几个git链接：

• https://github.com/HFTrader/DeepLearningBook
• https://github.com/ExtremeMart/DeepLearningBook-ReadingNotes
• https://github.com/ExtremeMart/DeepLearningBook-CN

目录：

• 5.1 Learning Algorithms
  • The Task, T
  • The Performance Measure, P
  • The Experience, E
  • Example: Linear Regression
• 5.2 Capacity, Overfitting and Underfitting
  • The No Free Lunch Theorem
  • Regularization
• 5.3 Hyperparameters and Validation Sets
  • Cross-Validation
• 5.4 Estimators, Bias and Variance
  • Point Estimation
  • Bias
  • Variance and Standard Error
  • Trading off Bias and Variance to Minimize Mean Squared Error
  • Consistency
• 5.5 Maximum Likelihood Estimation
  • Conditional Log-Likelihood and Mean Squared Error
  • Properties of Maximum Likelihood
• 5.6 Bayesian Statistics
  • Maximum A Posteriori (MAP) Estimation
• 5.7 Supervised Learning Algorithms
  • Probabilistic Supervised Learning
  • Support Vector Machines
  • Other Simple Supervised Learning Algorithms
• 5.8 Unsupervised Learning Algorithms
  • Principal Components Analysis
  • k-means Clustering
• 5.9 Stochastic Gradient Descent
• 5.10 Building a Machine Learning Algorithm
• 5.11 Challenges Motivating Deep Learning
  • The Curse of Dimensionality
  • Local Constancy and Smoothness Regularization
  • Manifold Learning