首页 磁力链接怎么用

[FreeCourseSite.com] Udemy - A Complete Guide on TensorFlow 2.0 using Keras API

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 2021-6-1 07:15 2024-11-1 14:36 296 5.12 GB 120
磁力链接
magnet:?xt=urn:btih:d48852f2a46869f29354278b5077f8c1db94e241
迅雷链接
thunder://QUFtYWduZXQ6P3h0PXVybjpidGloOmQ0ODg1MmYyYTQ2ODY5ZjI5MzU0Mjc4YjUwNzdmOGMxZGI5NGUyNDFaWg==
二维码链接
[FreeCourseSite.com] Udemy - A Complete Guide on TensorFlow 2.0 using Keras API的二维码
种子下载(838888不存储任何种子文件)
种子下载线路1(迅雷)--推荐
种子下载线路2(比特彗星)
种子下载线路3(torcache)
3条线路均为国内外知名下载网站种子链接，内容跟本站无关！
相关链接

FreeCourseSite com Udemy - A Complete Guide on TensorFlow 2 0 using Keras API

文件列表
  1. 1. Introduction/1. Welcome to the TensorFlow 2.0 course! Discover its structure and the TF toolkit..mp4146.28MB
  2. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/1. Project Setup.mp410.05MB
  3. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/2. Initial dataset preprocessing.mp434.99MB
  4. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/3. Dataset metadata.mp420.94MB
  5. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/4. Preprocessing function.mp421.12MB
  6. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/5. Dataset preprocessing pipeline.mp473.97MB
  7. 11. Fashion API with Flask and TensorFlow 2.0/1. Project Setup.mp436.74MB
  8. 11. Fashion API with Flask and TensorFlow 2.0/2. Importing project dependencies.mp411.9MB
  9. 11. Fashion API with Flask and TensorFlow 2.0/3. Loading a pre-trained model.mp420.49MB
  10. 11. Fashion API with Flask and TensorFlow 2.0/4. Defining the Flask application.mp412.37MB
  11. 11. Fashion API with Flask and TensorFlow 2.0/5. Creating classify function.mp453.13MB
  12. 11. Fashion API with Flask and TensorFlow 2.0/6. Starting the Flask application.mp427.63MB
  13. 11. Fashion API with Flask and TensorFlow 2.0/7. Sending API requests over internet to the model.mp435.01MB
  14. 12. Image Classification API with TensorFlow Serving/1. What is the TensorFlow Serving.mp424.46MB
  15. 12. Image Classification API with TensorFlow Serving/10. Sending the POST request to a specific model.mp49.65MB
  16. 12. Image Classification API with TensorFlow Serving/2. TensorFlow Serving architecture.mp419.5MB
  17. 12. Image Classification API with TensorFlow Serving/3. Project setup.mp425.52MB
  18. 12. Image Classification API with TensorFlow Serving/4. Dataset preprocessing.mp423.72MB
  19. 12. Image Classification API with TensorFlow Serving/5. Defining, training and evaluating a model.mp423.34MB
  20. 12. Image Classification API with TensorFlow Serving/6. Saving the model for production.mp425.43MB
  21. 12. Image Classification API with TensorFlow Serving/7. Serving the TensorFlow 2.0 Model.mp427.91MB
  22. 12. Image Classification API with TensorFlow Serving/8. Creating a JSON object.mp423.59MB
  23. 12. Image Classification API with TensorFlow Serving/9. Sending the first POST request to the model.mp427.32MB
  24. 13. TensorFlow Lite Prepare a model for a mobile device/1. What is the TensorFlow Lite.mp413.96MB
  25. 13. TensorFlow Lite Prepare a model for a mobile device/2. Project setup.mp48.04MB
  26. 13. TensorFlow Lite Prepare a model for a mobile device/3. Dataset preprocessing.mp428.77MB
  27. 13. TensorFlow Lite Prepare a model for a mobile device/4. Building a model.mp414.84MB
  28. 13. TensorFlow Lite Prepare a model for a mobile device/5. Training, evaluating the model.mp415.19MB
  29. 13. TensorFlow Lite Prepare a model for a mobile device/6. Saving the model.mp49.4MB
  30. 13. TensorFlow Lite Prepare a model for a mobile device/7. TensorFlow Lite Converter.mp46.29MB
  31. 13. TensorFlow Lite Prepare a model for a mobile device/8. Converting the model to a TensorFlow Lite model.mp44.92MB
  32. 13. TensorFlow Lite Prepare a model for a mobile device/9. Saving the converted model.mp48.69MB
  33. 14. Distributed Training with TensorFlow 2.0/1. What is the Distributed Training.mp411.08MB
  34. 14. Distributed Training with TensorFlow 2.0/2. Project Setup.mp49.08MB
  35. 14. Distributed Training with TensorFlow 2.0/3. Dataset preprocessing.mp425.58MB
  36. 14. Distributed Training with TensorFlow 2.0/4. Defining a non-distributed model (normal CNN model).mp414.05MB
  37. 14. Distributed Training with TensorFlow 2.0/5. Setting up a distributed strategy.mp47.4MB
  38. 14. Distributed Training with TensorFlow 2.0/6. Defining a distributed model.mp412.5MB
  39. 14. Distributed Training with TensorFlow 2.0/7. Final evaluation - Speed test normal model vs distributed model.mp428.42MB
  40. 15. Annex 1 - Artificial Neural Networks Theory/1. Plan of Attack.mp411.84MB
  41. 15. Annex 1 - Artificial Neural Networks Theory/2. The Neuron.mp498.69MB
  42. 15. Annex 1 - Artificial Neural Networks Theory/3. The Activation Function.mp445.34MB
  43. 15. Annex 1 - Artificial Neural Networks Theory/4. How do Neural Networks Work.mp481.8MB
  44. 15. Annex 1 - Artificial Neural Networks Theory/5. How do Neural Networks Learn.mp4112.15MB
  45. 15. Annex 1 - Artificial Neural Networks Theory/6. Gradient Descent.mp460.57MB
  46. 15. Annex 1 - Artificial Neural Networks Theory/7. Stochastic Gradient Descent.mp467.25MB
  47. 15. Annex 1 - Artificial Neural Networks Theory/8. Backpropagation.mp443.13MB
  48. 16. Annex 2 - Convolutional Neural Networks Theory/1. Plan of Attack.mp415.79MB
  49. 16. Annex 2 - Convolutional Neural Networks Theory/2. What are Convolutional Neural Networks.mp4107.88MB
  50. 16. Annex 2 - Convolutional Neural Networks Theory/3. Step 1 - Convolution.mp497.85MB
  51. 16. Annex 2 - Convolutional Neural Networks Theory/4. Step 1 Bis - ReLU Layer.mp453.37MB
  52. 16. Annex 2 - Convolutional Neural Networks Theory/5. Step 2 - Max Pooling.mp4140.21MB
  53. 16. Annex 2 - Convolutional Neural Networks Theory/6. Step 3 - Flattening.mp47.92MB
  54. 16. Annex 2 - Convolutional Neural Networks Theory/7. Step 4 - Full Connection.mp4194.15MB
  55. 16. Annex 2 - Convolutional Neural Networks Theory/8. Summary.mp430.33MB
  56. 16. Annex 2 - Convolutional Neural Networks Theory/9. Softmax & Cross-Entropy.mp4117.83MB
  57. 17. Annex 3 - Recurrent Neural Networks Theory/1. Plan of Attack.mp410.48MB
  58. 17. Annex 3 - Recurrent Neural Networks Theory/2. What are Recurrent Neural Networks.mp4120.95MB
  59. 17. Annex 3 - Recurrent Neural Networks Theory/3. Vanishing Gradient.mp4111MB
  60. 17. Annex 3 - Recurrent Neural Networks Theory/4. LSTMs.mp4136.43MB
  61. 17. Annex 3 - Recurrent Neural Networks Theory/5. LSTM Practical Intuition.mp4187.41MB
  62. 17. Annex 3 - Recurrent Neural Networks Theory/6. LSTM Variations.mp420.13MB
  63. 2. TensorFlow 2.0 Basics/1. From TensorFlow 1.x to TensorFlow 2.0.mp4114.79MB
  64. 2. TensorFlow 2.0 Basics/2. Constants, Variables, Tensors.mp471.34MB
  65. 2. TensorFlow 2.0 Basics/3. Operations with Tensors.mp449.26MB
  66. 2. TensorFlow 2.0 Basics/4. Strings.mp440.23MB
  67. 3. Artificial Neural Networks/1. Project Setup.mp459.26MB
  68. 3. Artificial Neural Networks/2. Data Preprocessing.mp461.76MB
  69. 3. Artificial Neural Networks/3. Building the Artificial Neural Network.mp460.44MB
  70. 3. Artificial Neural Networks/4. Training the Artificial Neural Network.mp448.52MB
  71. 3. Artificial Neural Networks/5. Evaluating the Artificial Neural Network.mp431.45MB
  72. 4. Convolutional Neural Networks/1. Project Setup & Data Preprocessing.mp447.36MB
  73. 4. Convolutional Neural Networks/2. Building the Convolutional Neural Network.mp488.19MB
  74. 4. Convolutional Neural Networks/3. Training and Evaluating the Convolutional Neural Network.mp458.24MB
  75. 5. Recurrent Neural Networks/1. Project Setup & Data Preprocessing.mp446.44MB
  76. 5. Recurrent Neural Networks/2. Building the Recurrent Neural Network.mp440.04MB
  77. 5. Recurrent Neural Networks/3. Training and Evaluating the Recurrent Neural Network.mp448.88MB
  78. 6. Transfer Learning and Fine Tuning/1. What is Transfer Learning.mp446.49MB
  79. 6. Transfer Learning and Fine Tuning/10. Transfer Learning.mp416.82MB
  80. 6. Transfer Learning and Fine Tuning/11. Evaluating Transfer Learning results.mp49.37MB
  81. 6. Transfer Learning and Fine Tuning/12. Fine Tuning model definition.mp424.59MB
  82. 6. Transfer Learning and Fine Tuning/13. Compiling the Fine Tuning model.mp46.41MB
  83. 6. Transfer Learning and Fine Tuning/14. Fine Tuning.mp410.18MB
  84. 6. Transfer Learning and Fine Tuning/15. Evaluating Fine Tuning results.mp49MB
  85. 6. Transfer Learning and Fine Tuning/2. Project Setup.mp449.38MB
  86. 6. Transfer Learning and Fine Tuning/3. Dataset preprocessing.mp431.83MB
  87. 6. Transfer Learning and Fine Tuning/4. Loading the MobileNet V2 model.mp417.84MB
  88. 6. Transfer Learning and Fine Tuning/5. Freezing the pre-trained model.mp46.07MB
  89. 6. Transfer Learning and Fine Tuning/6. Adding a custom head to the pre-trained model.mp419.69MB
  90. 6. Transfer Learning and Fine Tuning/7. Defining the transfer learning model.mp413.19MB
  91. 6. Transfer Learning and Fine Tuning/8. Compiling the Transfer Learning model.mp412.58MB
  92. 6. Transfer Learning and Fine Tuning/9. Image Data Generators.mp432.57MB
  93. 7. Deep Reinforcement Learning Theory/1. What is Reinforcement Learning.mp468.55MB
  94. 7. Deep Reinforcement Learning Theory/2. The Bellman Equation.mp495.06MB
  95. 7. Deep Reinforcement Learning Theory/3. Markov Decision Process (MDP).mp494.28MB
  96. 7. Deep Reinforcement Learning Theory/4. Q-Learning Intuition.mp479.07MB
  97. 7. Deep Reinforcement Learning Theory/5. Temporal Difference.mp497.1MB
  98. 7. Deep Reinforcement Learning Theory/6. Deep Q-Learning Intuition - Step 1.mp499.92MB
  99. 7. Deep Reinforcement Learning Theory/7. Deep Q-Learning Intuition - Step 2.mp443.07MB
  100. 7. Deep Reinforcement Learning Theory/8. Experience Replay.mp4114.74MB
  101. 7. Deep Reinforcement Learning Theory/9. Action Selection Policies.mp4136.84MB
  102. 8. Deep Reinforcement Learning for Stock Market trading/1. Project Setup.mp411.9MB
  103. 8. Deep Reinforcement Learning for Stock Market trading/10. Defining the model.mp411.86MB
  104. 8. Deep Reinforcement Learning for Stock Market trading/11. Training loop - Step 1.mp428.12MB
  105. 8. Deep Reinforcement Learning for Stock Market trading/12. Training loop - Step 2.mp454.18MB
  106. 8. Deep Reinforcement Learning for Stock Market trading/2. AI Trader - Step 1.mp427.2MB
  107. 8. Deep Reinforcement Learning for Stock Market trading/3. AI Trader - Step 2.mp411.9MB
  108. 8. Deep Reinforcement Learning for Stock Market trading/4. AI Trader - Step 3.mp415.83MB
  109. 8. Deep Reinforcement Learning for Stock Market trading/5. AI Trader - Step 4.mp415.92MB
  110. 8. Deep Reinforcement Learning for Stock Market trading/6. AI Trader - Step 5.mp433.2MB
  111. 8. Deep Reinforcement Learning for Stock Market trading/7. Dataset Loader function.mp438.89MB
  112. 8. Deep Reinforcement Learning for Stock Market trading/8. State creator function.mp432.32MB
  113. 8. Deep Reinforcement Learning for Stock Market trading/9. Loading the dataset.mp410.04MB
  114. 9. Data Validation with TensorFlow Data Validation (TFDV)/1. Project Setup.mp422.29MB
  115. 9. Data Validation with TensorFlow Data Validation (TFDV)/2. Loading the pollution dataset.mp424.84MB
  116. 9. Data Validation with TensorFlow Data Validation (TFDV)/3. Creating dataset Schema.mp424.09MB
  117. 9. Data Validation with TensorFlow Data Validation (TFDV)/4. Computing test set statistics.mp42.46MB
  118. 9. Data Validation with TensorFlow Data Validation (TFDV)/5. Anomaly detection with TensorFlow Data Validation.mp423.96MB
  119. 9. Data Validation with TensorFlow Data Validation (TFDV)/6. Preparing Schema for production.mp419.71MB
  120. 9. Data Validation with TensorFlow Data Validation (TFDV)/7. Saving the Schema.mp48.09MB
友情提示
不会用的朋友看这里 把磁力链接复制到离线下载，或者bt下载软件里即可下载文件，或者直接复制迅雷链接到迅雷里下载！ 亲，你造吗？将网页分享给您的基友，下载的人越多速度越快哦！

违规内容投诉邮箱：[email protected]

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
838888磁力链接 磁力链接bt种子搜索引擎磁力搜索 DMCA