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[DesireCourse.Com] 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 2019-6-22 22:04 2024-6-10 12:11 82 4.55 GB 55
磁力链接
magnet:?xt=urn:btih:63dda960206c06f8e234022d2b8161d58e8602d1
迅雷链接
thunder://QUFtYWduZXQ6P3h0PXVybjpidGloOjYzZGRhOTYwMjA2YzA2ZjhlMjM0MDIyZDJiODE2MWQ1OGU4NjAyZDFaWg==
二维码链接
[DesireCourse.Com] Udemy - Artificial Intelligence Masterclass的二维码
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DesireCourse Com Udemy - Artificial Intelligence Masterclass

文件列表
  1. 1. Introduction/1. Introduction + Course Structure + Demo.mp4156.75MB
  2. 1. Introduction/2. Your Three Best Resources.mp4143.27MB
  3. 10. Step 9 - Reinforcement Learning/2. What is Reinforcement Learning.mp468.59MB
  4. 10. Step 9 - Reinforcement Learning/3. A Pseudo Implementation of Reinforcement Learning for the Full World Model.mp4154.25MB
  5. 2. Step 1 - Artificial Neural Network/2. Plan of Attack.mp411.86MB
  6. 2. Step 1 - Artificial Neural Network/3. The Neuron.mp498.79MB
  7. 2. Step 1 - Artificial Neural Network/4. The Activation Function.mp445.35MB
  8. 2. Step 1 - Artificial Neural Network/5. How do Neural Networks work.mp481.94MB
  9. 2. Step 1 - Artificial Neural Network/6. How do Neural Networks learn.mp4112.12MB
  10. 2. Step 1 - Artificial Neural Network/7. Gradient Descent.mp460.62MB
  11. 2. Step 1 - Artificial Neural Network/8. Stochastic Gradient Descent.mp467.3MB
  12. 2. Step 1 - Artificial Neural Network/9. Backpropagation.mp443.15MB
  13. 3. Step 2 - Convolutional Neural Network/10. Softmax & Cross-Entropy.mp4117.97MB
  14. 3. Step 2 - Convolutional Neural Network/2. Plan of Attack.mp415.81MB
  15. 3. Step 2 - Convolutional Neural Network/3. What are Convolutional Neural Networks.mp4107.97MB
  16. 3. Step 2 - Convolutional Neural Network/4. Step 1 - The Convolution Operation.mp497.93MB
  17. 3. Step 2 - Convolutional Neural Network/5. Step 1 Bis - The ReLU Layer.mp453.45MB
  18. 3. Step 2 - Convolutional Neural Network/6. Step 2 - Pooling.mp4140.17MB
  19. 3. Step 2 - Convolutional Neural Network/7. Step 3 - Flattening.mp47.95MB
  20. 3. Step 2 - Convolutional Neural Network/8. Step 4 - Full Connection.mp4194.27MB
  21. 3. Step 2 - Convolutional Neural Network/9. Summary.mp430.33MB
  22. 4. Step 3 - AutoEncoder/10. Stacked AutoEncoders.mp416.44MB
  23. 4. Step 3 - AutoEncoder/11. Deep AutoEncoders.mp411.97MB
  24. 4. Step 3 - AutoEncoder/2. Plan of Attack.mp411.84MB
  25. 4. Step 3 - AutoEncoder/3. What are AutoEncoders.mp494.61MB
  26. 4. Step 3 - AutoEncoder/4. A Note on Biases.mp48.61MB
  27. 4. Step 3 - AutoEncoder/5. Training an AutoEncoder.mp450.3MB
  28. 4. Step 3 - AutoEncoder/6. Overcomplete Hidden Layers.mp428.06MB
  29. 4. Step 3 - AutoEncoder/7. Sparse AutoEncoders.mp457.45MB
  30. 4. Step 3 - AutoEncoder/8. Denoising AutoEncoders.mp424.1MB
  31. 4. Step 3 - AutoEncoder/9. Contractive AutoEncoders.mp420.56MB
  32. 5. Step 4 - Variational AutoEncoder/2. Introduction to the VAE.mp4103.68MB
  33. 5. Step 4 - Variational AutoEncoder/3. Variational AutoEncoders.mp426.31MB
  34. 5. Step 4 - Variational AutoEncoder/4. Reparameterization Trick.mp426.4MB
  35. 6. Step 5 - Implementing the CNN-VAE/2. Introduction to Step 5.mp458.86MB
  36. 6. Step 5 - Implementing the CNN-VAE/3. Initializing all the parameters and variables of the CNN-VAE class.mp471.72MB
  37. 6. Step 5 - Implementing the CNN-VAE/4. Building the Encoder part of the VAE.mp4133.65MB
  38. 6. Step 5 - Implementing the CNN-VAE/5. Building the V part of the VAE.mp480.33MB
  39. 6. Step 5 - Implementing the CNN-VAE/6. Building the Decoder part of the VAE.mp492.89MB
  40. 6. Step 5 - Implementing the CNN-VAE/7. Implementing the Training operations.mp4186.98MB
  41. 7. Step 6 - Recurrent Neural Network/2. Plan of Attack.mp410.5MB
  42. 7. Step 6 - Recurrent Neural Network/3. What are Recurrent Neural Networks.mp4121.1MB
  43. 7. Step 6 - Recurrent Neural Network/4. The Vanishing Gradient Problem.mp4111.17MB
  44. 7. Step 6 - Recurrent Neural Network/5. LSTMs.mp4136.52MB
  45. 7. Step 6 - Recurrent Neural Network/6. LSTM Practical Intuition.mp4187.42MB
  46. 7. Step 6 - Recurrent Neural Network/7. LSTM Variations.mp420.13MB
  47. 9. Step 8 - Implementing the MDN-RNN/10. Implementing the Training operations (Part 2).mp4162.89MB
  48. 9. Step 8 - Implementing the MDN-RNN/2. Initializing all the parameters and variables of the MDN-RNN class.mp499.49MB
  49. 9. Step 8 - Implementing the MDN-RNN/3. Building the RNN - Gathering the parameters.mp476.58MB
  50. 9. Step 8 - Implementing the MDN-RNN/4. Building the RNN - Creating an LSTM cell with Dropout.mp4127.16MB
  51. 9. Step 8 - Implementing the MDN-RNN/5. Building the RNN - Setting up the Input, Target, and Output of the RNN.mp4131.12MB
  52. 9. Step 8 - Implementing the MDN-RNN/6. Building the RNN - Getting the Deterministic Output of the RNN.mp4125.5MB
  53. 9. Step 8 - Implementing the MDN-RNN/7. Building the MDN - Getting the Input, Hidden Layer and Output of the MDN.mp4146.97MB
  54. 9. Step 8 - Implementing the MDN-RNN/8. Building the MDN - Getting the MDN parameters.mp4109.45MB
  55. 9. Step 8 - Implementing the MDN-RNN/9. Implementing the Training operations (Part 1).mp4177.45MB
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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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