Non-data scientists often use the word Model to mistakenly refer to the what in Machine Learning we canonically call Model Architecture. And it couldn't be more wrong!
非数据科学家经常使用“ 模型 ”一词来错误地指代机器学习中我们通常所说的“模型体系结构”。 而且这没有错！
是否想偶尔听到有关Tensorflow，Keras，DeepLearning4J，Python和Java的抱怨？ (Wanna hear occasional rants about Tensorflow, Keras, DeepLearning4J, Python and Java?)
Join me on twitter @ twitter.com/hudsonmendes!
加入我的Twitter @ twitter.com / hudsonmendes！
Taking Machine Learning models to production is a battle. And there I share my learnings (and my sorrows) there, so we can learn together!
将机器学习模型投入生产是一场战斗。 我在那里分享我的学习(和悲伤)，所以我们可以一起学习！
The Model Architecture is very important to the Model. Without the variety of architectures we have we would never be able to fit data for the vastness of different problems that are currently being successfully solved by Deep Learning.
模型架构对模型非常重要。 如果没有各种各样的架构，我们将无法为深度学习当前成功解决的众多不同问题提供合适的数据。
However, the Weights are a vital part of the model too. Different Weights can describe functions that have absolutely different geometry.
但是， 权重也是该模型的重要组成部分 。 不同的权重可以描述具有完全不同的几何形状的函数。
Model = Architecture (a.k.a. algorithm) + Weights (a.k.a. parameters)
模型=体系结构(又名算法)+权重(又名参数)
Let's have a look at how it works:
让我们看一下它是如何工作的：
The same function "architecture" (or, "equation") f(x) = tanh(w × x) has produced very different material functions with different physical shapes, due to their different weights (or "coefficients").
相同的函数“体系结构”(或“等式”) f(x)= tanh(w×x)由于权重(或“系数”)不同而产生了具有完全不同物理形状的 材料函数 。
不当用语 (Inappropriate terminology)
In the statement above, I have used inappropriate terminology on purpose:
在以上声明中，我故意使用了不恰当的术语：
"architecture" was used to describe the way that the variables interact within the function;
“ 体系结构 ”用来描述变量在函数内的交互方式；
"physical shape” was used to describe something as immaterial as a curve in the cartesian plan, which could not be less physical.
“ 物理形状
