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Total Size:
2.7 GB
Info Hash:
C7BB387E940A369D54E1C25A892B00661CB93B3B
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Added:
April 21, 2026, 10:34 a.m.
Stats:
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(Last updated: April 21, 2026, 10:38 a.m.)
| File | Size |
|---|---|
| 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 |
| 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 |
| 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 |
| 01-summary_of_deep_learning_components.mp4 | 36.3 MB |
| 01-limitations_of_mlp.mp4 | 27.9 MB |
| 01. Support - Onehack.Us.txt | 94 bytes |
| 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 |
| 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 |
| 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 |
| 01-summary_of_cnn_in_deep_learning.mp4 | 13.3 MB |
| 02-summary_of_faster_rcnn.mp4 | 22.5 MB |
| 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 |
| 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 |
| 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 |
| 01-summary_of_deep_learning_with_rnn_and_lstm_with_model_optimization.mp4 | 32.9 MB |
| history.p | 436 bytes |
| next_word_model.keras | 9.8 MB |
| resources.html | 65.7 KB |
| 01-course_summary_for_practical_deep_learning_with_python.mp4 | 23.4 MB |
| 02-practice_project_mnist_fashion_dataset_analysis_instructions.html | 64.0 KB |
| Support - Onehack.Us.txt | 94 bytes |
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SOURCE: Coursera | Practical Deep Learning With Python 2025
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Visit >>> http://onehack.us/ https://i.ibb.co/fYngh2NR/Practical-Deep.png Coursera - Practical Deep Learning With Python 2025 Course details 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 ... What you'll learn - Understand the core components of deep learning models and their role in AI. - Apply CNN, R-CNN, and Faster R-CNN for object detection tasks. - Implement RNNs and LSTMs for sequential data processing. - Optimize and evaluate deep learning models for improved performance. There are 4 modules in this course 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. Experience in building machine learning models, along with knowledge of statistics and proficiency in Python programming, is recommended for this course. Embark on this educational journey to enhance your expertise in deep learning and elevate your capabilities in building intelligent systems for the future of artificial intelligence. General Details: Duration: 6h 10m Updated: 03/2025 Language: English Source: https://www.coursera.org/learn/practical-deep-learning-with-python Instructor: https://www.edureka.co/ MP4 | Video: AVC, 1920x1080p | Audio: AAC, 44.100 KHz, 2 Ch
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