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[] Coursera - Introduction to Deep Learning

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文件列表

  1. 011.Modern CNNs/021. Training tips and tricks for deep CNNs.mp4 58MB
  2. 019.Applications of RNNs/039. Practical use cases for RNNs.mp4 56MB
  3. 015.Word Embeddings/030. Word embeddings.mp4 48MB
  4. 018.Modern RNNs/038. Modern RNNs LSTM and GRU.mp4 48MB
  5. 007.Matrix derivatives/013. Efficient MLP implementation.mp4 47MB
  6. 006.The simplest neural network MLP/010. Multilayer perceptron (MLP).mp4 45MB
  7. 003.Linear model as the simplest neural network/004. Linear classification.mp4 43MB
  8. 010.Introduction to CNN/020. Our first CNN architecture.mp4 43MB
  9. 016.Generative Adversarial Networks/033. Applications of adversarial approach.mp4 42MB
  10. 010.Introduction to CNN/019. Motivation for convolutional layers.mp4 41MB
  11. 014.More Autoencoders/027. Autoencoder applications.mp4 41MB
  12. 008.TensorFlow framework/015. What is TensorFlow.mp4 39MB
  13. 008.TensorFlow framework/016. Our first model in TensorFlow.mp4 37MB
  14. 015.Word Embeddings/029. Natural language processing primer.mp4 37MB
  15. 005.Stochastic methods for optimization/009. Gradient descent extensions.mp4 37MB
  16. 016.Generative Adversarial Networks/032. Generative Adversarial Networks.mp4 36MB
  17. 003.Linear model as the simplest neural network/003. Linear regression.mp4 36MB
  18. 017.Introduction to RNN/035. Simple RNN and Backpropagation.mp4 35MB
  19. 018.Modern RNNs/037. Dealing with vanishing and exploding gradients.mp4 35MB
  20. 011.Modern CNNs/022. Overview of modern CNN architectures.mp4 32MB
  21. 006.The simplest neural network MLP/012. Backpropagation.mp4 32MB
  22. 012.Applications of CNNs/024. A glimpse of other Computer Vision tasks.mp4 31MB
  23. 017.Introduction to RNN/034. Motivation for recurrent layers.mp4 30MB
  24. 009.Philosophy of deep learning/017. What Deep Learning is and is not.mp4 29MB
  25. 014.More Autoencoders/028. Autoencoder applications image generation, data visualization & more.mp4 28MB
  26. 016.Generative Adversarial Networks/031. Generative models 101.mp4 27MB
  27. 006.The simplest neural network MLP/011. Chain rule.mp4 27MB
  28. 004.Regularization in machine learning/006. Overfitting problem and model validation.mp4 26MB
  29. 018.Modern RNNs/036. The training of RNNs is not that easy.mp4 26MB
  30. 009.Philosophy of deep learning/018. Deep learning as a language.mp4 25MB
  31. 013.Intro to Unsupervised Learning/025. Unsupervised learning what it is and why bother.mp4 24MB
  32. 013.Intro to Unsupervised Learning/026. Autoencoders 101.mp4 22MB
  33. 002.Course intro/002. Course intro.mp4 22MB
  34. 007.Matrix derivatives/014. Other matrix derivatives.mp4 21MB
  35. 005.Stochastic methods for optimization/008. Stochastic gradient descent.mp4 21MB
  36. 004.Regularization in machine learning/007. Model regularization.mp4 20MB
  37. 012.Applications of CNNs/023. Learning new tasks with pre-trained CNNs.mp4 19MB
  38. 003.Linear model as the simplest neural network/005. Gradient descent.mp4 19MB
  39. 001.Specialization Promo/001. Welcome to AML specialization!.mp4 14MB
  40. 015.Word Embeddings/030. Word embeddings.srt 20KB
  41. 019.Applications of RNNs/039. Practical use cases for RNNs.srt 19KB
  42. 006.The simplest neural network MLP/010. Multilayer perceptron (MLP).srt 19KB
  43. 011.Modern CNNs/021. Training tips and tricks for deep CNNs.srt 18KB
  44. 018.Modern RNNs/038. Modern RNNs LSTM and GRU.srt 17KB
  45. 007.Matrix derivatives/013. Efficient MLP implementation.srt 17KB
  46. 003.Linear model as the simplest neural network/004. Linear classification.srt 16KB
  47. 010.Introduction to CNN/019. Motivation for convolutional layers.srt 16KB
  48. 016.Generative Adversarial Networks/033. Applications of adversarial approach.srt 16KB
  49. 016.Generative Adversarial Networks/032. Generative Adversarial Networks.srt 15KB
  50. 015.Word Embeddings/029. Natural language processing primer.srt 15KB
  51. 014.More Autoencoders/027. Autoencoder applications.srt 15KB
  52. 008.TensorFlow framework/015. What is TensorFlow.srt 15KB
  53. 009.Philosophy of deep learning/017. What Deep Learning is and is not.srt 14KB
  54. 008.TensorFlow framework/016. Our first model in TensorFlow.srt 14KB
  55. 018.Modern RNNs/037. Dealing with vanishing and exploding gradients.srt 14KB
  56. 005.Stochastic methods for optimization/009. Gradient descent extensions.srt 13KB
  57. 003.Linear model as the simplest neural network/003. Linear regression.srt 13KB
  58. 010.Introduction to CNN/020. Our first CNN architecture.srt 13KB
  59. 017.Introduction to RNN/035. Simple RNN and Backpropagation.srt 13KB
  60. 009.Philosophy of deep learning/018. Deep learning as a language.srt 12KB
  61. 006.The simplest neural network MLP/012. Backpropagation.srt 11KB
  62. 016.Generative Adversarial Networks/031. Generative models 101.srt 11KB
  63. 012.Applications of CNNs/024. A glimpse of other Computer Vision tasks.srt 11KB
  64. 014.More Autoencoders/028. Autoencoder applications image generation, data visualization & more.srt 11KB
  65. 017.Introduction to RNN/034. Motivation for recurrent layers.srt 11KB
  66. 018.Modern RNNs/036. The training of RNNs is not that easy.srt 10KB
  67. 006.The simplest neural network MLP/011. Chain rule.srt 10KB
  68. 004.Regularization in machine learning/006. Overfitting problem and model validation.srt 10KB
  69. 013.Intro to Unsupervised Learning/025. Unsupervised learning what it is and why bother.srt 10KB
  70. 011.Modern CNNs/022. Overview of modern CNN architectures.srt 10KB
  71. 002.Course intro/002. Course intro.srt 9KB
  72. 007.Matrix derivatives/014. Other matrix derivatives.srt 9KB
  73. 013.Intro to Unsupervised Learning/026. Autoencoders 101.srt 8KB
  74. 005.Stochastic methods for optimization/008. Stochastic gradient descent.srt 8KB
  75. 004.Regularization in machine learning/007. Model regularization.srt 7KB
  76. 003.Linear model as the simplest neural network/005. Gradient descent.srt 7KB
  77. 012.Applications of CNNs/023. Learning new tasks with pre-trained CNNs.srt 7KB
  78. 001.Specialization Promo/001. Welcome to AML specialization!.srt 5KB
  79. [FCS Forum].url 133B
  80. [FreeCourseSite.com].url 127B
  81. [CourseClub.NET].url 123B