Coursera Introduction to Machine Learning 2024 |
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Total Size |
1.5 GB |
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Total Files |
280 |
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Last Seen |
2025-08-31 00:14 |
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Hash |
B46C7AAEB5FA99253AB6E5B6F0CD9EC5613CD2B1 |
/.../04_alternative-approaches/ |
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01_simple-and-effective-alternative-methods-for-neural-nlp.mp4 |
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01_simple-and-effective-alternative-methods-for-neural-nlp.en.srt |
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01_simple-and-effective-alternative-methods-for-neural-nlp.en.txt |
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/.../01_logistic-regression/ |
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02_why-machine-learning-is-exciting_1.1.10_Why_Machine_Learning_is_Exciting.pdf |
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03_what-is-machine-learning_1.1.15_What_is_Machine_Learning.pdf |
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06_interpretation-of-logistic-regression_1.1.30_Interpretatiof_Logistic_Regression.pdf |
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08_motivation-for-multilayer-perceptron_1.1.40_Motivation_for_Multilayer_Perceptron.pdf |
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01_multilayer-perceptron-concepts_1.2.1_Multilayer_Perceptron_concepts.pdf |
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02_multilayer-perceptron-math-model_1.2.2_Multilayer_Perceptron_Math_Model.pdf |
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05_example-document-analysis_1.2.4_Example_Document_Analysis.pdf |
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06_interpretation-of-multilayer-perceptron_1.2.5_Interpretation_of_Multilayer_Perceptron.pdf |
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11_early-history-of-neural-networks_1.2.8_Early_History_of_Neural_Netorks.pdf |
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/.../03_convolutional-neural-networks/ |
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01_hierarchical-structure-of-images_1.3.1_Hierarchical_Structure_of_Images.pdf |
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03_convolutional-neural-network_1.3.3_Convolutional_Neural_Network.pdf |
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05_cnn-math-model_1.3.4_Convolutional_Neural_Network__Math_Model.pdf |
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07_advantages-of-hierarchical-features_1.3.6_Advantages_of_Hierarchical_Features.pdf |
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/.../04_applications-in-the-real-world/ |
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02_applications-in-use-and-practice_1.4.2_Applicatrions_in_use_and_practice.pdf |
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03_deep-learning-and-transfer-learning_1.4.3_Deep_Learning_and_Transfer_Learning.pdf |
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/.../05_pytorch-basics/ |
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/.../01_logistic-regression-as-running-example/ |
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01_how-do-we-define-learning_2.1.10_How_do_we_define_learning.pdf |
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02_how-do-we-evaluate-our-networks_2.1.20_How_do_we_evaluate_our_networkds.pdf |
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/.../02_learning-via-gradient-descent/ |
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01_how-do-we-learn-our-network_2.2.10_How_do_we_learn_our_network.pdf |
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02_how-do-we-handle-big-data_2.2.20_How_do_we_handle_big_data.pdf |
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/.../03_model-learning-with-pytorch/ |
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/.../01_convolutional-neural-network-basics/ |
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01_motivation-diabetic-retinopathy_3.1.10_Motivation_Diabetic_Retinopathy.pdf |
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02_breakdown-of-the-convolution-1d-and-2d_3.1.20_Breakdown_of_the_Convolution_1D_and_2D.pdf |
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/.../02_core-components-of-the-network/ |
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01_core-components-of-the-convolutional-layer_3.2.10_Core_Components_of_the_Convolutional_Layer.pdf |
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03_pooling-and-fully-connected-layers_3.2.40_Pooling_and_fully_contected_layers.pdf |
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/.../03_cnn-implementation/ |
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02_transfer-learning-and-fine-tuning_3.3.20_Transfer_Learning_and_Fine-Tuning.pdf |
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/.../04_convolutional-neural-networks-with-pytorch/ |
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/.../01_word-embeddings/ |
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01_introduction-to-the-concept-of-word-vectors_4.1.1_Introduction_to_the_Concept_of_Word_Vectors.pdf |
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/.../02_representative-example-nlp-problem-sentiment-analysis/ |
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/.../03_recurrent-neural-networks-and-long-short-term-memory/ |
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/.../05_natural-language-processing-with-pytorch/ |
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/.../01_inner-products/ |
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04_intuition-into-meaning-of-inner-products-of-word-vectors.en.srt |
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04_intuition-into-meaning-of-inner-products-of-word-vectors.en.txt |
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04_intuition-into-meaning-of-inner-products-of-word-vectors.mp4 |
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/.../02_attention-mechanism/ |
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/.../03_sequence-to-sequence-encoder-and-decoder/ |
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/.../04_the-transformer-network/ |
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/.../01_reinforcement-learning/ |
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/02_q-learning/ |
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/.../03_deep-q-learning/ |
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01_limitations-of-q-learning-and-introduction-to-deep-q-learning.en.srt |
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01_limitations-of-q-learning-and-introduction-to-deep-q-learning.en.txt |
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01_limitations-of-q-learning-and-introduction-to-deep-q-learning.mp4 |
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03_connecting-deep-q-learning-with-conventional-q-learning.en.srt |
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03_connecting-deep-q-learning-with-conventional-q-learning.en.txt |
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03_connecting-deep-q-learning-with-conventional-q-learning.mp4 |
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Total files 280 |
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