Coursera | Practical Deep Learning With Python 2025

Coursera | Practical Deep Learning With Python 2025
     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 -


     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 -


     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 -


     01-summary_of_deep_learning_components.mp4 -


     01-limitations_of_mlp.mp4 -


     01. Support - Onehack.Us.txt -


     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 -


     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 -


     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 -


     01-summary_of_cnn_in_deep_learning.mp4 -


     02-summary_of_faster_rcnn.mp4 -


     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 -


     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 -


     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 -


     01-summary_of_deep_learning_with_rnn_and_lstm_with_model_optimization.mp4 -


     history.p -


     next_word_model.keras -


     resources.html -


     01-course_summary_for_practical_deep_learning_with_python.mp4 -


     02-practice_project_mnist_fashion_dataset_analysis_instructions.html -


     Support - Onehack.Us.txt -

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