Theano 简明教程

Theano - Introduction

你在 Python 中开发过机器学习模型吗？然后，你显然知道开发这些模型的复杂性。开发通常是一个缓慢的过程，需要几个小时和几天的计算能力。

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.

机器学习模型开发需要大量的数学计算。这些通常需要算术计算，特别是多维大矩阵。这些天来，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。神经网络需要在大量数据上进行训练。分批次训练合理大小的数据。因此，学习过程是迭代的。因此，如果计算没有有效完成，则训练网络可能需要几个小时甚至几天。因此，对可执行代码进行优化是非常需要的。而这正是 Theano 所提供的。

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.

Theano 是一个 Python 库，它能让你定义机器学习中使用的数学表达式，优化这些表达式，并在关键领域精确地使用 GPU 来有效地评估它们。在大多数情况下，它可以与典型的全 C 实现相媲美。

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 是在 LISA 实验室编写的，目的是提供高效机器学习算法的快速开发。它是在 BSD 许可证下发布的。

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.

在本教程中，你将学习如何使用 Theano 库。

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