theano

Theano-简介 (Theano - Introduction)

Have you developed Machine Learning models in Python? Then, obviously you know the intricacies in developing these models. The development is typically a slow process taking hours and days of computational power.

您是否使用Python开发了机器学习模型？ 然后，显然您知道开发这些模型的复杂性。 开发通常是一个缓慢的过程，需要数小时和数天的计算能力。

The Machine Learning model development requires lot of mathematical computations. These generally require arithmetic computations especially large matrices of multiple dimensions. These days we use Neural Networks rather than the traditional statistical techniques for developing Machine Learning applications. The Neural Networks need to be trained over a huge amount of data. The training is done in batches of data of reasonable size. Thus, the learning process is iterative. Thus, if the computations are not done efficiently, training the network can take several hours or even days. Thus, the optimization of the executable code is highly desired. And that is what exactly Theano provides.

机器学习模型的开发需要大量的数学计算。 这些通常需要算术运算，尤其是多维的大型矩阵。 如今，我们使用神经网络而不是传统的统计技术来开发机器学习应用程序。 神经网络需要接受大量数据的培训。 训练是在合理大小的数据中分批进行的。 因此，学习过程是迭代的。 因此，如果计算效率不高，训练网络可能要花费几个小时甚至几天。 因此，非常需要可执行代码的优化。 而那正是Theano提供的。

Theano is a Python library that lets you define mathematical expressions used in Machine Learning, optimize these expressions and evaluate those very efficiently by decisively using GPUs in critical areas. It can rival typical full C-implementations in most of the cases.

Theano是一个Python库，可让您定义在机器学习中使用的数学表达式，优化这些表达式并通过在关键区域果断地使用GPU进行高效评估。 在大多数情况下，它可以与典型的完整C实现相媲美。

Theano was written at the LISA lab with the intention of providing rapid development of efficient machine learning algorithms. It is released under a BSD license.

Theano是在LISA实验室编写的，旨在快速开发高效的机器学习算法。 它是根据BSD许可证发行的。

In this tutorial, you will learn to use Theano library.

在本教程中，您将学习使用Theano库。

翻译自: https://www.tutorialspoint.com/theano/theano_introduction.htm

theano