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[] - Tensorflow Tutorial Hands-on AI development with Tensorflow

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文件列表

  1. 6. Live Projects/3. Cats vs Dogs.mp4 290MB
  2. 6. Live Projects/1. Fashion Clothing Recognition.mp4 157MB
  3. 5. Section 5/8. Facial Recognition using PCA.mp4 149MB
  4. 3. Section 3/4. Backpropagation.mp4 147MB
  5. 3. Section 3/6. Digit Classification.mp4 144MB
  6. 2. Section 2/3. Linear Regression - Theory.mp4 144MB
  7. 2. Section 2/1. Decision Trees - Theory.mp4 139MB
  8. 4. Section 4/2. Convolution in CNN (part1).mp4 137MB
  9. 4. Section 4/3. Convolution in CNN (part2).mp4 132MB
  10. 2. Section 2/6. Logistic Regression - Implementation.mp4 117MB
  11. 4. Section 4/4. Layers of CNN.mp4 114MB
  12. 1. Section 1/6. Intro to Machine Learning.mp4 112MB
  13. 1. Section 1/3. Graphs.mp4 112MB
  14. 3. Section 3/3. Complex Decision Boundaries.mp4 112MB
  15. 5. Section 5/7. Principal Component Analysis.mp4 110MB
  16. 4. Section 4/1. Introduction.mp4 109MB
  17. 2. Section 2/5. Logistic Regression - Theory.mp4 108MB
  18. 3. Section 3/2. Gates and Forward Propagation.mp4 104MB
  19. 2. Section 2/4. Linear Regression - Implementation.mp4 104MB
  20. 5. Section 5/2. K-Means Algorithm (Part 2).mp4 103MB
  21. 3. Section 3/1. Introduction.mp4 100MB
  22. 5. Section 5/1. K-Means Algorithm (Part 1).mp4 100MB
  23. 4. Section 4/6. Famous CNN Architectures.mp4 96MB
  24. 4. Section 4/5. Digit Classification.mp4 94MB
  25. 5. Section 5/4. K-Means ++.mp4 90MB
  26. 1. Section 1/1. What is TensorFlow 2 Preview.mp4 87MB
  27. 6. Live Projects/2. CIFAR 10 and CNN.mp4 84MB
  28. 6. Live Projects/4. Action Recognition.mp4 82MB
  29. 1. Section 1/4. Automatic Differentiation.mp4 78MB
  30. 2. Section 2/7. Overfitting and Regularization.mp4 76MB
  31. 1. Section 1/2. Basics of TensorFlow.mp4 74MB
  32. 1. Section 1/5. Keras and TensorFlow.mp4 67MB
  33. 5. Section 5/3. Centroid Initialization.mp4 66MB
  34. 3. Section 3/5. Gradient Descent Type and Softmax.mp4 63MB
  35. 2. Section 2/2. Decision Trees - Implementation.mp4 61MB
  36. 5. Section 5/6. K-Means Implementation.mp4 51MB
  37. 2. Section 2/8. Model Evaluation - Theory.mp4 46MB
  38. 2. Section 2/9. Model Evaluation - Implementation.mp4 33MB
  39. 5. Section 5/5. Number of Clusters.mp4 32MB
  40. 1. Section 1/7. Types of Supervised Learning.mp4 28MB
  41. 6. Live Projects/3. Cats vs Dogs.srt 40KB
  42. 6. Live Projects/1. Fashion Clothing Recognition.srt 22KB
  43. 3. Section 3/6. Digit Classification.srt 21KB
  44. 3. Section 3/4. Backpropagation.srt 21KB
  45. 5. Section 5/8. Facial Recognition using PCA.srt 19KB
  46. 2. Section 2/6. Logistic Regression - Implementation.srt 18KB
  47. 2. Section 2/3. Linear Regression - Theory.srt 17KB
  48. 2. Section 2/4. Linear Regression - Implementation.srt 17KB
  49. 4. Section 4/2. Convolution in CNN (part1).srt 17KB
  50. 2. Section 2/1. Decision Trees - Theory.srt 15KB
  51. 4. Section 4/3. Convolution in CNN (part2).srt 15KB
  52. 1. Section 1/3. Graphs.srt 15KB
  53. 5. Section 5/1. K-Means Algorithm (Part 1).srt 14KB
  54. 3. Section 3/3. Complex Decision Boundaries.srt 14KB
  55. 4. Section 4/4. Layers of CNN.srt 13KB
  56. 5. Section 5/2. K-Means Algorithm (Part 2).srt 13KB
  57. 6. Live Projects/2. CIFAR 10 and CNN.srt 13KB
  58. 5. Section 5/4. K-Means ++.srt 13KB
  59. 5. Section 5/7. Principal Component Analysis.srt 13KB
  60. 1. Section 1/1. What is TensorFlow 2 Preview.srt 13KB
  61. 4. Section 4/6. Famous CNN Architectures.srt 12KB
  62. 3. Section 3/1. Introduction.srt 12KB
  63. 3. Section 3/2. Gates and Forward Propagation.srt 12KB
  64. 4. Section 4/5. Digit Classification.srt 12KB
  65. 1. Section 1/6. Intro to Machine Learning.srt 12KB
  66. 2. Section 2/5. Logistic Regression - Theory.srt 12KB
  67. 1. Section 1/2. Basics of TensorFlow.srt 12KB
  68. 4. Section 4/1. Introduction.srt 12KB
  69. 1. Section 1/4. Automatic Differentiation.srt 11KB
  70. 6. Live Projects/4. Action Recognition.srt 10KB
  71. 2. Section 2/2. Decision Trees - Implementation.srt 10KB
  72. 2. Section 2/7. Overfitting and Regularization.srt 9KB
  73. 5. Section 5/3. Centroid Initialization.srt 9KB
  74. 1. Section 1/5. Keras and TensorFlow.srt 8KB
  75. 3. Section 3/5. Gradient Descent Type and Softmax.srt 8KB
  76. 5. Section 5/6. K-Means Implementation.srt 7KB
  77. 2. Section 2/8. Model Evaluation - Theory.srt 5KB
  78. 5. Section 5/5. Number of Clusters.srt 5KB
  79. 2. Section 2/9. Model Evaluation - Implementation.srt 4KB
  80. 1. Section 1/7. Types of Supervised Learning.srt 4KB
  81. READ_ME.txt 503B
  82. 1. Section 1/READ_ME.txt 503B
  83. 1. Section 1/CourseRecap-Click For More Courses!!.url 50B
  84. CourseRecap-Click For More Courses!!.url 50B