Edureka - Practical Deep Learning With Python

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Edureka - Practical Deep Learning With Python 01-Deep_Learning_Components 01-Environment_Set_Up_And_Configuration
  • 01-welcome_to_practical_deep_learning_with_python_instructions.html (7.2 KB)
  • 02-course_introduction.mp4 (28.0 MB)
  • 03-environment_configuration.mp4 (21.8 MB)
  • 04-system_requirements_and_pre_requisite_for_studying_deep_learning_instructions.html (4.5 KB)
02-Essentials_Of_Deep_Learning
  • 01-machine_learning_vs_deep_learning.mp4 (34.3 MB)
  • 02-what_is_deep_learning.mp4 (20.3 MB)
  • 03-neural_networks.mp4 (42.2 MB)
  • 04-artificial_neural_network_ann.mp4 (24.4 MB)
  • 05-ann_types_and_applications.mp4 (17.8 MB)
  • 06-forward_propagation.mp4 (20.6 MB)
  • 07-perceptron.mp4 (30.9 MB)
  • 08-learning_rate.mp4 (29.3 MB)
  • 09-what_is_activation_function.mp4 (17.8 MB)
  • 10-activation_function_and_its_types.mp4 (23.4 MB)
  • 11-importance_of_epoch.mp4 (24.8 MB)
  • 12-single_layer_perceptron_define_sigmoid_function.mp4 (44.0 MB)
  • 13-single_layer_perceptron_decision_boundary.mp4 (77.2 MB)
  • 14-learning_rate_in_deep_learning_instructions.html (3.9 KB)
03-Building_Perceptron_And_Its_Working
  • 01-limitations_of_single_layered_perceptron.mp4 (11.1 MB)
  • 02-multi_layered_perceptron.mp4 (12.0 MB)
  • 03-what_is_backpropagation.mp4 (10.3 MB)
  • 04-backpropagation.mp4 (17.0 MB)
  • 05-demonstration_building_a_simple_neural_network.mp4 (40.9 MB)
  • 06-demonstration_understanding_how_backpropagation_has_worked.mp4 (40.5 MB)
  • 07-demonstration_handwritten_digits_classification_data_preprocessing.mp4 (41.8 MB)
  • 08-demonstration_handwritten_digits_classification_designing_the_model.mp4 (73.2 MB)
  • 09-demonstration_handwritten_digits_classification_optimizing_the_model.mp4 (88.8 MB)
  • 10-hebbian_learning_algorithm_instructions.html (27.3 KB)
04-Module_Wrap_Up_And_Assessment
  • 01-summary_of_deep_learning_components.mp4 (36.3 MB)
02-Deep_Learning_With_Cnn_Rcnn_And_Faster_Rcnn 01-Convolutional_Neural_Network
  • 01-limitations_of_mlp.mp4 (27.9 MB)
  • 02-mlp_limitations_resolving_the_issue_with_cnn.mp4 (21.5 MB)
  • 03-visual_cortex_and_cnn.mp4 (31.6 MB)
  • 04-convolutional_layer.mp4 (32.0 MB)
  • 05-working_of_convolutional_layer.mp4 (32.0 MB)
  • 06-demonstration_load_and_preprocess_the_data.mp4 (42.0 MB)
  • 07-demonstration_designing_the_model.mp4 (52.8 MB)
  • 08-demonstration_building_the_cnn_model.mp4 (38.0 MB)
  • 09-demonstration_model_accuracy.mp4 (21.5 MB)
  • 10-demonstration_adding_more_layers.mp4 (62.4 MB)
  • 11-demonstration_building_basic_cnn_model_with_new_parameters.mp4 (78.2 MB)
  • 12-demonstration_pre_trained_model.mp4 (37.4 MB)
  • 13-why_convolutions_are_important_instructions.html (2.1 KB)
02-Tensorflow_Hub_For_Object_Detection_Using_Faster_Rcnn
  • 01-classification_and_object_detection.mp4 (29.8 MB)
  • 02-introduction_to_rcnn.mp4 (31.5 MB)
  • 03-r_cnn_bounding_box_regression.mp4 (12.5 MB)
  • 04-pre_trained_model.mp4 (29.0 MB)
  • 05-fast_regional_cnn.mp4 (32.1 MB)
  • 06-demonstration_creating_base_variables_and_loading_the_model.mp4 (37.0 MB)
  • 07-demonstration_training_the_model_and_visualizing_the_predictions.mp4 (53.6 MB)
  • 08-demonstration_svm_as_a_classifier.mp4 (23.4 MB)
  • 09-svm_classifier_in_object_detection_instructions.html (4.3 KB)
03-Faster_Rcnn_Recurrent_Convolutional_Neural_Network
  • 01-fast_rcnn_limitations.mp4 (24.9 MB)
  • 02-advent_of_faster_r_cnn.mp4 (25.2 MB)
  • 03-tensorflow_hub.mp4 (20.3 MB)
  • 04-demonstration_object_detection_with_faster_rcnn_pretrained_model_setup.mp4 (74.7 MB)
  • 05-demonstration_object_detection_with_faster_rcnn_building_the_model.mp4 (82.9 MB)
  • 06-faster_r_cnn_architecture_instructions.html (5.9 KB)
04-Module_Wrap_Up_And_Assessment
  • 01-summary_of_cnn_in_deep_learning.mp4 (13.3 MB)
  • 02-summary_of_faster_rcnn.mp4 (22.5 MB)
03-Deep_Learning_With_Rnn_Lstm_And_Model_Optimization 01-Working_Of_Recurrent_Neural_Networks_Rnn
  • 01-rnn_fundamentals.mp4 (20.5 MB)
  • 02-rnn_architecture.mp4 (22.6 MB)
  • 03-rnn_architecture_workflow.mp4 (28.9 MB)
  • 04-implementing_rnn.mp4 (28.9 MB)
  • 05-demonstration_rnn_dataset_preparation.mp4 (62.0 MB)
  • 06-demonstration_rnn_building_the_model.mp4 (62.4 MB)
  • 07-recurrent_neural_networks_rnns_in_deep_learning_instructions.html (19.6 KB)
02-Lstm_Architecture
  • 01-basics_of_lstm.mp4 (28.4 MB)
  • 02-lstm_structure.mp4 (24.2 MB)
  • 03-forget_gate_and_input_gate.mp4 (20.9 MB)
  • 04-output_gate.mp4 (14.1 MB)
  • 05-importance_of_lstm_architecture.mp4 (23.0 MB)
  • 06-types_of_lstm.mp4 (19.2 MB)
  • 07-demonstration_next_word_prediction_processing_the_corpus.mp4 (50.2 MB)
  • 08-demonstration_next_word_prediction_layers.mp4 (58.9 MB)
  • 09-demonstration_next_word_prediction_model_compilation_and_prediction.mp4 (96.6 MB)
  • 10-attention_based_lstm_long_short_term_memory_instructions.html (7.4 KB)
  • 11-capsule_networks_in_deep_learning_instructions.html (4.2 KB)
03-Module_Optimization_And_Compilation
  • 01-improving_a_model.mp4 (32.9 MB)
  • 02-model_optimization.mp4 (21.8 MB)
  • 03-using_adam_optimizer.mp4 (32.0 MB)
  • 04-model_compilation.mp4 (14.4 MB)
  • 05-model_compilation_with_popular_frameworks.mp4 (27.3 MB)
  • 06-demonstration_model_compilation_preparing_the_dataset.mp4 (55.5 MB)
  • 07-demonstration_building_and_compiling_model.mp4 (46.3 MB)
  • 08-demonstration_from_rmsprop_to_adam.mp4 (45.2 MB)
  • 09-model_optimizers_beyond_adam_instructions.html (87.4 KB)
04-Module_Wrap_Up_And_Assessment
  • 01-summary_of_deep_learning_with_rnn_and_lstm_with_model_optimization.mp4 (32.9 MB)
04-Course_Wrap_Up_And_Assessment
  • 01-course_summary_for_practical_deep_learning_with_

Description

Welcome to the Practical Deep Learning with Python course, where you'll gain hands-on experience with cutting-edge deep learning techniques to model and analyze complex datasets. Unlock the power of deep learning to solve real-world problems and uncover actionable insights from massive data volumes. This course explores industry-specific applications and equips you with the practical skills needed to build and optimize advanced models.

By the end of this course, you’ll be able to:
- Describe the foundational components of deep learning models and their significance in artificial intelligence.
- Illustrate the working of CNNs, R-CNNs, and Faster R-CNNs for object detection and related applications.
- Understand the limitations of Perceptrons and how Multi-Layer Perceptrons (MLPs) address them.
- Implement Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) architectures for sequential data analysis.
- Optimize and evaluate deep learning models to achieve higher accuracy and efficiency.

This course is designed for data scientists, machine learning engineers, and AI enthusiasts with a foundational knowledge of Python and machine learning who aim to expand their expertise in deep learning.



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Edureka - Practical Deep Learning With Python

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Download torrent
2.7 GB
seeders:39
leechers:19
Edureka - Practical Deep Learning With Python

Torrent hash: D0FACF94C5D4862B6C9BDC166BDD16DFFCC1D420