caffe2 caffe_Caffe2-简介

caffe2 caffe

Caffe2-简介 (Caffe2 - Introduction)

Last couple of years, Deep Learning has become a big trend in Machine Learning. It has been successfully applied to solve previously unsolvable problems in Vision, Speech Recognition and Natural Language Processing (NLP). There are many more domains in which Deep Learning is being applied and has shown its usefulness.

最近几年，深度学习已成为机器学习的一大趋势。 它已成功应用于解决视觉，语音识别和自然语言处理 (NLP)中以前无法解决的问题。 深度学习正被应用到更多领域，并显示出其有用性。

Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework developed at Berkeley Vision and Learning Center (BVLC). The Caffe project was created by Yangqing Jia during his Ph.D. at University of California - Berkeley. Caffe provides an easy way to experiment with deep learning. It is written in C++ and provides bindings for Python and Matlab.

Caffe(用于快速特征嵌入的卷积体系结构)是由伯克利视觉与学习中心(BVLC)开发的深度学习框架。 Caffe项目由贾庆清博士期间创建。 在加州大学伯克利分校就读。 Caffe提供了一种进行深度学习实验的简便方法。 它是用C ++编写的，并提供了Python和Matlab的绑定。

It supports many different types of deep learning architectures such as CNN (Convolutional Neural Network), LSTM (Long Short Term Memory) and FC (Fully Connected). It supports GPU and is thus, ideally suited for production environments involving deep neural networks. It also supports CPU-based kernel libraries such as NVIDIA, CUDA Deep Neural Network library (cuDNN) and Intel Math Kernel Library (Intel MKL).

它支持许多不同类型的深度学习架构，例如CNN (卷积神经网络)， LSTM (长短期记忆)和FC(全连接)。 它支持GPU，因此非常适合涉及深度神经网络的生产环境。 它还支持基于CPU的内核库，例如NVIDIA ，CUDA深度神经网络库(cuDNN)和英特尔数学内核库(Intel MKL) 。

In April 2017, U.S. based social networking service company Facebook announced Caffe2, which now includes RNN (Recurrent Neural Networks) and in March 2018, Caffe2 was merged into PyTorch. Caffe2 creators and community members have created models for solving various problems. These models are available to the public as pre-trained models. Caffe2 helps the creators in using these models and creating one’s own network for making predictions on the dataset.

2017年4月，美国社交网络服务公司Facebook宣布Caffe2，现在包括RNN(递归神经网络)，2018年3月，Caffe2合并为PyTorch。 Caffe2的创建者和社区成员创建了用于解决各种问题的模型。 这些模型作为预训练模型可供公众使用。 Caffe2帮助创建者使用这些模型并创建自己的网络以对数据集进行预测。

Before we go into the details of Caffe2, let us understand the difference between machine learning and deep learning. This is necessary to understand how models are created and used in Caffe2.

在详细介绍Caffe2之前，让我们了解机器学习和深度学习之间的区别。 这对于了解如何在Caffe2中创建和使用模型是必要的。

机器学习与深度学习 (Machine Learning v/s Deep Learning)

In any machine learning algorithm, be it a traditional one or a deep learning one, the selection of features in the dataset plays an extremely important role in getting the desired prediction accuracy. In traditional machine learning techniques, the feature selection is done mostly by human inspection, judgement and deep domain knowledge. Sometimes, you may seek help from a few tested algorithms for feature selection.

在任何机器学习算法中，无论是传统算法还是深度学习算法，数据集中特征的选择对于获得所需的预测精度都起着极其重要的作用。 在传统的机器学习技术中， 特征选择主要是通过人工检查，判断和深层知识来完成的。 有时，您可能会从一些经过测试的算法中寻求帮助以进行功能选择。

The traditional machine learning flow is depicted in the figure below −

下图描述了传统的机器学习流程-

In deep learning, the feature selection is automatic and is a part of deep learning algorithm itself. This is shown in the figure below −

在深度学习中，特征选择是自动的，并且是深度学习算法本身的一部分。 如下图所示：

In deep learning algorithms, feature engineering is done automatically. Generally, feature engineering is time-consuming and requires a good expertise in domain. To implement the automatic feature extraction, the deep learning algorithms typically ask for huge amount of data, so if you have only thousands and tens of thousands of data points, the deep learning technique may fail to give you satisfactory results.

在深度学习算法中， 特征工程是自动完成的。 通常，要素工程非常耗时，并且需要在领域内具有良好的专业知识。 为了实现自动特征提取，深度学习算法通常需要大量数据，因此，如果您只有成千上万的数据点，则深度学习技术可能无法为您提供令人满意的结果。

With larger data, the deep learning algorithms produce better results compared to traditional ML algorithms with an added advantage of less or no feature engineering.

与传统的机器学习算法相比，有了更大的数据，深度学习算法可产生更好的结果，并具有更少或不需要特征工程的优势。

翻译自: https://www.tutorialspoint.com/caffe2/caffe2_introduction.htm

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