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| 01. Support - Onehack.Us.txt |
94B |
| 01-basics_of_lstm.mp4 |
28.36MB |
| 01-classification_and_object_detection.mp4 |
29.81MB |
| 01-course_summary_for_practical_deep_learning_with_python.mp4 |
23.39MB |
| 01-fast_rcnn_limitations.mp4 |
24.90MB |
| 01-improving_a_model.mp4 |
32.93MB |
| 01-limitations_of_mlp.mp4 |
27.91MB |
| 01-limitations_of_single_layered_perceptron.mp4 |
11.05MB |
| 01-machine_learning_vs_deep_learning.mp4 |
34.27MB |
| 01-rnn_fundamentals.mp4 |
20.50MB |
| 01-summary_of_cnn_in_deep_learning.mp4 |
13.32MB |
| 01-summary_of_deep_learning_components.mp4 |
36.33MB |
| 01-summary_of_deep_learning_with_rnn_and_lstm_with_model_optimization.mp4 |
32.88MB |
| 01-welcome_to_practical_deep_learning_with_python_instructions.html |
7.21KB |
| 02-advent_of_faster_r_cnn.mp4 |
25.24MB |
| 02-course_introduction.mp4 |
27.98MB |
| 02-introduction_to_rcnn.mp4 |
31.51MB |
| 02-lstm_structure.mp4 |
24.24MB |
| 02-mlp_limitations_resolving_the_issue_with_cnn.mp4 |
21.51MB |
| 02-model_optimization.mp4 |
21.84MB |
| 02-multi_layered_perceptron.mp4 |
12.04MB |
| 02-practice_project_mnist_fashion_dataset_analysis_instructions.html |
64.00KB |
| 02-rnn_architecture.mp4 |
22.59MB |
| 02-summary_of_faster_rcnn.mp4 |
22.48MB |
| 02-what_is_deep_learning.mp4 |
20.31MB |
| 03-environment_configuration.mp4 |
21.82MB |
| 03-forget_gate_and_input_gate.mp4 |
20.87MB |
| 03-neural_networks.mp4 |
42.16MB |
| 03-r_cnn_bounding_box_regression.mp4 |
12.46MB |
| 03-rnn_architecture_workflow.mp4 |
28.92MB |
| 03-tensorflow_hub.mp4 |
20.32MB |
| 03-using_adam_optimizer.mp4 |
31.96MB |
| 03-visual_cortex_and_cnn.mp4 |
31.61MB |
| 03-what_is_backpropagation.mp4 |
10.26MB |
| 04-artificial_neural_network_ann.mp4 |
24.40MB |
| 04-backpropagation.mp4 |
17.00MB |
| 04-convolutional_layer.mp4 |
31.99MB |
| 04-demonstration object detection with faster rcnn pretrained model setup mp4 |
74.66MB |
| 04-implementing_rnn.mp4 |
28.87MB |
| 04-model_compilation.mp4 |
14.37MB |
| 04-output_gate.mp4 |
14.09MB |
| 04-pre_trained_model.mp4 |
29.04MB |
| 04-system requirements and pre requisite for studying deep learning instructions html |
4.51KB |
| 05-ann_types_and_applications.mp4 |
17.78MB |
| 05-demonstration_building_a_simple_neural_network.mp4 |
40.88MB |
| 05-demonstration_object_detection_with_faster_rcnn_building_the_model.mp4 |
82.91MB |
| 05-demonstration_rnn_dataset_preparation.mp4 |
62.04MB |
| 05-fast_regional_cnn.mp4 |
32.10MB |
| 05-importance_of_lstm_architecture.mp4 |
23.04MB |
| 05-model_compilation_with_popular_frameworks.mp4 |
27.34MB |
| 05-working_of_convolutional_layer.mp4 |
31.99MB |
| 06-demonstration_creating_base_variables_and_loading_the_model.mp4 |
37.00MB |
| 06-demonstration_load_and_preprocess_the_data.mp4 |
42.04MB |
| 06-demonstration_model_compilation_preparing_the_dataset.mp4 |
55.53MB |
| 06-demonstration_rnn_building_the_model.mp4 |
62.38MB |
| 06-demonstration_understanding_how_backpropagation_has_worked.mp4 |
40.45MB |
| 06-faster_r_cnn_architecture_instructions.html |
5.92KB |
| 06-forward_propagation.mp4 |
20.61MB |
| 06-types_of_lstm.mp4 |
19.16MB |
| 07-demonstration_building_and_compiling_model.mp4 |
46.26MB |
| 07-demonstration_designing_the_model.mp4 |
52.84MB |
| 07-demonstration_handwritten_digits_classification_data_preprocessing.mp4 |
41.79MB |
| 07-demonstration_next_word_prediction_processing_the_corpus.mp4 |
50.16MB |
| 07-demonstration_training_the_model_and_visualizing_the_predictions.mp4 |
53.63MB |
| 07-perceptron.mp4 |
30.93MB |
| 07-recurrent_neural_networks_rnns_in_deep_learning_instructions.html |
19.64KB |
| 08-demonstration_building_the_cnn_model.mp4 |
37.97MB |
| 08-demonstration_from_rmsprop_to_adam.mp4 |
45.17MB |
| 08-demonstration_handwritten_digits_classification_designing_the_model.mp4 |
73.22MB |
| 08-demonstration_next_word_prediction_layers.mp4 |
58.93MB |
| 08-demonstration_svm_as_a_classifier.mp4 |
23.40MB |
| 08-learning_rate.mp4 |
29.25MB |
| 09-demonstration_handwritten_digits_classification_optimizing_the_model.mp4 |
88.77MB |
| 09-demonstration_model_accuracy.mp4 |
21.46MB |
| 09-demonstration_next_word_prediction_model_compilation_and_prediction.mp4 |
96.56MB |
| 09-model_optimizers_beyond_adam_instructions.html |
87.35KB |
| 09-svm_classifier_in_object_detection_instructions.html |
4.26KB |
| 09-what_is_activation_function.mp4 |
17.83MB |
| 10-activation_function_and_its_types.mp4 |
23.41MB |
| 10-attention_based_lstm_long_short_term_memory_instructions.html |
7.41KB |
| 10-demonstration_adding_more_layers.mp4 |
62.39MB |
| 10-hebbian_learning_algorithm_instructions.html |
27.28KB |
| 11-capsule_networks_in_deep_learning_instructions.html |
4.17KB |
| 11-demonstration_building_basic_cnn_model_with_new_parameters.mp4 |
78.21MB |
| 11-importance_of_epoch.mp4 |
24.78MB |
| 12-demonstration_pre_trained_model.mp4 |
37.38MB |
| 12-single_layer_perceptron_define_sigmoid_function.mp4 |
44.01MB |
| 13-single_layer_perceptron_decision_boundary.mp4 |
77.15MB |
| 13-why_convolutions_are_important_instructions.html |
2.08KB |
| 14-learning_rate_in_deep_learning_instructions.html |
3.86KB |
| history.p |
436B |
| next_word_model.keras |
9.76MB |
| resources.html |
65.68KB |
| Support - Onehack.Us.txt |
94B |