The Conception Of Machine Learninng

the conception of Machine Learning 1  Hidden layer  [Heteroskedasticity](https://blog.csdn.net/dingming001/article/details/73826630)  Hessian Matrix  Hyperparameter tuning  How To Choose Hidden Unit Activiation Functions  Bias-Variance Tradeoff  alpha in ridge regression  Bootstrapping,[Transmission Gate](https://blog.csdn.net/batuwuhanpei/article/details/51884351)  capacity  Common Optimizers of Neural Nets  K-Fold Cross-Validation  Common Output Layer Activation Functions  Concave & convex function  cross-entropy  conditional probability  Cost and Lost Functions  Confidence Intervals  F1   Exploding Gradient Problem  error type  Finding Linear Regression Parameters  Gradient Descent  Gradient Descent rule of thume

The Unknow Word

The First Column	The Second Column
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