Coursera - Introduction to Machine Learning 2024-3

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Coursera - Introduction to Machine Learning 2024-3

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收录时间：2024-06-23
最近下载：2025-10-18

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文件列表

  • 04_recurrent-neural-networks-for-natural-language-processing/04_alternative-approaches/01_simple-and-effective-alternative-methods-for-neural-nlp.mp4 47.6 MB
  • 04_recurrent-neural-networks-for-natural-language-processing/01_word-embeddings/03_example-of-word-embeddings.mp4 47.5 MB
  • 06_introduction-to-reinforcement-learning/01_reinforcement-learning/03_example-of-reinforcement-learning-in-practice.mp4 46.2 MB
  • 02_basics-of-model-learning/01_logistic-regression-as-running-example/01_how-do-we-define-learning.mp4 43.0 MB
  • 04_recurrent-neural-networks-for-natural-language-processing/03_recurrent-neural-networks-and-long-short-term-memory/02_long-short-term-memory.mp4 40.2 MB
  • 04_recurrent-neural-networks-for-natural-language-processing/02_representative-example-nlp-problem-sentiment-analysis/01_neural-model-of-text.mp4 39.1 MB
  • 06_introduction-to-reinforcement-learning/02_q-learning/03_extensions-of-q-learning.mp4 35.1 MB
  • 05_the-transformer-network-for-natural-language-processing/02_attention-mechanism/03_self-attention-and-positional-encodings.mp4 34.9 MB
  • 06_introduction-to-reinforcement-learning/01_reinforcement-learning/02_reinforcement-learning-problem-setup.mp4 32.5 MB
  • 06_introduction-to-reinforcement-learning/03_deep-q-learning/02_deep-q-learning-based-on-images.mp4 30.7 MB
  • 02_basics-of-model-learning/02_learning-via-gradient-descent/02_how-do-we-handle-big-data.mp4 29.7 MB
  • 05_the-transformer-network-for-natural-language-processing/04_the-transformer-network/02_the-complete-transformer-network.mp4 29.3 MB
  • 01_simple-introduction-to-machine-learning/04_applications-in-the-real-world/02_applications-in-use-and-practice.mp4 28.8 MB
  • 01_simple-introduction-to-machine-learning/02_multilayer-perceptron/11_early-history-of-neural-networks.mp4 28.7 MB
  • 01_simple-introduction-to-machine-learning/04_applications-in-the-real-world/01_cnn-on-real-images.mp4 26.8 MB
  • 02_basics-of-model-learning/01_logistic-regression-as-running-example/02_how-do-we-evaluate-our-networks.mp4 26.8 MB
  • 05_the-transformer-network-for-natural-language-processing/03_sequence-to-sequence-encoder-and-decoder/02_coupling-the-sequence-encoder-and-decoder.mp4 26.0 MB
  • 04_recurrent-neural-networks-for-natural-language-processing/03_recurrent-neural-networks-and-long-short-term-memory/03_long-short-term-memory-review.mp4 25.8 MB
  • 01_simple-introduction-to-machine-learning/04_applications-in-the-real-world/03_deep-learning-and-transfer-learning.mp4 24.4 MB
  • 06_introduction-to-reinforcement-learning/03_deep-q-learning/03_connecting-deep-q-learning-with-conventional-q-learning.mp4 23.2 MB
  • 01_simple-introduction-to-machine-learning/02_multilayer-perceptron/09_model-selection.mp4 23.2 MB
  • 06_introduction-to-reinforcement-learning/02_q-learning/01_moving-to-a-non-myopic-policy.mp4 22.9 MB
  • 01_simple-introduction-to-machine-learning/01_logistic-regression/05_logistic-regression.mp4 22.8 MB
  • 06_introduction-to-reinforcement-learning/03_deep-q-learning/01_limitations-of-q-learning-and-introduction-to-deep-q-learning.mp4 22.3 MB
  • 06_introduction-to-reinforcement-learning/02_q-learning/02_q-learning.mp4 22.1 MB
  • 05_the-transformer-network-for-natural-language-processing/02_attention-mechanism/01_introduction-of-attention-mechanism.mp4 21.9 MB
  • 04_recurrent-neural-networks-for-natural-language-processing/02_representative-example-nlp-problem-sentiment-analysis/04_more-details-on-how-to-learn-model-parameters.mp4 21.8 MB
  • 05_the-transformer-network-for-natural-language-processing/03_sequence-to-sequence-encoder-and-decoder/01_attention-based-sequence-encoder.mp4 21.7 MB
  • 04_recurrent-neural-networks-for-natural-language-processing/03_recurrent-neural-networks-and-long-short-term-memory/04_use-of-lstm-for-text-synthesis.mp4 20.9 MB
  • 05_the-transformer-network-for-natural-language-processing/04_the-transformer-network/01_multi-head-attention.mp4 20.6 MB
  • 04_recurrent-neural-networks-for-natural-language-processing/01_word-embeddings/02_words-to-vectors.mp4 20.3 MB
  • 04_recurrent-neural-networks-for-natural-language-processing/03_recurrent-neural-networks-and-long-short-term-memory/01_the-recurrent-neural-network.mp4 20.2 MB
  • 01_simple-introduction-to-machine-learning/01_logistic-regression/06_interpretation-of-logistic-regression.mp4 20.2 MB
  • 03_image-analysis-with-convolutional-neural-networks/01_convolutional-neural-network-basics/01_motivation-diabetic-retinopathy.mp4 19.6 MB
  • 01_simple-introduction-to-machine-learning/02_multilayer-perceptron/04_deep-learning.mp4 19.5 MB
  • 04_recurrent-neural-networks-for-natural-language-processing/02_representative-example-nlp-problem-sentiment-analysis/02_the-softmax-function.mp4 19.4 MB
  • 06_introduction-to-reinforcement-learning/01_reinforcement-learning/01_introduction-to-reinforcement-learning.mp4 19.4 MB
  • 01_simple-introduction-to-machine-learning/03_convolutional-neural-networks/06_how-the-model-learns.mp4 19.0 MB
  • 04_recurrent-neural-networks-for-natural-language-processing/02_representative-example-nlp-problem-sentiment-analysis/03_methods-for-learning-model-parameters.mp4 18.9 MB
  • 02_basics-of-model-learning/02_learning-via-gradient-descent/01_how-do-we-learn-our-network.mp4 18.9 MB
  • 01_simple-introduction-to-machine-learning/02_multilayer-perceptron/07_transfer-learning.mp4 18.5 MB
  • 04_recurrent-neural-networks-for-natural-language-processing/01_word-embeddings/01_introduction-to-the-concept-of-word-vectors.mp4 18.0 MB
  • 05_the-transformer-network-for-natural-language-processing/01_inner-products/04_intuition-into-meaning-of-inner-products-of-word-vectors.mp4 17.4 MB
  • 01_simple-introduction-to-machine-learning/05_pytorch-basics/01_introduction-to-pytorch.mp4 17.1 MB
  • 01_simple-introduction-to-machine-learning/02_multilayer-perceptron/06_interpretation-of-multilayer-perceptron.mp4 17.0 MB
  • 01_simple-introduction-to-machine-learning/03_convolutional-neural-networks/01_hierarchical-structure-of-images.mp4 15.7 MB
  • 05_the-transformer-network-for-natural-language-processing/02_attention-mechanism/02_queries-keys-and-values-of-attention-network.mp4 15.4 MB
  • 05_the-transformer-network-for-natural-language-processing/01_inner-products/01_word-vectors-and-their-interpretation.mp4 15.0 MB
  • 01_simple-introduction-to-machine-learning/03_convolutional-neural-networks/02_convolution-filters.mp4 14.8 MB
  • 05_the-transformer-network-for-natural-language-processing/01_inner-products/03_inner-products-between-word-vectors.mp4 14.3 MB
  • 01_simple-introduction-to-machine-learning/01_logistic-regression/02_why-machine-learning-is-exciting.mp4 14.3 MB
  • 03_image-analysis-with-convolutional-neural-networks/01_convolutional-neural-network-basics/02_breakdown-of-the-convolution-1d-and-2d.mp4 13.4 MB
  • 01_simple-introduction-to-machine-learning/01_logistic-regression/03_what-is-machine-learning.mp4 13.2 MB
  • 03_image-analysis-with-convolutional-neural-networks/02_core-components-of-the-network/01_core-components-of-the-convolutional-layer.mp4 12.9 MB
  • 01_simple-introduction-to-machine-learning/03_convolutional-neural-networks/07_advantages-of-hierarchical-features.mp4 12.9 MB
  • 01_simple-introduction-to-machine-learning/02_multilayer-perceptron/02_multilayer-perceptron-math-model.mp4 12.8 MB
  • 01_simple-introduction-to-machine-learning/02_multilayer-perceptron/01_multilayer-perceptron-concepts.mp4 12.6 MB
  • 05_the-transformer-network-for-natural-language-processing/01_inner-products/02_relationships-between-word-vectors.mp4 10.5 MB
  • 01_simple-introduction-to-machine-learning/02_multilayer-perceptron/05_example-document-analysis.mp4 10.0 MB
  • 01_simple-introduction-to-machine-learning/01_logistic-regression/08_motivation-for-multilayer-perceptron.mp4 9.9 MB
  • 03_image-analysis-with-convolutional-neural-networks/03_cnn-implementation/01_training-the-network.mp4 9.7 MB
  • 01_simple-introduction-to-machine-learning/03_convolutional-neural-networks/05_cnn-math-model.mp4 9.3 MB
  • 01_simple-introduction-to-machine-learning/03_convolutional-neural-networks/03_convolutional-neural-network.mp4 8.9 MB
  • 02_basics-of-model-learning/02_learning-via-gradient-descent/03_early-stopping.mp4 8.6 MB
  • 01_simple-introduction-to-machine-learning/04_applications-in-the-real-world/03_deep-learning-and-transfer-learning_1.4.3_Deep_Learning_and_Transfer_Learning.pdf 7.8 MB
  • 05_the-transformer-network-for-natural-language-processing/03_sequence-to-sequence-encoder-and-decoder/03_cross-attention-in-the-sequence-to-sequence-model.mp4 7.5 MB
  • 03_image-analysis-with-convolutional-neural-networks/03_cnn-implementation/02_transfer-learning-and-fine-tuning.mp4 7.4 MB
  • 03_image-analysis-with-convolutional-neural-networks/02_core-components-of-the-network/03_pooling-and-fully-connected-layers.mp4 7.0 MB
  • 03_image-analysis-with-convolutional-neural-networks/02_core-components-of-the-network/02_activation-functions.mp4 6.7 MB
  • 02_basics-of-model-learning/03_model-learning-with-pytorch/02_model-learning-with-pytorch.mp4 6.0 MB
  • 06_introduction-to-reinforcement-learning/01_reinforcement-learning/05_reinforcement-learning-with-pytorch.mp4 5.4 MB
  • 03_image-analysis-with-convolutional-neural-networks/03_cnn-implementation/02_transfer-learning-and-fine-tuning_3.3.20_Transfer_Learning_and_Fine-Tuning.pdf 5.3 MB
  • 04_recurrent-neural-networks-for-natural-language-processing/05_natural-language-processing-with-pytorch/01_natural-language-processing-with-pytorch.mp4 4.4 MB
  • 01_simple-introduction-to-machine-learning/01_logistic-regression/03_what-is-machine-learning_1.1.15_What_is_Machine_Learning.pdf 4.3 MB
  • 01_simple-introduction-to-machine-learning/04_applications-in-the-real-world/02_applications-in-use-and-practice_1.4.2_Applicatrions_in_use_and_practice.pdf 4.1 MB
  • 03_image-analysis-with-convolutional-neural-networks/04_convolutional-neural-networks-with-pytorch/02_cnn-with-pytorch.mp4 3.9 MB
  • 01_simple-introduction-to-machine-learning/04_applications-in-the-real-world/01_cnn-on-real-images_1.4.1_CNN_on_Real_Images.pdf 3.7 MB
  • 02_basics-of-model-learning/02_learning-via-gradient-descent/02_how-do-we-handle-big-data_2.2.20_How_do_we_handle_big_data.pdf 3.1 MB
  • 01_simple-introduction-to-machine-learning/02_multilayer-perceptron/09_model-selection_1.2.7__Model_Selection.pdf 3.0 MB
  • 03_image-analysis-with-convolutional-neural-networks/02_core-components-of-the-network/01_core-components-of-the-convolutional-layer_3.2.10_Core_Components_of_the_Convolutional_Layer.pdf 3.0 MB
  • 01_simple-introduction-to-machine-learning/02_multilayer-perceptron/04_deep-learning_1.2.3_Deep_Learning.pdf 2.8 MB
  • 01_simple-introduction-to-machine-learning/02_multilayer-perceptron/01_multilayer-perceptron-concepts_1.2.1_Multilayer_Perceptron_concepts.pdf 2.7 MB
  • 01_simple-introduction-to-machine-learning/01_logistic-regression/08_motivation-for-multilayer-perceptron_1.1.40_Motivation_for_Multilayer_Perceptron.pdf 2.7 MB
  • 01_simple-introduction-to-machine-learning/01_logistic-regression/05_logistic-regression_1.1.20_Logistic_Regression.pdf 2.7 MB
  • 04_recurrent-neural-networks-for-natural-language-processing/02_representative-example-nlp-problem-sentiment-analysis/01_neural-model-of-text_4.2.1_Neural_model_of_text.pdf 2.6 MB
  • 01_simple-introduction-to-machine-learning/03_convolutional-neural-networks/07_advantages-of-hierarchical-features_1.3.6_Advantages_of_Hierarchical_Features.pdf 2.6 MB
  • 03_image-analysis-with-convolutional-neural-networks/02_core-components-of-the-network/02_activation-functions_3.2.30_Activations_Functions.pdf 2.5 MB
  • 01_simple-introduction-to-machine-learning/02_multilayer-perceptron/11_early-history-of-neural-networks_1.2.8_Early_History_of_Neural_Netorks.pdf 2.5 MB
  • 02_basics-of-model-learning/02_learning-via-gradient-descent/03_early-stopping_2.2.30_Early_Stopping.pdf 2.5 MB
  • 01_simple-introduction-to-machine-learning/01_logistic-regression/02_why-machine-learning-is-exciting_1.1.10_Why_Machine_Learning_is_Exciting.pdf 2.4 MB
  • 01_simple-introduction-to-machine-learning/03_convolutional-neural-networks/02_convolution-filters_1.3.2_Convolution_filter.pdf 2.1 MB
  • 01_simple-introduction-to-machine-learning/01_logistic-regression/06_interpretation-of-logistic-regression_1.1.30_Interpretatiof_Logistic_Regression.pdf 2.0 MB
  • 02_basics-of-model-learning/01_logistic-regression-as-running-example/02_how-do-we-evaluate-our-networks_2.1.20_How_do_we_evaluate_our_networkds.pdf 1.8 MB
  • 03_image-analysis-with-convolutional-neural-networks/01_convolutional-neural-network-basics/01_motivation-diabetic-retinopathy_3.1.10_Motivation_Diabetic_Retinopathy.pdf 1.7 MB
  • 03_image-analysis-with-convolutional-neural-networks/01_convolutional-neural-network-basics/02_breakdown-of-the-convolution-1d-and-2d_3.1.20_Breakdown_of_the_Convolution_1D_and_2D.pdf 1.6 MB
  • 04_recurrent-neural-networks-for-natural-language-processing/01_word-embeddings/02_words-to-vectors_4.1.2_Words_to_Vectors.pdf 1.6 MB
  • 04_recurrent-neural-networks-for-natural-language-processing/01_word-embeddings/01_introduction-to-the-concept-of-word-vectors_4.1.1_Introduction_to_the_Concept_of_Word_Vectors.pdf 1.5 MB
  • 01_simple-introduction-to-machine-learning/03_convolutional-neural-networks/01_hierarchical-structure-of-images_1.3.1_Hierarchical_Structure_of_Images.pdf 1.4 MB
  • 01_simple-introduction-to-machine-learning/03_convolutional-neural-networks/05_cnn-math-model_1.3.4_Convolutional_Neural_Network__Math_Model.pdf 1.2 MB
  • 01_simple-introduction-to-machine-learning/02_multilayer-perceptron/02_multilayer-perceptron-math-model_1.2.2_Multilayer_Perceptron_Math_Model.pdf 1.1 MB
  • 01_simple-introduction-to-machine-learning/02_multilayer-perceptron/06_interpretation-of-multilayer-perceptron_1.2.5_Interpretation_of_Multilayer_Perceptron.pdf 1.1 MB
  • 01_simple-introduction-to-machine-learning/02_multilayer-perceptron/07_transfer-learning_1.2.6_Transfer_Learning.pdf 1.1 MB
  • 01_simple-introduction-to-machine-learning/03_convolutional-neural-networks/03_convolutional-neural-network_1.3.3_Convolutional_Neural_Network.pdf 1.1 MB
  • 03_image-analysis-with-convolutional-neural-networks/02_core-components-of-the-network/03_pooling-and-fully-connected-layers_3.2.40_Pooling_and_fully_contected_layers.pdf 1.1 MB
  • 01_simple-introduction-to-machine-learning/03_convolutional-neural-networks/06_how-the-model-learns_1.3.5_How_the_Model_Learns.pdf 1.1 MB
  • 03_image-analysis-with-convolutional-neural-networks/03_cnn-implementation/01_training-the-network_3.3.10_Training_the_Network.pdf 1.0 MB
  • 01_simple-introduction-to-machine-learning/02_multilayer-perceptron/05_example-document-analysis_1.2.4_Example_Document_Analysis.pdf 1.0 MB
  • 01_simple-introduction-to-machine-learning/02_multilayer-perceptron/10_model-selection_quiz.html 208.8 kB
  • 01_simple-introduction-to-machine-learning/04_applications-in-the-real-world/05_week-1-comprehensive_exam.html 199.7 kB
  • 06_introduction-to-reinforcement-learning/02_q-learning/04_q-learning-quiz_quiz.html 190.4 kB
  • 02_basics-of-model-learning/01_logistic-regression-as-running-example/01_how-do-we-define-learning_2.1.10_How_do_we_define_learning.pdf 110.4 kB
  • 01_simple-introduction-to-machine-learning/01_logistic-regression/09_logistic-regression_quiz.html 107.1 kB
  • 06_introduction-to-reinforcement-learning/01_reinforcement-learning/04_reinforcement-learning-quiz_quiz.html 93.5 kB
  • 02_basics-of-model-learning/02_learning-via-gradient-descent/01_how-do-we-learn-our-network_2.2.10_How_do_we_learn_our_network.pdf 87.0 kB
  • 06_introduction-to-reinforcement-learning/01_reinforcement-learning/03_example-of-reinforcement-learning-in-practice.en.srt 28.1 kB
  • 04_recurrent-neural-networks-for-natural-language-processing/03_recurrent-neural-networks-and-long-short-term-memory/02_long-short-term-memory.en.srt 25.5 kB
  • 05_the-transformer-network-for-natural-language-processing/02_attention-mechanism/03_self-attention-and-positional-encodings.en.srt 24.8 kB
  • 02_basics-of-model-learning/01_logistic-regression-as-running-example/02_how-do-we-evaluate-our-networks.en.srt 21.1 kB
  • 06_introduction-to-reinforcement-learning/03_deep-q-learning/01_limitations-of-q-learning-and-introduction-to-deep-q-learning.en.srt 20.3 kB
  • 05_the-transformer-network-for-natural-language-processing/03_sequence-to-sequence-encoder-and-decoder/02_coupling-the-sequence-encoder-and-decoder.en.srt 19.7 kB
  • 01_simple-introduction-to-machine-learning/02_multilayer-perceptron/11_early-history-of-neural-networks.en.srt 18.8 kB
  • 04_recurrent-neural-networks-for-natural-language-processing/04_alternative-approaches/01_simple-and-effective-alternative-methods-for-neural-nlp.en.srt 18.6 kB
  • 02_basics-of-model-learning/02_learning-via-gradient-descent/02_how-do-we-handle-big-data.en.srt 16.8 kB
  • 04_recurrent-neural-networks-for-natural-language-processing/02_representative-example-nlp-problem-sentiment-analysis/01_neural-model-of-text.en.srt 16.4 kB
  • 05_the-transformer-network-for-natural-language-processing/01_inner-products/04_intuition-into-meaning-of-inner-products-of-word-vectors.en.srt 16.2 kB
  • 05_the-transformer-network-for-natural-language-processing/04_the-transformer-network/02_the-complete-transformer-network.en.srt 16.1 kB
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  • 06_introduction-to-reinforcement-learning/03_deep-q-learning/03_connecting-deep-q-learning-with-conventional-q-learning.en.srt 15.2 kB
  • 05_the-transformer-network-for-natural-language-processing/03_sequence-to-sequence-encoder-and-decoder/01_attention-based-sequence-encoder.en.srt 15.0 kB
  • 02_basics-of-model-learning/01_logistic-regression-as-running-example/01_how-do-we-define-learning.en.srt 14.8 kB
  • 04_recurrent-neural-networks-for-natural-language-processing/03_recurrent-neural-networks-and-long-short-term-memory/01_the-recurrent-neural-network.en.srt 14.7 kB
  • 06_introduction-to-reinforcement-learning/02_q-learning/01_moving-to-a-non-myopic-policy.en.srt 14.7 kB
  • 06_introduction-to-reinforcement-learning/02_q-learning/03_extensions-of-q-learning.en.srt 14.7 kB
  • 05_the-transformer-network-for-natural-language-processing/02_attention-mechanism/01_introduction-of-attention-mechanism.en.srt 14.6 kB
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  • 03_image-analysis-with-convolutional-neural-networks/01_convolutional-neural-network-basics/01_motivation-diabetic-retinopathy.en.srt 14.4 kB
  • 03_image-analysis-with-convolutional-neural-networks/01_convolutional-neural-network-basics/02_breakdown-of-the-convolution-1d-and-2d.en.srt 13.8 kB
  • 06_introduction-to-reinforcement-learning/01_reinforcement-learning/01_introduction-to-reinforcement-learning.en.srt 12.9 kB
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  • 04_recurrent-neural-networks-for-natural-language-processing/02_representative-example-nlp-problem-sentiment-analysis/03_methods-for-learning-model-parameters.en.srt 12.7 kB
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