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- 收录时间:2020-11-28 文档个数:1 文档大小:23.9 GB 最近下载:2022-04-03 人气:42 磁力链接
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- 收录时间:2020-11-27 文档个数:1 文档大小:23.8 GB 最近下载:2025-04-26 人气:1757 磁力链接
[Udemy] 100 Days of Code - The Complete Python Pro Bootcamp for 2020 23.8 GB
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- 收录时间:2020-11-14 文档个数:1 文档大小:23.8 GB 最近下载:2025-05-06 人气:7911 磁力链接
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- 收录时间:2022-02-14 文档个数:543 文档大小:23.6 GB 最近下载:2025-05-05 人气:6025 磁力链接
19 - Understand and design CNNs/005 Examine feature map activations.mp4 273.2 MB
22 - Style transfer/004 Transferring the screaming bathtub.mp4 227.4 MB
19 - Understand and design CNNs/012 The EMNIST dataset (letter recognition).mp4 211.1 MB
19 - Understand and design CNNs/002 CNN to classify MNIST digits.mp4 210.1 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/005 CodeChallenge sine wave extrapolation.mp4 205.2 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/009 Lorem ipsum.mp4 201.9 MB
07 - ANNs (Artificial Neural Networks)/013 Multi-output ANN (iris dataset).mp4 195.8 MB
19 - Understand and design CNNs/004 Classify Gaussian blurs.mp4 194.1 MB
09 - Regularization/004 Dropout regularization in practice.mp4 192.1 MB
16 - Autoencoders/006 Autoencoder with tied weights.mp4 186.4 MB
18 - Convolution and transformations/003 Convolution in code.mp4 181.5 MB
08 - Overfitting and cross-validation/006 Cross-validation -- DataLoader.mp4 180.7 MB
23 - Generative adversarial networks/002 Linear GAN with MNIST.mp4 178.2 MB
07 - ANNs (Artificial Neural Networks)/009 Learning rates comparison.mp4 176.8 MB
12 - More on data/003 CodeChallenge unbalanced data.mp4 174.3 MB
11 - FFNs (Feed-Forward Networks)/003 FFN to classify digits.mp4 169.7 MB
16 - Autoencoders/005 The latent code of MNIST.mp4 169.7 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/004 Predicting alternating sequences.mp4 167.9 MB
07 - ANNs (Artificial Neural Networks)/018 Model depth vs. breadth.mp4 166.6 MB
12 - More on data/007 Data feature augmentation.mp4 166.0 MB
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- 收录时间:2022-03-09 文档个数:533 文档大小:23.6 GB 最近下载:2025-05-05 人气:1954 磁力链接
19 - Understand and design CNNs/005 Examine feature map activations.mp4 273.2 MB
22 - Style transfer/004 Transferring the screaming bathtub.mp4 227.4 MB
19 - Understand and design CNNs/012 The EMNIST dataset (letter recognition).mp4 211.1 MB
19 - Understand and design CNNs/002 CNN to classify MNIST digits.mp4 210.1 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/005 CodeChallenge sine wave extrapolation.mp4 205.2 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/009 Lorem ipsum.mp4 201.9 MB
07 - ANNs (Artificial Neural Networks)/013 Multi-output ANN (iris dataset).mp4 195.8 MB
19 - Understand and design CNNs/004 Classify Gaussian blurs.mp4 194.1 MB
09 - Regularization/004 Dropout regularization in practice.mp4 192.1 MB
16 - Autoencoders/006 Autoencoder with tied weights.mp4 186.4 MB
18 - Convolution and transformations/003 Convolution in code.mp4 181.5 MB
08 - Overfitting and cross-validation/006 Cross-validation -- DataLoader.mp4 180.7 MB
23 - Generative adversarial networks/002 Linear GAN with MNIST.mp4 178.2 MB
07 - ANNs (Artificial Neural Networks)/009 Learning rates comparison.mp4 176.8 MB
12 - More on data/003 CodeChallenge unbalanced data.mp4 174.3 MB
11 - FFNs (Feed-Forward Networks)/003 FFN to classify digits.mp4 169.7 MB
16 - Autoencoders/005 The latent code of MNIST.mp4 169.7 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/004 Predicting alternating sequences.mp4 167.9 MB
07 - ANNs (Artificial Neural Networks)/018 Model depth vs. breadth.mp4 166.6 MB
12 - More on data/007 Data feature augmentation.mp4 166.0 MB
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GetFreeCourses.Co-Udemy-100 Days of Code - The Complete Python Pro Bootcamp for 2021.zip 23.6 GB
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- 【压缩文件】 100 Days of Code - The Complete Python Pro Bootcamp for 2021
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100 Days of Code - The Complete Python Pro Bootcamp for 2021.zip 23.5 GB
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06 Single Inheritance/002 Single Inheritance - Lecture.mp4 448.6 MB
12 Exceptions (Single Inheritance)/004 Handling Exceptions - Lecture.mp4 433.3 MB
14 Metaprogramming/021 Classes, Metaclasses, and __call__.mp4 417.9 MB
02 Classes/018 Properties - Lecture.mp4 411.4 MB
03 Project 1/011 Project Solution - Testing with unittest.mp4 338.7 MB
12 Exceptions (Single Inheritance)/002 Python Exceptions - Lecture.mp4 332.5 MB
14 Metaprogramming/012 Class Decorators - Coding.mp4 322.8 MB
02 Classes/004 Class Attributes - Lecture.mp4 321.7 MB
02 Classes/012 Function Attributes - Lecture.mp4 308.3 MB
06 Single Inheritance/010 Delegating to Parent - Lecture.mp4 305.2 MB
02 Classes/020 Property Decorators - Lecture.mp4 296.9 MB
06 Single Inheritance/006 Overriding - Lecture.mp4 285.9 MB
02 Classes/027 Class and Static Methods - Lecture.mp4 272.4 MB
02 Classes/002 Objects and Classes - Lecture.mp4 265.1 MB
12 Exceptions (Single Inheritance)/009 Custom Exceptions - Coding.mp4 263.1 MB
09 Project 4/002 Solution - Part 1.mp4 256.9 MB
14 Metaprogramming/003 The __new__ Method - Lecture.mp4 254.7 MB
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14 Metaprogramming/025 Attribute Read Accessors - Lecture.mp4 245.4 MB
10 Enumerations/003 Enumerations - Lecture.mp4 239.7 MB
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06 Single Inheritance/002 Single Inheritance - Lecture.mp4 448.6 MB
12 Exceptions (Single Inheritance)/004 Handling Exceptions - Lecture.mp4 433.3 MB
14 Metaprogramming/021 Classes, Metaclasses, and __call__.mp4 417.9 MB
02 Classes/018 Properties - Lecture.mp4 411.4 MB
03 Project 1/011 Project Solution - Testing with unittest.mp4 338.7 MB
12 Exceptions (Single Inheritance)/002 Python Exceptions - Lecture.mp4 332.5 MB
14 Metaprogramming/012 Class Decorators - Coding.mp4 322.8 MB
02 Classes/004 Class Attributes - Lecture.mp4 321.7 MB
02 Classes/012 Function Attributes - Lecture.mp4 308.3 MB
06 Single Inheritance/010 Delegating to Parent - Lecture.mp4 305.2 MB
02 Classes/020 Property Decorators - Lecture.mp4 296.9 MB
06 Single Inheritance/006 Overriding - Lecture.mp4 285.9 MB
02 Classes/027 Class and Static Methods - Lecture.mp4 272.4 MB
02 Classes/002 Objects and Classes - Lecture.mp4 265.1 MB
12 Exceptions (Single Inheritance)/009 Custom Exceptions - Coding.mp4 263.1 MB
09 Project 4/002 Solution - Part 1.mp4 256.9 MB
14 Metaprogramming/003 The __new__ Method - Lecture.mp4 254.7 MB
01 Introduction/001 Introduction.mp4 254.5 MB
14 Metaprogramming/025 Attribute Read Accessors - Lecture.mp4 245.4 MB
10 Enumerations/003 Enumerations - Lecture.mp4 239.7 MB
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29 Project-7 School donation analysis/260.Projects.csv 2.6 GB
29 Project-7 School donation analysis/261.Resources.csv 819.3 MB
29 Project-7 School donation analysis/258.Donations.csv 611.4 MB
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29 Project-7 School donation analysis/006 First analysis.mp4 224.5 MB
02 Installing Python on Mac/002 Installing Python on Mac.mp4 221.7 MB
18 Project-4 Tic Tac Toe/008 Player move.mp4 220.7 MB
20 Numpy/003 Methods for Numpy.mp4 210.0 MB
27 Project-5 Plotter/004 Creating data.mp4 202.0 MB
27 Project-5 Plotter/008 Plotting 2 and 3 columns.mp4 197.7 MB
31 Tkinter/019 Menu and menu button.mp4 181.1 MB
23 Seaborn/004 Categories of plots.mp4 179.0 MB
21 Pandas/013 Loading data using pandas.mp4 169.8 MB
23 Seaborn/010 Matrix plots.mp4 169.1 MB
03 Installing Python on windows/003 Installing Atom.mp4 163.0 MB
16 Project-2 Dictionary/011 How to get the close matches.mp4 159.7 MB
27 Project-5 Plotter/007 Plotting 1 column.mp4 158.5 MB
27 Project-5 Plotter/009 Plotter.mp4 155.1 MB
33 Turtle graphics/004 Creating the robot.mp4 155.0 MB
32 Databases/006 Creating table in postgreSQL.mp4 154.5 MB
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13. Programação Orientada a Objetos/17. Desafio POO (Resp).mp4 404.9 MB
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15. Programação Funcional/5. Funções Lambda.mp4 268.2 MB
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13. Programação Orientada a Objetos/15. Tratamento de Exceções.mp4 242.9 MB
14. Prog. Orientada a Objetos Avançada/3. Tipos de Métodos.mp4 238.7 MB
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13. Programação Orientada a Objetos/9. Classe Projeto.mp4 230.0 MB
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5. Fundamentos/1. Primeiro Programa.mp4 220.4 MB
5. Fundamentos/4. Comentários.mp4 215.6 MB
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5. Fundamentos/16. Builtins.mp4 208.7 MB
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13. Programação Orientada a Objetos/17. Desafio POO (Resp).mp4 404.9 MB
2. Introdução/5. Algoritmo.mp4 304.4 MB
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15. Programação Funcional/5. Funções Lambda.mp4 268.2 MB
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4. Executando Código Python/2. Executando Código no Jupyter.mp4 248.2 MB
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13. Programação Orientada a Objetos/9. Classe Projeto.mp4 230.0 MB
5. Fundamentos/31. Conjuntos.mp4 223.3 MB
5. Fundamentos/1. Primeiro Programa.mp4 220.4 MB
5. Fundamentos/4. Comentários.mp4 215.6 MB
7. Estruturas de Controle/9. FOR #04.mp4 214.0 MB
13. Programação Orientada a Objetos/13. Métodos Privados.mp4 212.0 MB
13. Programação Orientada a Objetos/1. Visão Geral da POO.mp4 209.7 MB
5. Fundamentos/16. Builtins.mp4 208.7 MB
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19 Understand and design CNNs/005 Examine feature map activations.mp4 273.2 MB
22 Style transfer/004 Transferring the screaming bathtub.mp4 227.4 MB
19 Understand and design CNNs/012 The EMNIST dataset (letter recognition).mp4 211.1 MB
19 Understand and design CNNs/002 CNN to classify MNIST digits.mp4 210.1 MB
07 ANNs/013 Multi-output ANN (iris dataset).mp4 195.8 MB
19 Understand and design CNNs/004 Classify Gaussian blurs.mp4 194.1 MB
09 Regularization/004 Dropout regularization in practice.mp4 192.1 MB
16 Autoencoders/006 Autoencoder with tied weights.mp4 186.4 MB
18 Convolution and transformations/003 Convolution in code.mp4 181.5 MB
08 Overfitting and cross-validation/006 Cross-validation -- DataLoader.mp4 180.7 MB
23 Generative adversarial networks/002 Linear GAN with MNIST.mp4 178.2 MB
07 ANNs/009 Learning rates comparison.mp4 176.8 MB
12 More on data/003 CodeChallenge_ unbalanced data.mp4 174.3 MB
11 FFNs/003 FFN to classify digits.mp4 169.7 MB
16 Autoencoders/005 The latent code of MNIST.mp4 169.7 MB
07 ANNs/018 Model depth vs. breadth.mp4 166.6 MB
12 More on data/007 Data feature augmentation.mp4 166.0 MB
21 Transfer learning/007 Pretraining with autoencoders.mp4 164.2 MB
14 FFN milestone projects/004 Project 2_ My solution.mp4 163.3 MB
21 Transfer learning/008 CIFAR10 with autoencoder-pretrained model.mp4 160.8 MB
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19 Understand and design CNNs/005 Examine feature map activations.mp4 273.2 MB
22 Style transfer/004 Transferring the screaming bathtub.mp4 227.4 MB
19 Understand and design CNNs/012 The EMNIST dataset (letter recognition).mp4 211.1 MB
19 Understand and design CNNs/002 CNN to classify MNIST digits.mp4 210.1 MB
07 ANNs/013 Multi-output ANN (iris dataset).mp4 195.8 MB
19 Understand and design CNNs/004 Classify Gaussian blurs.mp4 194.1 MB
09 Regularization/004 Dropout regularization in practice.mp4 192.1 MB
16 Autoencoders/006 Autoencoder with tied weights.mp4 186.4 MB
18 Convolution and transformations/003 Convolution in code.mp4 181.5 MB
08 Overfitting and cross-validation/006 Cross-validation -- DataLoader.mp4 180.7 MB
23 Generative adversarial networks/002 Linear GAN with MNIST.mp4 178.2 MB
07 ANNs/009 Learning rates comparison.mp4 176.8 MB
12 More on data/003 CodeChallenge_ unbalanced data.mp4 174.3 MB
11 FFNs/003 FFN to classify digits.mp4 169.7 MB
16 Autoencoders/005 The latent code of MNIST.mp4 169.7 MB
07 ANNs/018 Model depth vs. breadth.mp4 166.6 MB
12 More on data/007 Data feature augmentation.mp4 166.0 MB
21 Transfer learning/007 Pretraining with autoencoders.mp4 164.2 MB
14 FFN milestone projects/004 Project 2_ My solution.mp4 163.3 MB
21 Transfer learning/008 CIFAR10 with autoencoder-pretrained model.mp4 160.8 MB
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19 Understand and design CNNs/005 Examine feature map activations.mp4 273.2 MB
22 Style transfer/004 Transferring the screaming bathtub.mp4 227.4 MB
19 Understand and design CNNs/012 The EMNIST dataset (letter recognition).mp4 211.1 MB
19 Understand and design CNNs/002 CNN to classify MNIST digits.mp4 210.1 MB
07 ANNs/013 Multi-output ANN (iris dataset).mp4 195.8 MB
19 Understand and design CNNs/004 Classify Gaussian blurs.mp4 194.1 MB
09 Regularization/004 Dropout regularization in practice.mp4 192.1 MB
16 Autoencoders/006 Autoencoder with tied weights.mp4 186.4 MB
18 Convolution and transformations/003 Convolution in code.mp4 181.5 MB
08 Overfitting and cross-validation/006 Cross-validation -- DataLoader.mp4 180.7 MB
23 Generative adversarial networks/002 Linear GAN with MNIST.mp4 178.2 MB
07 ANNs/009 Learning rates comparison.mp4 176.8 MB
12 More on data/003 CodeChallenge_ unbalanced data.mp4 174.3 MB
11 FFNs/003 FFN to classify digits.mp4 169.7 MB
16 Autoencoders/005 The latent code of MNIST.mp4 169.7 MB
07 ANNs/018 Model depth vs. breadth.mp4 166.6 MB
12 More on data/007 Data feature augmentation.mp4 166.0 MB
21 Transfer learning/007 Pretraining with autoencoders.mp4 164.2 MB
14 FFN milestone projects/004 Project 2_ My solution.mp4 163.3 MB
21 Transfer learning/008 CIFAR10 with autoencoder-pretrained model.mp4 160.8 MB
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01 Введение/006 Терминал и Linux-команды.mp4 352.8 MB
14 Пишем Бэкдоры/010 Сериализация - Теория.mp4 317.3 MB
10 Обходим HTTPS/001 Как обойти HTTPS.mp4 304.5 MB
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13 Пишем Keylogger/13 Пишем Keylogger/006 Объектно-ориентированное программирование.mp4 271.9 MB
14 Пишем Бэкдоры/002 Взаимодействие клиента и сервера.mp4 259.8 MB
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12 Пишем Вредоносную Программу/12 Пишем Вредоносную Программу/008 Основы получения паролей.mp4 241.4 MB
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