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[Tutorialsplanet.NET] Udemy - PyTorch Deep Learning and Artificial Intelligence

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Tutorialsplanet NET Udemy - PyTorch Deep Learning and Artificial Intelligence

文件列表
  1. 1. Introduction/1. Welcome.mp435.72MB
  2. 1. Introduction/2. Overview and Outline.mp479.67MB
  3. 1. Introduction/3. Where to get the Code.mp429.46MB
  4. 10. GANs (Generative Adversarial Networks)/1. GAN Theory.mp492.11MB
  5. 10. GANs (Generative Adversarial Networks)/2. GAN Code Preparation.mp428.08MB
  6. 10. GANs (Generative Adversarial Networks)/3. GAN Code.mp461.37MB
  7. 10. GANs (Generative Adversarial Networks)/4. Exercise DCGAN (Deep Convolutional GAN).mp415.31MB
  8. 11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.mp440.66MB
  9. 11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.mp441.46MB
  10. 11. Deep Reinforcement Learning (Theory)/11. Q-Learning.mp466.78MB
  11. 11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).mp460.24MB
  12. 11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).mp452.22MB
  13. 11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.mp440.25MB
  14. 11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.mp4104.93MB
  15. 11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.mp444.13MB
  16. 11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).mp450.51MB
  17. 11. Deep Reinforcement Learning (Theory)/5. The Return.mp423.42MB
  18. 11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.mp447.72MB
  19. 11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.mp431.67MB
  20. 11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp442.92MB
  21. 11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp457.02MB
  22. 12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.mp428.82MB
  23. 12. Stock Trading Project with Deep Reinforcement Learning/10. Exercise Personalized Stock Trading Bot.mp47.86MB
  24. 12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.mp455.69MB
  25. 12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.mp424.97MB
  26. 12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.mp426.87MB
  27. 12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.mp466.33MB
  28. 12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.mp469.98MB
  29. 12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.mp458.59MB
  30. 12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.mp452.61MB
  31. 12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.mp417.22MB
  32. 13. VIP Uncertainty Estimation/1. Custom Loss and Estimating Prediction Uncertainty.mp443.55MB
  33. 13. VIP Uncertainty Estimation/2. Estimating Prediction Uncertainty Code.mp442.75MB
  34. 14. VIP Facial Recognition/1. Facial Recognition Section Introduction.mp424.31MB
  35. 14. VIP Facial Recognition/10. Facial Recognition Section Summary.mp418.32MB
  36. 14. VIP Facial Recognition/2. Siamese Networks.mp450.51MB
  37. 14. VIP Facial Recognition/3. Code Outline.mp423.85MB
  38. 14. VIP Facial Recognition/4. Loading in the data.mp435.06MB
  39. 14. VIP Facial Recognition/5. Splitting the data into train and test.mp426.29MB
  40. 14. VIP Facial Recognition/6. Converting the data into pairs.mp430.38MB
  41. 14. VIP Facial Recognition/7. Generating Generators.mp432.44MB
  42. 14. VIP Facial Recognition/8. Creating the model and loss.mp429.38MB
  43. 14. VIP Facial Recognition/9. Accuracy and imbalanced classes.mp451.1MB
  44. 15. In-Depth Loss Functions/1. Mean Squared Error.mp433.79MB
  45. 15. In-Depth Loss Functions/2. Binary Cross Entropy.mp423.69MB
  46. 15. In-Depth Loss Functions/3. Categorical Cross Entropy.mp431.74MB
  47. 16. In-Depth Gradient Descent/1. Gradient Descent.mp434.9MB
  48. 16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.mp422.99MB
  49. 16. In-Depth Gradient Descent/3. Momentum.mp434.25MB
  50. 16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.mp434.85MB
  51. 16. In-Depth Gradient Descent/5. Adam (pt 1).mp455.17MB
  52. 16. In-Depth Gradient Descent/6. Adam (pt 2).mp452.78MB
  53. 18. Setting up your Environment (FAQ by Student Request)/1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4150.67MB
  54. 18. Setting up your Environment (FAQ by Student Request)/2. Windows-Focused Environment Setup 2018.mp4180.68MB
  55. 18. Setting up your Environment (FAQ by Student Request)/3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4167.33MB
  56. 19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code Yourself (part 1).mp471.86MB
  57. 19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code Yourself (part 2).mp449.15MB
  58. 19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. Proof that using Jupyter Notebook is the same as not using it.mp469.49MB
  59. 2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.mp460.46MB
  60. 2. Google Colab/2. Uploading your own data to Google Colab.mp490.54MB
  61. 2. Google Colab/3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp444.38MB
  62. 20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).mp435.26MB
  63. 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.mp40B
  64. 20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp479.59MB
  65. 20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4108.22MB
  66. 21. Appendix FAQ Finale/1. What is the Appendix.mp416.38MB
  67. 21. Appendix FAQ Finale/2. BONUS Where to get discount coupons and FREE deep learning material.mp437.81MB
  68. 3. Machine Learning and Neurons/1. What is Machine Learning.mp470.59MB
  69. 3. Machine Learning and Neurons/10. Classification Notebook.mp478.29MB
  70. 3. Machine Learning and Neurons/11. Exercise Predicting Diabetes Onset.mp412.56MB
  71. 3. Machine Learning and Neurons/12. Saving and Loading a Model.mp428.83MB
  72. 3. Machine Learning and Neurons/13. A Short Neuroscience Primer.mp444.65MB
  73. 3. Machine Learning and Neurons/14. How does a model learn.mp450.07MB
  74. 3. Machine Learning and Neurons/15. Model With Logits.mp427.32MB
  75. 3. Machine Learning and Neurons/16. Train Sets vs. Validation Sets vs. Test Sets.mp452.15MB
  76. 3. Machine Learning and Neurons/17. Suggestion Box.mp416.1MB
  77. 3. Machine Learning and Neurons/2. Regression Basics.mp473.02MB
  78. 3. Machine Learning and Neurons/3. Regression Code Preparation.mp445.54MB
  79. 3. Machine Learning and Neurons/4. Regression Notebook.mp471.93MB
  80. 3. Machine Learning and Neurons/5. Moore's Law.mp430.63MB
  81. 3. Machine Learning and Neurons/6. Moore's Law Notebook.mp478.91MB
  82. 3. Machine Learning and Neurons/7. Exercise Real Estate Predictions.mp45.58MB
  83. 3. Machine Learning and Neurons/8. Linear Classification Basics.mp467.22MB
  84. 3. Machine Learning and Neurons/9. Classification Code Preparation.mp426.54MB
  85. 4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.mp433.48MB
  86. 4. Feedforward Artificial Neural Networks/10. Exercise E. Coli Protein Localization Sites.mp410.46MB
  87. 4. Feedforward Artificial Neural Networks/2. Forward Propagation.mp447.1MB
  88. 4. Feedforward Artificial Neural Networks/3. The Geometrical Picture.mp456.42MB
  89. 4. Feedforward Artificial Neural Networks/4. Activation Functions.mp489.25MB
  90. 4. Feedforward Artificial Neural Networks/5. Multiclass Classification.mp448.69MB
  91. 4. Feedforward Artificial Neural Networks/6. How to Represent Images.mp475.43MB
  92. 4. Feedforward Artificial Neural Networks/7. Code Preparation (ANN).mp466.12MB
  93. 4. Feedforward Artificial Neural Networks/8. ANN for Image Classification.mp4106.32MB
  94. 4. Feedforward Artificial Neural Networks/9. ANN for Regression.mp480.19MB
  95. 5. Convolutional Neural Networks/1. What is Convolution (part 1).mp479.64MB
  96. 5. Convolutional Neural Networks/10. CNN for CIFAR-10.mp456.71MB
  97. 5. Convolutional Neural Networks/11. Data Augmentation.mp444.52MB
  98. 5. Convolutional Neural Networks/12. Batch Normalization.mp423.44MB
  99. 5. Convolutional Neural Networks/13. Improving CIFAR-10 Results.mp477.42MB
  100. 5. Convolutional Neural Networks/14. Exercise Facial Expression Recognition.mp48.25MB
  101. 5. Convolutional Neural Networks/2. What is Convolution (part 2).mp424.49MB
  102. 5. Convolutional Neural Networks/3. What is Convolution (part 3).mp428.7MB
  103. 5. Convolutional Neural Networks/4. Convolution on Color Images.mp476.38MB
  104. 5. Convolutional Neural Networks/5. CNN Architecture.mp489.54MB
  105. 5. Convolutional Neural Networks/6. CNN Code Preparation (part 1).mp476.73MB
  106. 5. Convolutional Neural Networks/7. CNN Code Preparation (part 2).mp436.71MB
  107. 5. Convolutional Neural Networks/8. CNN Code Preparation (part 3).mp433.69MB
  108. 5. Convolutional Neural Networks/9. CNN for Fashion MNIST.mp474.45MB
  109. 6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.mp4114.29MB
  110. 6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).mp450.38MB
  111. 6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.mp486.68MB
  112. 6. Recurrent Neural Networks, Time Series, and Sequence Data/12. RNN for Image Classification (Theory).mp432.27MB
  113. 6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Code).mp420.53MB
  114. 6. Recurrent Neural Networks, Time Series, and Sequence Data/14. Stock Return Predictions using LSTMs (pt 1).mp477.82MB
  115. 6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 2).mp443.22MB
  116. 6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 3).mp471.07MB
  117. 6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Other Ways to Forecast.mp428.28MB
  118. 6. Recurrent Neural Networks, Time Series, and Sequence Data/18. Exercise More Forecasting.mp49.06MB
  119. 6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.mp448.5MB
  120. 6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.mp481.2MB
  121. 6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.mp417.92MB
  122. 6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.mp492.6MB
  123. 6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.mp455.31MB
  124. 6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.mp471.85MB
  125. 6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.mp456.4MB
  126. 6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).mp476.06MB
  127. 7. Natural Language Processing (NLP)/1. Embeddings.mp459.98MB
  128. 7. Natural Language Processing (NLP)/10. Exercise Sentiment Analysis.mp49.12MB
  129. 7. Natural Language Processing (NLP)/2. Neural Networks with Embeddings.mp415.63MB
  130. 7. Natural Language Processing (NLP)/3. Text Preprocessing (pt 1).mp452.3MB
  131. 7. Natural Language Processing (NLP)/4. Text Preprocessing (pt 2).mp444.41MB
  132. 7. Natural Language Processing (NLP)/5. Text Preprocessing (pt 3).mp447.73MB
  133. 7. Natural Language Processing (NLP)/6. Text Classification with LSTMs.mp465.05MB
  134. 7. Natural Language Processing (NLP)/7. CNNs for Text.mp458.54MB
  135. 7. Natural Language Processing (NLP)/8. Text Classification with CNNs.mp439.33MB
  136. 7. Natural Language Processing (NLP)/9. VIP Making Predictions with a Trained NLP Model.mp448.81MB
  137. 8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.mp464.74MB
  138. 8. Recommender Systems/2. Recommender Systems with Deep Learning Code Preparation.mp440.1MB
  139. 8. Recommender Systems/3. Recommender Systems with Deep Learning Code (pt 1).mp469.57MB
  140. 8. Recommender Systems/4. Recommender Systems with Deep Learning Code (pt 2).mp476.88MB
  141. 8. Recommender Systems/5. VIP Making Predictions with a Trained Recommender Model.mp432.75MB
  142. 8. Recommender Systems/6. Exercise Book Recommendations.mp44.08MB
  143. 9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.mp458.19MB
  144. 9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp421.67MB
  145. 9. Transfer Learning for Computer Vision/3. Large Datasets.mp441.26MB
  146. 9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.mp421.8MB
  147. 9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).mp477.78MB
  148. 9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).mp456.32MB
  149. 9. Transfer Learning for Computer Vision/7. Exercise Transfer Learning.mp46.95MB
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