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[] - Udemy - Applied Deep Learning Build a Chatbot - Theory, Application

  • 收录时间:2020-05-17 06:23:25
  • 文件大小:3GB
  • 下载次数:49
  • 最近下载:2021-01-11 07:31:05
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文件列表

  1. 6. Practical Part 4 - Building the Model/2. Defining the Encoder.mp4 242MB
  2. 6. Practical Part 4 - Building the Model/6. Designing the Decoder Part 2.mp4 160MB
  3. 6. Practical Part 4 - Building the Model/4. Designing the Attention Model.mp4 151MB
  4. 7. Practical Part 5 - Training the Model/3. Visualize Training Part 1.mp4 132MB
  5. 6. Practical Part 4 - Building the Model/5. Designing the Decoder Part 1.mp4 127MB
  6. 7. Practical Part 5 - Training the Model/5. Training.mp4 123MB
  7. 7. Practical Part 5 - Training the Model/4. Visualize Training Part 2.mp4 113MB
  8. 5. Practical Part 3 - Data Preperation/5. Preparing the Data for Model Part 4.mp4 104MB
  9. 4. Practical Part 2 - Processing the Dataset/8. Processing the Text Part 2.mp4 96MB
  10. 4. Practical Part 2 - Processing the Dataset/7. Processing the Text.mp4 96MB
  11. 4. Practical Part 2 - Processing the Dataset/6. Processing the Words.mp4 89MB
  12. 5. Practical Part 3 - Data Preperation/4. Preparing the Data for Model Part 3.mp4 89MB
  13. 5. Practical Part 3 - Data Preperation/1. Preparing the Data for Model Part 1.mp4 87MB
  14. 4. Practical Part 2 - Processing the Dataset/10. Getting Rid of Rare Words.mp4 83MB
  15. 4. Practical Part 2 - Processing the Dataset/1. The Dataset.mp4 82MB
  16. 1. Theory Part 1 - RNNs and LSTMs/2. Introduction to RNNs Part 1.mp4 79MB
  17. 3. Practical Part 1 - Introduction to PyTorch/2. Torch Tensors Part 1.mp4 78MB
  18. 4. Practical Part 2 - Processing the Dataset/4. Processing the Dataset Part 3.mp4 76MB
  19. 4. Practical Part 2 - Processing the Dataset/3. Processing the Data Part 2.mp4 74MB
  20. 3. Practical Part 1 - Introduction to PyTorch/1. Installing PyTorch and an Introduction.mp4 73MB
  21. 1. Theory Part 1 - RNNs and LSTMs/5. Playing with the Activations.mp4 72MB
  22. 4. Practical Part 2 - Processing the Dataset/2. Processing the Dataset Part 1.mp4 68MB
  23. 3. Practical Part 1 - Introduction to PyTorch/3. Torch Tensors Part 2.vtt 68MB
  24. 3. Practical Part 1 - Introduction to PyTorch/3. Torch Tensors Part 2.mp4 68MB
  25. 1. Theory Part 1 - RNNs and LSTMs/3. Introduction to RNNs Part 2.mp4 68MB
  26. 7. Practical Part 5 - Training the Model/1. Creating the Loss Function.mp4 67MB
  27. 1. Theory Part 1 - RNNs and LSTMs/6. LSTMs.mp4 67MB
  28. 4. Practical Part 2 - Processing the Dataset/9. Filtering the Text.mp4 63MB
  29. 6. Practical Part 4 - Building the Model/3. Understanding Pack Padded Sequence.mp4 59MB
  30. 4. Practical Part 2 - Processing the Dataset/5. Processing the Dataset Part 4.mp4 56MB
  31. 5. Practical Part 3 - Data Preperation/3. Preparing the Data for Model Part 2.mp4 55MB
  32. 6. Practical Part 4 - Building the Model/1. Understanding the Encoder.mp4 53MB
  33. 7. Practical Part 5 - Training the Model/2. Teacher Forcing.mp4 49MB
  34. 5. Practical Part 3 - Data Preperation/2. Understanding the zip function.mp4 45MB
  35. 2. Theory Part 2 - Sequence Modeling/1. Sequence-to-Sequence Models.mp4 44MB
  36. 2. Theory Part 2 - Sequence Modeling/2. Attention Mechanisms.mp4 40MB
  37. 2. Theory Part 2 - Sequence Modeling/3. How Attention Mechanisms Work.mp4 37MB
  38. 1. Theory Part 1 - RNNs and LSTMs/7. LSTM Variants.mp4 23MB
  39. 1. Theory Part 1 - RNNs and LSTMs/8. LSTM Step-by-Step Example Walktrough.mp4 23MB
  40. 6. Practical Part 4 - Building the Model/2. Defining the Encoder.vtt 28KB
  41. 6. Practical Part 4 - Building the Model/6. Designing the Decoder Part 2.vtt 20KB
  42. 6. Practical Part 4 - Building the Model/4. Designing the Attention Model.vtt 18KB
  43. 6. Practical Part 4 - Building the Model/5. Designing the Decoder Part 1.vtt 17KB
  44. 7. Practical Part 5 - Training the Model/3. Visualize Training Part 1.vtt 16KB
  45. 5. Practical Part 3 - Data Preperation/5. Preparing the Data for Model Part 4.vtt 15KB
  46. 7. Practical Part 5 - Training the Model/5. Training.vtt 14KB
  47. 5. Practical Part 3 - Data Preperation/1. Preparing the Data for Model Part 1.vtt 14KB
  48. 4. Practical Part 2 - Processing the Dataset/6. Processing the Words.vtt 14KB
  49. 3. Practical Part 1 - Introduction to PyTorch/2. Torch Tensors Part 1.vtt 13KB
  50. 7. Practical Part 5 - Training the Model/4. Visualize Training Part 2.vtt 13KB
  51. 1. Theory Part 1 - RNNs and LSTMs/2. Introduction to RNNs Part 1.vtt 13KB
  52. 3. Practical Part 1 - Introduction to PyTorch/1. Installing PyTorch and an Introduction.vtt 13KB
  53. 5. Practical Part 3 - Data Preperation/4. Preparing the Data for Model Part 3.vtt 12KB
  54. 4. Practical Part 2 - Processing the Dataset/1. The Dataset.vtt 11KB
  55. 4. Practical Part 2 - Processing the Dataset/7. Processing the Text.vtt 11KB
  56. 1. Theory Part 1 - RNNs and LSTMs/5. Playing with the Activations.vtt 11KB
  57. 1. Theory Part 1 - RNNs and LSTMs/6. LSTMs.vtt 11KB
  58. 4. Practical Part 2 - Processing the Dataset/10. Getting Rid of Rare Words.vtt 10KB
  59. 2. Theory Part 2 - Sequence Modeling/1. Sequence-to-Sequence Models.vtt 10KB
  60. 4. Practical Part 2 - Processing the Dataset/4. Processing the Dataset Part 3.vtt 10KB
  61. 4. Practical Part 2 - Processing the Dataset/8. Processing the Text Part 2.vtt 10KB
  62. 1. Theory Part 1 - RNNs and LSTMs/3. Introduction to RNNs Part 2.vtt 10KB
  63. 5. Practical Part 3 - Data Preperation/3. Preparing the Data for Model Part 2.vtt 9KB
  64. 4. Practical Part 2 - Processing the Dataset/3. Processing the Data Part 2.vtt 9KB
  65. 6. Practical Part 4 - Building the Model/3. Understanding Pack Padded Sequence.vtt 8KB
  66. 2. Theory Part 2 - Sequence Modeling/3. How Attention Mechanisms Work.vtt 8KB
  67. 4. Practical Part 2 - Processing the Dataset/9. Filtering the Text.vtt 8KB
  68. 4. Practical Part 2 - Processing the Dataset/2. Processing the Dataset Part 1.vtt 8KB
  69. 4. Practical Part 2 - Processing the Dataset/5. Processing the Dataset Part 4.vtt 8KB
  70. 7. Practical Part 5 - Training the Model/1. Creating the Loss Function.vtt 7KB
  71. 7. Practical Part 5 - Training the Model/2. Teacher Forcing.vtt 7KB
  72. 2. Theory Part 2 - Sequence Modeling/2. Attention Mechanisms.vtt 7KB
  73. 6. Practical Part 4 - Building the Model/1. Understanding the Encoder.vtt 7KB
  74. 5. Practical Part 3 - Data Preperation/2. Understanding the zip function.vtt 6KB
  75. 1. Theory Part 1 - RNNs and LSTMs/8. LSTM Step-by-Step Example Walktrough.vtt 5KB
  76. 1. Theory Part 1 - RNNs and LSTMs/7. LSTM Variants.vtt 4KB
  77. 1. Theory Part 1 - RNNs and LSTMs/1. Before we Start.html 1KB
  78. 7. Practical Part 5 - Training the Model/6. Proceeding.html 384B
  79. 1. Theory Part 1 - RNNs and LSTMs/4. Test Your Understanding.html 160B
  80. PaidCoursesForFree.com.url 121B