目录
3、模型参数（Parameter ）和超参数（Hyperparameter）区分
1、超参数（Hyperparameter）
模型外部的配置变量，不能通过测试数据训练来获得。
专业解释：
- They are often used in processes to help estimate model parameters.
- They are often specified by the practitioner.【人为设定】
- They can often be set using heuristics.
- They are often tuned for a given predictive modeling problem.
2、模型参数（Parameter ）
模型设计+超参数+训练数据集训练，最终获得的参数，例如最终模型为y=ax+b，系数a和b就是模型参数。
以下是专业的解释：
- They are estimated or learned from data.
- They are required by the model when making predictions.
- They values define the skill of the model on your problem.
- They are often not set manually by the practitioner.【不能人为设定】
- They are often saved as part of the learned model.
3、模型参数（Parameter ）和超参数（Hyperparameter）区分
Hyperparameters are the knobs that you can turn when building your machine / deep learning model.
Hyperparameters are all the training variables set manually with a pre-determined value before starting the training.
If you have to specify a model parameter manually then it is probably a model hyperparameter.
一个深度神经网络（Deep Neural Networks）的例子帮助更好理解
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