Coursera 笔记

DeepLearning.AI

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

改进深度神经网络：超参数调优、正则化与优化

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课程笔记

1 篇
  1. 01Course Notes课程笔记
Module 01

Practical Aspects of Deep Learning

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  1. 01Train / Dev / Test sets
  2. 02Bias / Variance
  3. 03Basic Recipe for Machine Learning
  4. 04Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas!
  5. 05Clarification about Upcoming Regularization Video
  6. 06Regularization
  7. 07Why Regularization Reduces Overfitting?
  8. 08Dropout Regularization
  9. 09Clarification about Upcoming Understanding Dropout Video
  10. 10Understanding Dropout
  11. 11Other Regularization Methods
  12. 12Normalizing Inputs
  13. 13Vanishing / Exploding Gradients
  14. 14Weight Initialization for Deep Networks
  15. 15Numerical Approximation of Gradients
  16. 16Gradient Checking
  17. 17Gradient Checking Implementation Notes
  18. 18Lecture Notes W1
  19. 19(Optional) Downloading your Notebook, Downloading your Workspace and Refreshing your Workspace
  20. 20Yoshua Bengio Interview
Module 02

Optimization Algorithms

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  1. 01Mini-batch Gradient Descent
  2. 02Understanding Mini-batch Gradient Descent
  3. 03Exponentially Weighted Averages
  4. 04Understanding Exponentially Weighted Averages
  5. 05Bias Correction in Exponentially Weighted Averages
  6. 06Gradient Descent with Momentum
  7. 07RMSprop
  8. 08Clarification about Upcoming Adam Optimization Video
  9. 09Adam Optimization Algorithm
  10. 10Clarification about Learning Rate Decay Video
  11. 11Learning Rate Decay
  12. 12The Problem of Local Optima
  13. 13Lecture Notes W2
  14. 14Yuanqing Lin Interview
Module 03

Hyperparameter Tuning, Batch Normalization and Programming Frameworks

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  1. 01Tuning Process
  2. 02Using an Appropriate Scale to pick Hyperparameters
  3. 03Hyperparameters Tuning in Practice: Pandas vs. Caviar
  4. 04Clarification about Upcoming Normalizing Activations in a Network Video
  5. 05Normalizing Activations in a Network
  6. 06Fitting Batch Norm into a Neural Network
  7. 07Why does Batch Norm work?
  8. 08Batch Norm at Test Time
  9. 09Clarifications about Upcoming Softmax Video
  10. 10Softmax Regression
  11. 11Training a Softmax Classifier
  12. 12Deep Learning Frameworks
  13. 13TensorFlow
  14. 14(Optional) Learn about Gradient Tape and More
  15. 15Lecture Notes W3
  16. 16[IMPORTANT] Reminder about end of access to Lab Notebooks
  17. 17References
  18. 18Acknowledgments