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[FreeTutorials.Us] Udemy - Artificial Intelligence Masterclass

文件类型 收录时间 最后活跃 资源热度 文件大小 文件数量
视频 2019-7-16 05:48 2024-11-17 09:30 147 6.1 GB 69
磁力链接
magnet:?xt=urn:btih:0cbfbe4bdd220e48976639ff1b095c571a8a26c6
迅雷链接
thunder://QUFtYWduZXQ6P3h0PXVybjpidGloOjBjYmZiZTRiZGQyMjBlNDg5NzY2MzlmZjFiMDk1YzU3MWE4YTI2YzZaWg==
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[FreeTutorials.Us] Udemy - Artificial Intelligence Masterclass的二维码
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FreeTutorials Us Udemy - Artificial Intelligence Masterclass

文件列表
  1. 1. Introduction/1. Updates on Udemy Reviews.mp422.03MB
  2. 1. Introduction/2. Introduction + Course Structure + Demo.mp4195.34MB
  3. 1. Introduction/4. Your Three Best Resources.mp4134.49MB
  4. 10. Step 9 - Reinforcement Learning/2. What is Reinforcement Learning.mp468.6MB
  5. 10. Step 9 - Reinforcement Learning/3. A Pseudo Implementation of Reinforcement Learning for the Full World Model.mp4154.25MB
  6. 11. Step 10 - Deep NeuroEvolution/2. Deep NeuroEvolution.mp4108.84MB
  7. 11. Step 10 - Deep NeuroEvolution/3. Evolution Strategies.mp4119.44MB
  8. 11. Step 10 - Deep NeuroEvolution/4. Genetic Algorithms.mp4149.11MB
  9. 11. Step 10 - Deep NeuroEvolution/5. Covariance-Matrix Adaptation Evolution Strategy (CMA-ES).mp4144.06MB
  10. 11. Step 10 - Deep NeuroEvolution/6. Parameter-Exploring Policy Gradients (PEPG).mp4143.9MB
  11. 11. Step 10 - Deep NeuroEvolution/7. OpenAI Evolution Strategy.mp4108.09MB
  12. 12. The Final Run/1. The Whole Implementation.mp4273.65MB
  13. 12. The Final Run/3. Installing the required packages.mp4158.71MB
  14. 12. The Final Run/4. The Final Race Human Intelligence vs. Artificial Intelligence.mp4125.09MB
  15. 12. The Final Run/5. THANK YOU bonus video.mp429.21MB
  16. 2. Step 1 - Artificial Neural Network/2. Plan of Attack.mp415.85MB
  17. 2. Step 1 - Artificial Neural Network/3. The Neuron.mp498.79MB
  18. 2. Step 1 - Artificial Neural Network/4. The Activation Function.mp445.36MB
  19. 2. Step 1 - Artificial Neural Network/5. How do Neural Networks work.mp481.94MB
  20. 2. Step 1 - Artificial Neural Network/6. How do Neural Networks learn.mp4112.11MB
  21. 2. Step 1 - Artificial Neural Network/7. Gradient Descent.mp460.62MB
  22. 2. Step 1 - Artificial Neural Network/8. Stochastic Gradient Descent.mp467.29MB
  23. 2. Step 1 - Artificial Neural Network/9. Backpropagation.mp443.14MB
  24. 3. Step 2 - Convolutional Neural Network/10. Softmax & Cross-Entropy.mp4117.97MB
  25. 3. Step 2 - Convolutional Neural Network/2. Plan of Attack.mp421.81MB
  26. 3. Step 2 - Convolutional Neural Network/3. What are Convolutional Neural Networks.mp4107.97MB
  27. 3. Step 2 - Convolutional Neural Network/4. Step 1 - The Convolution Operation.mp497.93MB
  28. 3. Step 2 - Convolutional Neural Network/5. Step 1 Bis - The ReLU Layer.mp453.44MB
  29. 3. Step 2 - Convolutional Neural Network/6. Step 2 - Pooling.mp4140.17MB
  30. 3. Step 2 - Convolutional Neural Network/7. Step 3 - Flattening.mp47.94MB
  31. 3. Step 2 - Convolutional Neural Network/8. Step 4 - Full Connection.mp4194.26MB
  32. 3. Step 2 - Convolutional Neural Network/9. Summary.mp430.33MB
  33. 4. Step 3 - AutoEncoder/10. Stacked AutoEncoders.mp416.44MB
  34. 4. Step 3 - AutoEncoder/11. Deep AutoEncoders.mp411.97MB
  35. 4. Step 3 - AutoEncoder/2. Plan of Attack.mp415.85MB
  36. 4. Step 3 - AutoEncoder/3. What are AutoEncoders.mp494.61MB
  37. 4. Step 3 - AutoEncoder/4. A Note on Biases.mp48.61MB
  38. 4. Step 3 - AutoEncoder/5. Training an AutoEncoder.mp450.3MB
  39. 4. Step 3 - AutoEncoder/6. Overcomplete Hidden Layers.mp428.06MB
  40. 4. Step 3 - AutoEncoder/7. Sparse AutoEncoders.mp457.45MB
  41. 4. Step 3 - AutoEncoder/8. Denoising AutoEncoders.mp424.1MB
  42. 4. Step 3 - AutoEncoder/9. Contractive AutoEncoders.mp420.55MB
  43. 5. Step 4 - Variational AutoEncoder/2. Introduction to the VAE.mp472.81MB
  44. 5. Step 4 - Variational AutoEncoder/3. Variational AutoEncoders.mp426.31MB
  45. 5. Step 4 - Variational AutoEncoder/4. Reparameterization Trick.mp426.41MB
  46. 6. Step 5 - Implementing the CNN-VAE/2. Introduction to Step 5.mp458.85MB
  47. 6. Step 5 - Implementing the CNN-VAE/3. Initializing all the parameters and variables of the CNN-VAE class.mp471.72MB
  48. 6. Step 5 - Implementing the CNN-VAE/4. Building the Encoder part of the VAE.mp4133.64MB
  49. 6. Step 5 - Implementing the CNN-VAE/5. Building the V part of the VAE.mp480.33MB
  50. 6. Step 5 - Implementing the CNN-VAE/6. Building the Decoder part of the VAE.mp492.89MB
  51. 6. Step 5 - Implementing the CNN-VAE/7. Implementing the Training operations.mp4186.98MB
  52. 7. Step 6 - Recurrent Neural Network/2. Plan of Attack.mp410.5MB
  53. 7. Step 6 - Recurrent Neural Network/3. What are Recurrent Neural Networks.mp4121.09MB
  54. 7. Step 6 - Recurrent Neural Network/4. The Vanishing Gradient Problem.mp4111.17MB
  55. 7. Step 6 - Recurrent Neural Network/5. LSTMs.mp4136.52MB
  56. 7. Step 6 - Recurrent Neural Network/6. LSTM Practical Intuition.mp4187.41MB
  57. 7. Step 6 - Recurrent Neural Network/7. LSTM Variations.mp420.12MB
  58. 8. Step 7 - Mixture Density Network/2. Introduction to the MDN-RNN.mp483.39MB
  59. 8. Step 7 - Mixture Density Network/3. Mixture Density Networks.mp465.35MB
  60. 8. Step 7 - Mixture Density Network/4. VAE + MDN-RNN Visualization.mp445.3MB
  61. 9. Step 8 - Implementing the MDN-RNN/10. Implementing the Training operations (Part 2).mp4162.89MB
  62. 9. Step 8 - Implementing the MDN-RNN/2. Initializing all the parameters and variables of the MDN-RNN class.mp499.49MB
  63. 9. Step 8 - Implementing the MDN-RNN/3. Building the RNN - Gathering the parameters.mp476.58MB
  64. 9. Step 8 - Implementing the MDN-RNN/4. Building the RNN - Creating an LSTM cell with Dropout.mp4127.16MB
  65. 9. Step 8 - Implementing the MDN-RNN/5. Building the RNN - Setting up the Input, Target, and Output of the RNN.mp4131.12MB
  66. 9. Step 8 - Implementing the MDN-RNN/6. Building the RNN - Getting the Deterministic Output of the RNN.mp4125.49MB
  67. 9. Step 8 - Implementing the MDN-RNN/7. Building the MDN - Getting the Input, Hidden Layer and Output of the MDN.mp4146.98MB
  68. 9. Step 8 - Implementing the MDN-RNN/8. Building the MDN - Getting the MDN parameters.mp4109.44MB
  69. 9. Step 8 - Implementing the MDN-RNN/9. Implementing the Training operations (Part 1).mp4177.44MB
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概述 838888磁力搜索是一个磁力链接搜索引擎，是学术研究的副产品，用于解决资源过度分散的问题 它通过BitTorrent协议加入DHT网络，实时的自动采集数据，仅存储文件的标题、大小、文件列表、文件标识符（磁力链接）等基础信息 838888磁力搜索不下载任何真实资源，无法判断资源的合法性及真实性，使用838888磁力搜索服务的用户需自行鉴别内容的真伪 838888磁力搜索不上传任何资源，不提供Tracker服务，不提供种子文件的下载，这意味着838888磁力搜索 838888磁力搜索是一个完全合法的系统
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