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 |
| thume |
|