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365 Data Science - Deep Learning with TensorFlow [CoursesGhar]

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文件列表

  1. 13. Business case/4. Preprocessing the data.mp4 79MB
  2. 14. Conclusion/3. An overview of CNNs.mp4 72MB
  3. 6. Deep nets overview/3. Really understand deep nets.mp4 58MB
  4. 13. Business case/10. Homework.mp4 57MB
  5. 13. Business case/5. Creating the batching class.mp4 57MB
  6. 2. Neural networks Intro/11. One-parameter gradient descent.mp4 56MB
  7. 12. Deeper example/9. Commenting on the results.mp4 53MB
  8. 6. Deep nets overview/7. Backpropagation.mp4 53MB
  9. 13. Business case/1. The dataset.mp4 53MB
  10. 2. Neural networks Intro/12. N-parameter gradient descent.mp4 50MB
  11. 1. Introduction/2. What does the course cover.mp4 49MB
  12. 14. Conclusion/1. Summary.mp4 49MB
  13. 4. Minimal example/4. Training the model.mp4 47MB
  14. 13. Business case/6. Outlining the model.mp4 46MB
  15. 12. Deeper example/4. MNIST - Outlining the model.mp4 45MB
  16. 13. Business case/3. Balancing a dataset.mp4 45MB
  17. 5. Introduction to TensorFlow/1. TensorFlow outline.mp4 45MB
  18. 2. Neural networks Intro/1. Introduction to neural networks.mp4 43MB
  19. 2. Neural networks Intro/6. The linear model. Multiple inputs and multiple outputs.mp4 42MB
  20. 14. Conclusion/5. Non-NN approaches.mp4 42MB
  21. 2. Neural networks Intro/3. Types of machine learning.mp4 41MB
  22. 11. Preprocessing/3. Standardization.mp4 40MB
  23. 12. Deeper example/6. Accuracy of a model.mp4 40MB
  24. 6. Deep nets overview/4. Why do we need non-linearities.mp4 38MB
  25. 1. Introduction/1. Welcome to Machine Learning.mp4 38MB
  26. 8. Overfitting/3. Train vs validation.mp4 38MB
  27. 3. Setting up the environment/3. Installing Anaconda.mp4 37MB
  28. 10. Optimizers/4. Learning rate schedules.mp4 37MB
  29. 3. Setting up the environment/2. Why Python and why Jupyter.mp4 35MB
  30. 10. Optimizers/1. SGD_Batching.mp4 34MB
  31. 8. Overfitting/1. Underfitting and overfitting.mp4 34MB
  32. 12. Deeper example/8. Optimization.mp4 34MB
  33. 2. Neural networks Intro/10. Cross-entropy loss.mp4 33MB
  34. 6. Deep nets overview/2. What is a deep net.mp4 33MB
  35. 8. Overfitting/2. Underfitting and overfitting. A classification example.mp4 32MB
  36. 11. Preprocessing/5. One-hot vs binary.mp4 32MB
  37. 8. Overfitting/4. Train vs validation vs test.mp4 31MB
  38. 12. Deeper example/2. How to tackle the MNIST dataset.mp4 31MB
  39. 5. Introduction to TensorFlow/6. Output.mp4 30MB
  40. 10. Optimizers/6. Adaptive learning schedules.mp4 30MB
  41. 6. Deep nets overview/5. Activation functions.mp4 29MB
  42. 10. Optimizers/7. Adaptive moment estimation.mp4 29MB
  43. 8. Overfitting/6. Early stopping - motivation and types.mp4 28MB
  44. 5. Introduction to TensorFlow/4. Laying down the model.mp4 28MB
  45. 13. Business case/7. Optimizing the algorithm.mp4 27MB
  46. 14. Conclusion/4. An overview of RNNs.mp4 27MB
  47. 2. Neural networks Intro/2. Training the model.mp4 27MB
  48. 9. Initialization/1. Initializaiton.mp4 26MB
  49. 2. Neural networks Intro/4. The linear model.mp4 26MB
  50. 8. Overfitting/5. N-fold cross validation.mp4 26MB
  51. 11. Preprocessing/1. Preprocessing.mp4 26MB
  52. 6. Deep nets overview/6. Softmax activation.mp4 25MB
  53. 6. Deep nets overview/8. Backpropagation - intuition.mp4 24MB
  54. 4. Minimal example/2. Generating the data (optional).mp4 24MB
  55. 2. Neural networks Intro/5. The linear model. Multiple inputs..mp4 24MB
  56. 2. Neural networks Intro/7. Graphical representation.mp4 22MB
  57. 5. Introduction to TensorFlow/5. Laying down the optimizers.mp4 21MB
  58. 2. Neural networks Intro/9. L2-norm loss.mp4 21MB
  59. 3. Setting up the environment/5. Jupyter Dashboard - Part 2.mp4 21MB
  60. 4. Minimal example/3. Initializing the variables.mp4 20MB
  61. 13. Business case/8. Running the code.mp4 20MB
  62. 5. Introduction to TensorFlow/2. TensorFlow introduction.mp4 19MB
  63. 9. Initialization/3. Xavier_s initialization.mp4 19MB
  64. 10. Optimizers/3. Momentum.mp4 19MB
  65. 12. Deeper example/5. MNIST - Declaring the loss.mp4 19MB
  66. 11. Preprocessing/4. Dealing with categorical data.mp4 18MB
  67. 2. Neural networks Intro/8. The objective function.mp4 18MB
  68. 14. Conclusion/2. Whats more out there.mp4 18MB
  69. 6. Deep nets overview/1. The layer.mp4 16MB
  70. 10. Optimizers/2. Local minima pitfalls.mp4 14MB
  71. 3. Setting up the environment/6. Installing the TensorFlow package.mp4 14MB
  72. 4. Minimal example/1. Outline.mp4 14MB
  73. 5. Introduction to TensorFlow/3. Types of file formats used in TensorFlow.mp4 13MB
  74. 12. Deeper example/3. MNIST - Importing libraries and data.mp4 13MB
  75. 9. Initialization/2. Types of simple initializations.mp4 12MB
  76. 13. Business case/2. Outlining the solution.mp4 12MB
  77. 11. Preprocessing/2. Basic preprocessing.mp4 11MB
  78. 10. Optimizers/5. Learning rate schedules. A picture.mp4 11MB
  79. 3. Setting up the environment/4. Jupyter Dashboard - Part 1.mp4 10MB
  80. 12. Deeper example/7. Early stopping and batching preparation.mp4 10MB
  81. 13. Business case/9. Test.mp4 9MB
  82. 12. Deeper example/1. MNIST dataset.mp4 9MB
  83. 3. Setting up the environment/1. Setting up the environment - Do not skip, please!.mp4 8MB
  84. Uploaded by [Coursesghar.com].txt 1KB
  85. !! IMPORTANT Note !!.txt 298B
  86. !!! Please Support !!! [CoursesGhar.Com].txt 197B
  87. Join Our Telegram Group For More Updates !!!.url 138B
  88. 00. Websites You May Like/A1movies.com.pk.url 116B
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  90. Visit coursesghar.com for more awesome tutorials.url 114B