Download Coursera | Practical Deep Learning With Python 2025 Torrent

Torrent Contents Size: 2.66 GB

Coursera | Practical Deep Learning With Python 2025

01-Deep_Learning_Components

01-Environment_Set_Up_And_Configuration

01-welcome_to_practical_deep_learning_with_python_instructions.html	HTML	7.21 KB
02-course_introduction.mp4	MP4	27.98 MB
03-environment_configuration.mp4	MP4	21.82 MB
04-system_requirements_and_pre_requisite_for_studying_deep_learning_instructions.html	HTML	4.51 KB

02-Essentials_Of_Deep_Learning

01-machine_learning_vs_deep_learning.mp4	MP4	34.27 MB
02-what_is_deep_learning.mp4	MP4	20.31 MB
03-neural_networks.mp4	MP4	42.16 MB
04-artificial_neural_network_ann.mp4	MP4	24.4 MB
05-ann_types_and_applications.mp4	MP4	17.78 MB
06-forward_propagation.mp4	MP4	20.61 MB
07-perceptron.mp4	MP4	30.93 MB
08-learning_rate.mp4	MP4	29.25 MB
09-what_is_activation_function.mp4	MP4	17.83 MB
10-activation_function_and_its_types.mp4	MP4	23.41 MB
11-importance_of_epoch.mp4	MP4	24.78 MB
12-single_layer_perceptron_define_sigmoid_function.mp4	MP4	44.01 MB
13-single_layer_perceptron_decision_boundary.mp4	MP4	77.15 MB
14-learning_rate_in_deep_learning_instructions.html	HTML	3.86 KB

03-Building_Perceptron_And_Its_Working

01-limitations_of_single_layered_perceptron.mp4	MP4	11.05 MB
02-multi_layered_perceptron.mp4	MP4	12.04 MB
03-what_is_backpropagation.mp4	MP4	10.26 MB
04-backpropagation.mp4	MP4	17 MB
05-demonstration_building_a_simple_neural_network.mp4	MP4	40.88 MB
06-demonstration_understanding_how_backpropagation_has_worked.mp4	MP4	40.45 MB
07-demonstration_handwritten_digits_classification_data_preprocessing.mp4	MP4	41.79 MB
08-demonstration_handwritten_digits_classification_designing_the_model.mp4	MP4	73.22 MB
09-demonstration_handwritten_digits_classification_optimizing_the_model.mp4	MP4	88.77 MB
10-hebbian_learning_algorithm_instructions.html	HTML	27.28 KB

04-Module_Wrap_Up_And_Assessment

01-summary_of_deep_learning_components.mp4

MP4

36.33 MB

02-Deep_Learning_With_Cnn_Rcnn_And_Faster_Rcnn

01-Convolutional_Neural_Network

01-limitations_of_mlp.mp4	MP4	27.91 MB
01. Support - Onehack.Us.txt	TXT	94 B
02-mlp_limitations_resolving_the_issue_with_cnn.mp4	MP4	21.51 MB
03-visual_cortex_and_cnn.mp4	MP4	31.61 MB
04-convolutional_layer.mp4	MP4	31.99 MB
05-working_of_convolutional_layer.mp4	MP4	31.99 MB
06-demonstration_load_and_preprocess_the_data.mp4	MP4	42.04 MB
07-demonstration_designing_the_model.mp4	MP4	52.84 MB
08-demonstration_building_the_cnn_model.mp4	MP4	37.97 MB
09-demonstration_model_accuracy.mp4	MP4	21.46 MB
10-demonstration_adding_more_layers.mp4	MP4	62.39 MB
11-demonstration_building_basic_cnn_model_with_new_parameters.mp4	MP4	78.21 MB
12-demonstration_pre_trained_model.mp4	MP4	37.38 MB
13-why_convolutions_are_important_instructions.html	HTML	2.08 KB

02-Tensorflow_Hub_For_Object_Detection_Using_Faster_Rcnn

01-classification_and_object_detection.mp4	MP4	29.81 MB
02-introduction_to_rcnn.mp4	MP4	31.51 MB
03-r_cnn_bounding_box_regression.mp4	MP4	12.46 MB
04-pre_trained_model.mp4	MP4	29.04 MB
05-fast_regional_cnn.mp4	MP4	32.1 MB
06-demonstration_creating_base_variables_and_loading_the_model.mp4	MP4	37 MB
07-demonstration_training_the_model_and_visualizing_the_predictions.mp4	MP4	53.63 MB
08-demonstration_svm_as_a_classifier.mp4	MP4	23.4 MB
09-svm_classifier_in_object_detection_instructions.html	HTML	4.26 KB

03-Faster_Rcnn_Recurrent_Convolutional_Neural_Network

01-fast_rcnn_limitations.mp4	MP4	24.9 MB
02-advent_of_faster_r_cnn.mp4	MP4	25.24 MB
03-tensorflow_hub.mp4	MP4	20.32 MB
04-demonstration_object_detection_with_faster_rcnn_pretrained_model_setup.mp4	MP4	74.66 MB
05-demonstration_object_detection_with_faster_rcnn_building_the_model.mp4	MP4	82.91 MB
06-faster_r_cnn_architecture_instructions.html	HTML	5.92 KB

04-Module_Wrap_Up_And_Assessment

	01-summary_of_cnn_in_deep_learning.mp4	MP4	13.32 MB
	02-summary_of_faster_rcnn.mp4	MP4	22.48 MB

03-Deep_Learning_With_Rnn_Lstm_And_Model_Optimization

01-Working_Of_Recurrent_Neural_Networks_Rnn

01-rnn_fundamentals.mp4	MP4	20.5 MB
02-rnn_architecture.mp4	MP4	22.59 MB
03-rnn_architecture_workflow.mp4	MP4	28.92 MB
04-implementing_rnn.mp4	MP4	28.87 MB
05-demonstration_rnn_dataset_preparation.mp4	MP4	62.04 MB
06-demonstration_rnn_building_the_model.mp4	MP4	62.38 MB
07-recurrent_neural_networks_rnns_in_deep_learning_instructions.html	HTML	19.64 KB

02-Lstm_Architecture

01-basics_of_lstm.mp4	MP4	28.36 MB
02-lstm_structure.mp4	MP4	24.24 MB
03-forget_gate_and_input_gate.mp4	MP4	20.87 MB
04-output_gate.mp4	MP4	14.09 MB
05-importance_of_lstm_architecture.mp4	MP4	23.04 MB
06-types_of_lstm.mp4	MP4	19.16 MB
07-demonstration_next_word_prediction_processing_the_corpus.mp4	MP4	50.16 MB
08-demonstration_next_word_prediction_layers.mp4	MP4	58.93 MB
09-demonstration_next_word_prediction_model_compilation_and_prediction.mp4	MP4	96.56 MB
10-attention_based_lstm_long_short_term_memory_instructions.html	HTML	7.41 KB
11-capsule_networks_in_deep_learning_instructions.html	HTML	4.17 KB

03-Module_Optimization_And_Compilation

01-improving_a_model.mp4	MP4	32.93 MB
02-model_optimization.mp4	MP4	21.84 MB
03-using_adam_optimizer.mp4	MP4	31.96 MB
04-model_compilation.mp4	MP4	14.37 MB
05-model_compilation_with_popular_frameworks.mp4	MP4	27.34 MB
06-demonstration_model_compilation_preparing_the_dataset.mp4	MP4	55.53 MB
07-demonstration_building_and_compiling_model.mp4	MP4	46.26 MB
08-demonstration_from_rmsprop_to_adam.mp4	MP4	45.17 MB
09-model_optimizers_beyond_adam_instructions.html	HTML	87.35 KB

04-Module_Wrap_Up_And_Assessment

01-summary_of_deep_learning_with_rnn_and_lstm_with_model_optimization.mp4

MP4

32.88 MB

04-Course_Wrap_Up_And_Assessment

	01-course_summary_for_practical_deep_learning_with_python.mp4	MP4	23.39 MB
	02-practice_project_mnist_fashion_dataset_analysis_instructions.html	HTML	64 KB

Resources

01-Module_3_Datasets

	history.p	P	436 B
	next_word_model.keras	KERAS	9.76 MB

02-Module_2_Datasets

resources.html

HTML

65.68 KB

Support - Onehack.Us.txt

TXT

94 B

Description

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Coursera | Practical Deep Learning With Python 2025

Torrent Contents Size: 2.66 GB

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