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[DesireCourse.Net] Udemy - Tensorflow 2.0 Deep Learning and Artificial Intelligence

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视频 2020-2-28 18:24 2024-11-19 19:06 129 6.96 GB 122
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DesireCourse Net Udemy - Tensorflow 2 0 Deep Learning and Artificial Intelligence

文件列表
  1. 1. Welcome/1. Introduction.mp439.15MB
  2. 1. Welcome/2. Outline.mp473.7MB
  3. 1. Welcome/3. Where to get the code.mp430.48MB
  4. 10. GANs (Generative Adversarial Networks)/1. GAN Theory.mp486.53MB
  5. 10. GANs (Generative Adversarial Networks)/2. GAN Code.mp478.19MB
  6. 11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.mp437.77MB
  7. 11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.mp437.58MB
  8. 11. Deep Reinforcement Learning (Theory)/11. Q-Learning.mp461.28MB
  9. 11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).mp455.7MB
  10. 11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).mp449.2MB
  11. 11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.mp437.5MB
  12. 11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.mp497.78MB
  13. 11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.mp442.99MB
  14. 11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).mp448.96MB
  15. 11. Deep Reinforcement Learning (Theory)/5. The Return.mp420.94MB
  16. 11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.mp443.28MB
  17. 11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.mp430.35MB
  18. 11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp439.03MB
  19. 11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp452.54MB
  20. 12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.mp429.65MB
  21. 12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.mp456.02MB
  22. 12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.mp424.06MB
  23. 12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.mp429.77MB
  24. 12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.mp446.83MB
  25. 12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.mp483.39MB
  26. 12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.mp462.33MB
  27. 12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.mp459.15MB
  28. 12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.mp418.17MB
  29. 13. Advanced Tensorflow Usage/1. What is a Web Service (Tensorflow Serving pt 1).mp431.56MB
  30. 13. Advanced Tensorflow Usage/2. Tensorflow Serving pt 2.mp4124.46MB
  31. 13. Advanced Tensorflow Usage/3. Tensorflow Lite (TFLite).mp442.39MB
  32. 13. Advanced Tensorflow Usage/4. Why is Google the King of Distributed Computing.mp450.81MB
  33. 13. Advanced Tensorflow Usage/5. Training with Distributed Strategies.mp450.06MB
  34. 14. Low-Level Tensorflow/1. Differences Between Tensorflow 1.x and Tensorflow 2.x.mp442.5MB
  35. 14. Low-Level Tensorflow/2. Constants and Basic Computation.mp450.24MB
  36. 14. Low-Level Tensorflow/3. Variables and Gradient Tape.mp470.61MB
  37. 14. Low-Level Tensorflow/4. Build Your Own Custom Model.mp470.18MB
  38. 15. In-Depth Loss Functions/1. Mean Squared Error.mp437.34MB
  39. 15. In-Depth Loss Functions/2. Binary Cross Entropy.mp421.5MB
  40. 15. In-Depth Loss Functions/3. Categorical Cross Entropy.mp435.43MB
  41. 16. In-Depth Gradient Descent/1. Gradient Descent.mp434.89MB
  42. 16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.mp425.05MB
  43. 16. In-Depth Gradient Descent/3. Momentum.mp439.35MB
  44. 16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.mp438.52MB
  45. 16. In-Depth Gradient Descent/5. Adam.mp442.57MB
  46. 18. Setting up your Environment/1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4166.72MB
  47. 18. Setting up your Environment/2. Windows-Focused Environment Setup 2018.mp4193.99MB
  48. 18. Setting up your Environment/3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4167.29MB
  49. 19. Appendix FAQ/1. What is the Appendix.mp418.04MB
  50. 19. Appendix FAQ/10. BONUS Where to get discount coupons and FREE deep learning material.mp437.85MB
  51. 19. Appendix FAQ/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4117.07MB
  52. 19. Appendix FAQ/3. How to Code Yourself (part 1).mp482.12MB
  53. 19. Appendix FAQ/4. How to Code Yourself (part 2).mp456.41MB
  54. 19. Appendix FAQ/5. Proof that using Jupyter Notebook is the same as not using it.mp477.94MB
  55. 19. Appendix FAQ/6. How to Succeed in this Course (Long Version).mp438.92MB
  56. 19. Appendix FAQ/7. Is Theano Dead.mp444.38MB
  57. 19. Appendix FAQ/8. What order should I take your courses in (part 1).mp488.14MB
  58. 19. Appendix FAQ/9. What order should I take your courses in (part 2).mp4122.64MB
  59. 2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.mp465.17MB
  60. 2. Google Colab/2. Tensorflow 2.0 in Google Colab.mp451.14MB
  61. 2. Google Colab/3. Uploading your own data to Google Colab.mp489.09MB
  62. 2. Google Colab/4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp443.85MB
  63. 3. Machine Learning and Neurons/1. What is Machine Learning.mp473.16MB
  64. 3. Machine Learning and Neurons/2. Code Preparation (Classification Theory).mp468.5MB
  65. 3. Machine Learning and Neurons/3. Classification Notebook.mp466.3MB
  66. 3. Machine Learning and Neurons/4. Code Preparation (Regression Theory).mp431.33MB
  67. 3. Machine Learning and Neurons/5. Regression Notebook.mp471.75MB
  68. 3. Machine Learning and Neurons/6. The Neuron.mp449.43MB
  69. 3. Machine Learning and Neurons/7. How does a model learn.mp455.02MB
  70. 3. Machine Learning and Neurons/8. Making Predictions.mp441.95MB
  71. 3. Machine Learning and Neurons/9. Saving and Loading a Model.mp435.29MB
  72. 4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.mp432.54MB
  73. 4. Feedforward Artificial Neural Networks/2. Forward Propagation.mp449.32MB
  74. 4. Feedforward Artificial Neural Networks/3. The Geometrical Picture.mp456.45MB
  75. 4. Feedforward Artificial Neural Networks/4. Activation Functions.mp492.17MB
  76. 4. Feedforward Artificial Neural Networks/5. Multiclass Classification.mp446.88MB
  77. 4. Feedforward Artificial Neural Networks/6. How to Represent Images.mp480.85MB
  78. 4. Feedforward Artificial Neural Networks/7. Code Preparation (ANN).mp456.16MB
  79. 4. Feedforward Artificial Neural Networks/8. ANN for Image Classification.mp458.35MB
  80. 4. Feedforward Artificial Neural Networks/9. ANN for Regression.mp483.95MB
  81. 5. Convolutional Neural Networks/1. What is Convolution (part 1).mp483.58MB
  82. 5. Convolutional Neural Networks/10. Batch Normalization.mp423.46MB
  83. 5. Convolutional Neural Networks/11. Improving CIFAR-10 Results.mp486.35MB
  84. 5. Convolutional Neural Networks/2. What is Convolution (part 2).mp425.15MB
  85. 5. Convolutional Neural Networks/3. What is Convolution (part 3).mp427.64MB
  86. 5. Convolutional Neural Networks/4. Convolution on Color Images.mp477.02MB
  87. 5. Convolutional Neural Networks/5. CNN Architecture.mp490.94MB
  88. 5. Convolutional Neural Networks/6. CNN Code Preparation.mp486.3MB
  89. 5. Convolutional Neural Networks/7. CNN for Fashion MNIST.mp451.65MB
  90. 5. Convolutional Neural Networks/8. CNN for CIFAR-10.mp434.8MB
  91. 5. Convolutional Neural Networks/9. Data Augmentation.mp439.16MB
  92. 6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.mp4103.19MB
  93. 6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).mp453.58MB
  94. 6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.mp477.65MB
  95. 6. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.mp4143.12MB
  96. 6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).mp431.5MB
  97. 6. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).mp427.44MB
  98. 6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).mp480.04MB
  99. 6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).mp438.19MB
  100. 6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).mp476.74MB
  101. 6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.mp447.24MB
  102. 6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.mp487.68MB
  103. 6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.mp418.27MB
  104. 6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.mp492.04MB
  105. 6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.mp420.42MB
  106. 6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.mp487.22MB
  107. 6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.mp464.34MB
  108. 6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).mp476.09MB
  109. 7. Natural Language Processing (NLP)/1. Embeddings.mp457.96MB
  110. 7. Natural Language Processing (NLP)/2. Code Preparation (NLP).mp462.93MB
  111. 7. Natural Language Processing (NLP)/3. Text Preprocessing.mp436.14MB
  112. 7. Natural Language Processing (NLP)/4. Text Classification with LSTMs.mp460.56MB
  113. 7. Natural Language Processing (NLP)/5. CNNs for Text.mp440.86MB
  114. 7. Natural Language Processing (NLP)/6. Text Classification with CNNs.mp446.4MB
  115. 8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.mp468.73MB
  116. 8. Recommender Systems/2. Recommender Systems with Deep Learning Code.mp458.79MB
  117. 9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.mp455.15MB
  118. 9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp431.55MB
  119. 9. Transfer Learning for Computer Vision/3. Large Datasets and Data Generators.mp436.56MB
  120. 9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.mp420.62MB
  121. 9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).mp466.57MB
  122. 9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).mp446.06MB
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