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Deep Learning - Artificial Neural Networks with Tensorflow

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文件列表

  1. Chapter 3 Feedforward Artificial Neural Networks/004. Activation Functions.mp4 62MB
  2. Chapter 3 Feedforward Artificial Neural Networks/009. ANN for Regression.mp4 56MB
  3. Chapter 2 Machine Learning and Neurons/001. What Is Machine Learning.mp4 54MB
  4. Chapter 3 Feedforward Artificial Neural Networks/006. How to Represent Images.mp4 50MB
  5. Chapter 2 Machine Learning and Neurons/002. Code Preparation (Classification Theory).mp4 48MB
  6. Chapter 2 Machine Learning and Neurons/005. Regression Notebook.mp4 48MB
  7. Chapter 2 Machine Learning and Neurons/003. Classification Notebook.mp4 45MB
  8. Chapter 3 Feedforward Artificial Neural Networks/007. Code Preparation (Artificial Neural Networks).mp4 42MB
  9. Chapter 5 In-Depth Gradient Descent/005. Adam Optimization (Part 1).mp4 42MB
  10. Chapter 5 In-Depth Gradient Descent/006. Adam Optimization (Part 2).mp4 40MB
  11. Chapter 3 Feedforward Artificial Neural Networks/008. ANN for Image Classification.mp4 40MB
  12. Chapter 3 Feedforward Artificial Neural Networks/003. The Geometrical Picture.mp4 39MB
  13. Chapter 2 Machine Learning and Neurons/007. How Does a Model Learn .mp4 39MB
  14. Chapter 2 Machine Learning and Neurons/006. The Neuron.mp4 34MB
  15. Chapter 3 Feedforward Artificial Neural Networks/005. Multiclass Classification.mp4 34MB
  16. Chapter 3 Feedforward Artificial Neural Networks/002. Forward Propagation.mp4 34MB
  17. Chapter 5 In-Depth Gradient Descent/004. Variable and Adaptive Learning Rates.mp4 33MB
  18. Chapter 4 In-Depth Loss Functions/001. Mean Squared Error.mp4 30MB
  19. Chapter 2 Machine Learning and Neurons/008. Making Predictions.mp4 30MB
  20. Chapter 5 In-Depth Gradient Descent/003. Momentum.mp4 29MB
  21. Chapter 5 In-Depth Gradient Descent/001. Gradient Descent.mp4 29MB
  22. Chapter 2 Machine Learning and Neurons/009. Saving and Loading a Model.mp4 28MB
  23. Chapter 4 In-Depth Loss Functions/003. Categorical Cross Entropy.mp4 27MB
  24. Chapter 3 Feedforward Artificial Neural Networks/001. Artificial Neural Networks Section Introduction.mp4 27MB
  25. Chapter 3 Feedforward Artificial Neural Networks/010. How to Choose Hyperparameters.mp4 24MB
  26. Chapter 2 Machine Learning and Neurons/004. Code Preparation (Regression Theory).mp4 24MB
  27. Chapter 1 Welcome/002. Outline.mp4 22MB
  28. Chapter 5 In-Depth Gradient Descent/002. Stochastic Gradient Descent.mp4 21MB
  29. Chapter 4 In-Depth Loss Functions/002. Binary Cross Entropy.mp4 20MB
  30. Chapter 1 Welcome/001. Introduction.mp4 19MB
  31. Chapter 2 Machine Learning and Neurons/010. Why Keras.mp4 18MB
  32. Chapter 2 Machine Learning and Neurons/011. Suggestion Box.mp4 17MB
  33. Chapter 3 Feedforward Artificial Neural Networks/004. Activation Functions.en.srt 24KB
  34. Chapter 2 Machine Learning and Neurons/002. Code Preparation (Classification Theory).en.srt 22KB
  35. Chapter 2 Machine Learning and Neurons/001. What Is Machine Learning.en.srt 20KB
  36. Chapter 5 In-Depth Gradient Descent/005. Adam Optimization (Part 1).en.srt 18KB
  37. Chapter 3 Feedforward Artificial Neural Networks/007. Code Preparation (Artificial Neural Networks).en.srt 17KB
  38. Chapter 3 Feedforward Artificial Neural Networks/006. How to Represent Images.en.srt 17KB
  39. Chapter 5 In-Depth Gradient Descent/004. Variable and Adaptive Learning Rates.en.srt 16KB
  40. Chapter 5 In-Depth Gradient Descent/006. Adam Optimization (Part 2).en.srt 15KB
  41. Chapter 2 Machine Learning and Neurons/007. How Does a Model Learn .en.srt 15KB
  42. Chapter 3 Feedforward Artificial Neural Networks/009. ANN for Regression.en.srt 14KB
  43. Chapter 2 Machine Learning and Neurons/006. The Neuron.en.srt 14KB
  44. Chapter 2 Machine Learning and Neurons/005. Regression Notebook.en.srt 13KB
  45. Chapter 3 Feedforward Artificial Neural Networks/002. Forward Propagation.en.srt 13KB
  46. Chapter 3 Feedforward Artificial Neural Networks/003. The Geometrical Picture.en.srt 13KB
  47. Chapter 4 In-Depth Loss Functions/001. Mean Squared Error.en.srt 12KB
  48. Chapter 3 Feedforward Artificial Neural Networks/005. Multiclass Classification.en.srt 12KB
  49. Chapter 3 Feedforward Artificial Neural Networks/008. ANN for Image Classification.en.srt 11KB
  50. Chapter 5 In-Depth Gradient Descent/001. Gradient Descent.en.srt 10KB
  51. Chapter 4 In-Depth Loss Functions/003. Categorical Cross Entropy.en.srt 10KB
  52. Chapter 2 Machine Learning and Neurons/003. Classification Notebook.en.srt 10KB
  53. Chapter 2 Machine Learning and Neurons/004. Code Preparation (Regression Theory).en.srt 10KB
  54. Chapter 2 Machine Learning and Neurons/008. Making Predictions.en.srt 9KB
  55. Chapter 3 Feedforward Artificial Neural Networks/010. How to Choose Hyperparameters.en.srt 9KB
  56. Chapter 3 Feedforward Artificial Neural Networks/001. Artificial Neural Networks Section Introduction.en.srt 8KB
  57. Chapter 5 In-Depth Gradient Descent/003. Momentum.en.srt 8KB
  58. Chapter 4 In-Depth Loss Functions/002. Binary Cross Entropy.en.srt 8KB
  59. Chapter 1 Welcome/002. Outline.en.srt 8KB
  60. Chapter 2 Machine Learning and Neurons/010. Why Keras.en.srt 6KB
  61. Chapter 5 In-Depth Gradient Descent/002. Stochastic Gradient Descent.en.srt 6KB
  62. Chapter 2 Machine Learning and Neurons/009. Saving and Loading a Model.en.srt 5KB
  63. Chapter 2 Machine Learning and Neurons/011. Suggestion Box.en.srt 5KB
  64. Chapter 1 Welcome/001. Introduction.en.srt 3KB