_instructions.html
03-Module_Optimization_And_Compilation
01-improving_a_model.mp4
02-model_optimization.mp4
03-using_adam_optimizer.mp4
04-model_compilation.mp4
05-model_compilation_with_popular_frameworks.mp4
06-demonstration_model_compilation_preparing_the_dataset.mp4
07-demonstration_building_and_compiling_model.mp4
08-demonstration_from_rmsprop_to_adam.mp4
09-model_optimizers_beyond_adam_instructions.html
04-Module_Wrap_Up_And_Assessment
01-summary_of_deep_learning_with_rnn_and_lstm_with_model_optimization.mp4
04-Course_Wrap_Up_And_Assessment
01-course_summary_for_practical_deep_learning_with_python.mp4
02-practice_project_mnist_fashion_dataset_analysis_instructions.html
Resources
01-Module_3_Datasets
deeplearning.txt
history.p
next_word_model.keras
02-Module_2_Datasets
resources.html
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