Edureka Practical Deep Learning With Python

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Files
  1. 01-welcome_to_practical_deep_learning_with_python_instructions.html7.2 KB
  2. 02-course_introduction.mp428.0 MB
  3. 03-environment_configuration.mp421.8 MB
  4. 04-system_requirements_and_pre_requisite_for_studying_deep_learning_instructions.html4.5 KB
  5. 01-machine_learning_vs_deep_learning.mp434.3 MB
  6. 02-what_is_deep_learning.mp420.3 MB
  7. 03-neural_networks.mp442.2 MB
  8. 04-artificial_neural_network_ann.mp424.4 MB
  9. 05-ann_types_and_applications.mp417.8 MB
  10. 06-forward_propagation.mp420.6 MB
  11. 07-perceptron.mp430.9 MB
  12. 08-learning_rate.mp429.3 MB
  13. 09-what_is_activation_function.mp417.8 MB
  14. 10-activation_function_and_its_types.mp423.4 MB
  15. 11-importance_of_epoch.mp424.8 MB
  16. 12-single_layer_perceptron_define_sigmoid_function.mp444.0 MB
  17. 13-single_layer_perceptron_decision_boundary.mp477.2 MB
  18. 14-learning_rate_in_deep_learning_instructions.html3.9 KB
  19. 01-limitations_of_single_layered_perceptron.mp411.1 MB
  20. 02-multi_layered_perceptron.mp412.0 MB
  21. 03-what_is_backpropagation.mp410.3 MB
  22. 04-backpropagation.mp417.0 MB
  23. 05-demonstration_building_a_simple_neural_network.mp440.9 MB
  24. 06-demonstration_understanding_how_backpropagation_has_worked.mp440.5 MB
  25. 07-demonstration_handwritten_digits_classification_data_preprocessing.mp441.8 MB
  26. 08-demonstration_handwritten_digits_classification_designing_the_model.mp473.2 MB
  27. 09-demonstration_handwritten_digits_classification_optimizing_the_model.mp488.8 MB
  28. 10-hebbian_learning_algorithm_instructions.html27.3 KB
  29. 01-summary_of_deep_learning_components.mp436.3 MB
  30. 01-limitations_of_mlp.mp427.9 MB
  31. 02-mlp_limitations_resolving_the_issue_with_cnn.mp421.5 MB
  32. 03-visual_cortex_and_cnn.mp431.6 MB
  33. 04-convolutional_layer.mp432.0 MB
  34. 05-working_of_convolutional_layer.mp432.0 MB
  35. 06-demonstration_load_and_preprocess_the_data.mp442.0 MB
  36. 07-demonstration_designing_the_model.mp452.8 MB
  37. 08-demonstration_building_the_cnn_model.mp438.0 MB
  38. 09-demonstration_model_accuracy.mp421.5 MB
  39. 10-demonstration_adding_more_layers.mp462.4 MB
  40. 11-demonstration_building_basic_cnn_model_with_new_parameters.mp478.2 MB
  41. 12-demonstration_pre_trained_model.mp437.4 MB
  42. 13-why_convolutions_are_important_instructions.html2.1 KB
  43. 01-classification_and_object_detection.mp429.8 MB
  44. 02-introduction_to_rcnn.mp431.5 MB
  45. 03-r_cnn_bounding_box_regression.mp412.5 MB
  46. 04-pre_trained_model.mp429.0 MB
  47. 05-fast_regional_cnn.mp432.1 MB
  48. 06-demonstration_creating_base_variables_and_loading_the_model.mp437.0 MB
  49. 07-demonstration_training_the_model_and_visualizing_the_predictions.mp453.6 MB
  50. 08-demonstration_svm_as_a_classifier.mp423.4 MB
  51. 09-svm_classifier_in_object_detection_instructions.html4.3 KB
  52. 01-fast_rcnn_limitations.mp424.9 MB
  53. 02-advent_of_faster_r_cnn.mp425.2 MB
  54. 03-tensorflow_hub.mp420.3 MB
  55. 04-demonstration_object_detection_with_faster_rcnn_pretrained_model_setup.mp474.7 MB
  56. 05-demonstration_object_detection_with_faster_rcnn_building_the_model.mp482.9 MB
  57. 06-faster_r_cnn_architecture_instructions.html5.9 KB
  58. 01-summary_of_cnn_in_deep_learning.mp413.3 MB
  59. 02-summary_of_faster_rcnn.mp422.5 MB
  60. 01-rnn_fundamentals.mp420.5 MB
  61. 02-rnn_architecture.mp422.6 MB
  62. 03-rnn_architecture_workflow.mp428.9 MB
  63. 04-implementing_rnn.mp428.9 MB
  64. 05-demonstration_rnn_dataset_preparation.mp462.0 MB
  65. 06-demonstration_rnn_building_the_model.mp462.4 MB
  66. 07-recurrent_neural_networks_rnns_in_deep_learning_instructions.html19.6 KB
  67. 01-basics_of_lstm.mp428.4 MB
  68. 02-lstm_structure.mp424.2 MB
  69. 03-forget_gate_and_input_gate.mp420.9 MB
  70. 04-output_gate.mp414.1 MB
  71. 05-importance_of_lstm_architecture.mp423.0 MB
  72. 06-types_of_lstm.mp419.2 MB
  73. 07-demonstration_next_word_prediction_processing_the_corpus.mp450.2 MB
  74. 08-demonstration_next_word_prediction_layers.mp458.9 MB
  75. 09-demonstration_next_word_prediction_model_compilation_and_prediction.mp496.6 MB
  76. 10-attention_based_lstm_long_short_term_memory_instructions.html7.4 KB
  77. 11-capsule_networks_in_deep_learning_instructions.html4.2 KB
  78. 01-improving_a_model.mp432.9 MB
  79. 02-model_optimization.mp421.8 MB
  80. 03-using_adam_optimizer.mp432.0 MB
  81. 04-model_compilation.mp414.4 MB
  82. 05-model_compilation_with_popular_frameworks.mp427.3 MB
  83. 06-demonstration_model_compilation_preparing_the_dataset.mp455.5 MB
  84. 07-demonstration_building_and_compiling_model.mp446.3 MB
  85. 08-demonstration_from_rmsprop_to_adam.mp445.2 MB
  86. 09-model_optimizers_beyond_adam_instructions.html87.4 KB
  87. 01-summary_of_deep_learning_with_rnn_and_lstm_with_model_optimization.mp432.9 MB
  88. 01-course_summary_for_practical_deep_learning_with_python.mp423.4 MB
  89. 02-practice_project_mnist_fashion_dataset_analysis_instructions.html64.0 KB
  90. deeplearning.txt48.5 KB
  91. history.p436 bytes
  92. next_word_model.keras9.8 MB
  93. resources.html65.7 KB

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