首页 磁力链接怎么用

[GigaCourse.Com] Udemy - Tensorflow 2.0 Deep Learning and Artificial Intelligence

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 2023-6-15 13:28 2024-12-29 15:28 129 6.83 GB 133
磁力链接
magnet:?xt=urn:btih:d70b1933126900588bd11838743cc21140d2526a
迅雷链接
thunder://QUFtYWduZXQ6P3h0PXVybjpidGloOmQ3MGIxOTMzMTI2OTAwNTg4YmQxMTgzODc0M2NjMjExNDBkMjUyNmFaWg==
二维码链接
[GigaCourse.Com] Udemy - Tensorflow 2.0 Deep Learning and Artificial Intelligence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

GigaCourse Com Udemy - Tensorflow 2 0 Deep Learning and Artificial Intelligence

文件列表
  1. 1. Welcome/1. Introduction.mp434.81MB
  2. 1. Welcome/2. Outline.mp473.67MB
  3. 1. Welcome/3. Where to get the code.mp462.91MB
  4. 10. GANs (Generative Adversarial Networks)/1. GAN Theory.mp487.16MB
  5. 10. GANs (Generative Adversarial Networks)/2. GAN Code.mp478.3MB
  6. 11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.mp438.05MB
  7. 11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.mp440.11MB
  8. 11. Deep Reinforcement Learning (Theory)/11. Q-Learning.mp461.83MB
  9. 11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).mp456.27MB
  10. 11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).mp449.6MB
  11. 11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.mp437.7MB
  12. 11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.mp498.59MB
  13. 11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.mp443.33MB
  14. 11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).mp449.35MB
  15. 11. Deep Reinforcement Learning (Theory)/5. The Return.mp421.13MB
  16. 11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.mp443.56MB
  17. 11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.mp431.71MB
  18. 11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp442.74MB
  19. 11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp452.91MB
  20. 12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.mp426.04MB
  21. 12. Stock Trading Project with Deep Reinforcement Learning/10. Help! Why is the code slower on my machine.mp442.46MB
  22. 12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.mp450.97MB
  23. 12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.mp424.04MB
  24. 12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.mp425.98MB
  25. 12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.mp439.55MB
  26. 12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.mp468MB
  27. 12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.mp452.05MB
  28. 12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.mp452.51MB
  29. 12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.mp416.59MB
  30. 13. Advanced Tensorflow Usage/1. What is a Web Service (Tensorflow Serving pt 1).mp427.78MB
  31. 13. Advanced Tensorflow Usage/2. Tensorflow Serving pt 2.mp4104.99MB
  32. 13. Advanced Tensorflow Usage/3. Tensorflow Lite (TFLite).mp442.59MB
  33. 13. Advanced Tensorflow Usage/4. Why is Google the King of Distributed Computing.mp444.93MB
  34. 13. Advanced Tensorflow Usage/5. Training with Distributed Strategies.mp443.54MB
  35. 13. Advanced Tensorflow Usage/6. Using the TPU.mp445.24MB
  36. 14. Low-Level Tensorflow/1. Differences Between Tensorflow 1.x and Tensorflow 2.x.mp438.68MB
  37. 14. Low-Level Tensorflow/2. Constants and Basic Computation.mp440.3MB
  38. 14. Low-Level Tensorflow/3. Variables and Gradient Tape.mp456.05MB
  39. 14. Low-Level Tensorflow/4. Build Your Own Custom Model.mp458.55MB
  40. 15. In-Depth Loss Functions/1. Mean Squared Error.mp433.77MB
  41. 15. In-Depth Loss Functions/2. Binary Cross Entropy.mp423.68MB
  42. 15. In-Depth Loss Functions/3. Categorical Cross Entropy.mp431.7MB
  43. 16. In-Depth Gradient Descent/1. Gradient Descent.mp434.92MB
  44. 16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.mp422.97MB
  45. 16. In-Depth Gradient Descent/3. Momentum.mp434.25MB
  46. 16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.mp434.85MB
  47. 16. In-Depth Gradient Descent/5. Adam (pt 1).mp455.12MB
  48. 16. In-Depth Gradient Descent/6. Adam (pt 2).mp452.76MB
  49. 17. Extras/1. How to Choose Hyperparameters.mp437.92MB
  50. 17. Extras/2. Where Are The Exercises.mp425.98MB
  51. 18. Setting up your Environment (FAQ by Student Request)/1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4150.59MB
  52. 18. Setting up your Environment (FAQ by Student Request)/2. Anaconda Environment Setup.mp4180.9MB
  53. 18. Setting up your Environment (FAQ by Student Request)/3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4167.3MB
  54. 19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. Beginner's Coding Tips.mp475.71MB
  55. 19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code Yourself (part 1).mp471.85MB
  56. 19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. How to Code Yourself (part 2).mp449.14MB
  57. 19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Proof that using Jupyter Notebook is the same as not using it.mp469.45MB
  58. 19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/5. Is Theano Dead.mp440.76MB
  59. 2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.mp453.84MB
  60. 2. Google Colab/2. Tensorflow 2.0 in Google Colab.mp440.65MB
  61. 2. Google Colab/3. Uploading your own data to Google Colab.mp473.59MB
  62. 2. Google Colab/4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp438.93MB
  63. 2. Google Colab/5. How to Succeed in this Course.mp443.75MB
  64. 20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).mp435.22MB
  65. 20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4105.61MB
  66. 20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp479.71MB
  67. 20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4108.17MB
  68. 21. Appendix FAQ Finale/1. What is the Appendix.mp416.38MB
  69. 21. Appendix FAQ Finale/2. BONUS Lecture.mp437.79MB
  70. 3. Machine Learning and Neurons/1. What is Machine Learning.mp465.5MB
  71. 3. Machine Learning and Neurons/10. Why Keras.mp426.51MB
  72. 3. Machine Learning and Neurons/11. Suggestion Box.mp427.12MB
  73. 3. Machine Learning and Neurons/2. Code Preparation (Classification Theory).mp459.8MB
  74. 3. Machine Learning and Neurons/3. Classification Notebook.mp454.54MB
  75. 3. Machine Learning and Neurons/4. Code Preparation (Regression Theory).mp427.29MB
  76. 3. Machine Learning and Neurons/5. Regression Notebook.mp457.47MB
  77. 3. Machine Learning and Neurons/6. The Neuron.mp442.57MB
  78. 3. Machine Learning and Neurons/7. How does a model learn.mp447.95MB
  79. 3. Machine Learning and Neurons/8. Making Predictions.mp433.88MB
  80. 3. Machine Learning and Neurons/9. Saving and Loading a Model.mp429.73MB
  81. 4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.mp429.82MB
  82. 4. Feedforward Artificial Neural Networks/10. ANN for Regression.mp469.27MB
  83. 4. Feedforward Artificial Neural Networks/2. Beginners Rejoice The Math in This Course is Optional.mp468.52MB
  84. 4. Feedforward Artificial Neural Networks/3. Forward Propagation.mp446.7MB
  85. 4. Feedforward Artificial Neural Networks/4. The Geometrical Picture.mp456.43MB
  86. 4. Feedforward Artificial Neural Networks/5. Activation Functions.mp480.54MB
  87. 4. Feedforward Artificial Neural Networks/6. Multiclass Classification.mp441.38MB
  88. 4. Feedforward Artificial Neural Networks/7. How to Represent Images.mp470.46MB
  89. 4. Feedforward Artificial Neural Networks/8. Code Preparation (ANN).mp450.92MB
  90. 4. Feedforward Artificial Neural Networks/9. ANN for Image Classification.mp447.71MB
  91. 5. Convolutional Neural Networks/1. What is Convolution (part 1).mp479.77MB
  92. 5. Convolutional Neural Networks/10. Batch Normalization.mp421.11MB
  93. 5. Convolutional Neural Networks/11. Improving CIFAR-10 Results.mp472.91MB
  94. 5. Convolutional Neural Networks/2. What is Convolution (part 2).mp422.27MB
  95. 5. Convolutional Neural Networks/3. What is Convolution (part 3).mp427.64MB
  96. 5. Convolutional Neural Networks/4. Convolution on Color Images.mp469.44MB
  97. 5. Convolutional Neural Networks/5. CNN Architecture.mp480.58MB
  98. 5. Convolutional Neural Networks/6. CNN Code Preparation.mp476.88MB
  99. 5. Convolutional Neural Networks/7. CNN for Fashion MNIST.mp442.79MB
  100. 5. Convolutional Neural Networks/8. CNN for CIFAR-10.mp429.69MB
  101. 5. Convolutional Neural Networks/9. Data Augmentation.mp434.95MB
  102. 6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.mp490.15MB
  103. 6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).mp450.36MB
  104. 6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.mp464.65MB
  105. 6. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.mp4124.05MB
  106. 6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).mp429.12MB
  107. 6. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).mp423.3MB
  108. 6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).mp467.11MB
  109. 6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).mp432.97MB
  110. 6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).mp467.34MB
  111. 6. Recurrent Neural Networks, Time Series, and Sequence Data/18. Other Ways to Forecast.mp428.33MB
  112. 6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.mp446.75MB
  113. 6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.mp471.7MB
  114. 6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.mp416.2MB
  115. 6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.mp483MB
  116. 6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.mp418.43MB
  117. 6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.mp474.07MB
  118. 6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.mp452.48MB
  119. 6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).mp479.86MB
  120. 7. Natural Language Processing (NLP)/1. Embeddings.mp452.56MB
  121. 7. Natural Language Processing (NLP)/2. Code Preparation (NLP).mp457.04MB
  122. 7. Natural Language Processing (NLP)/3. Text Preprocessing.mp428.76MB
  123. 7. Natural Language Processing (NLP)/4. Text Classification with LSTMs.mp450.68MB
  124. 7. Natural Language Processing (NLP)/5. CNNs for Text.mp440.4MB
  125. 7. Natural Language Processing (NLP)/6. Text Classification with CNNs.mp439.62MB
  126. 8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.mp468.66MB
  127. 8. Recommender Systems/2. Recommender Systems with Deep Learning Code.mp458.81MB
  128. 9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.mp455.13MB
  129. 9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp431.57MB
  130. 9. Transfer Learning for Computer Vision/3. Large Datasets and Data Generators.mp436.56MB
  131. 9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.mp420.58MB
  132. 9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).mp466.52MB
  133. 9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).mp446.05MB
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