从事构建、开发以及维护 ML 产品工作的机器学习工程师, 和进行研究分析、评估系统影响并形成项目思路的数据专家, 二者分工存在差异。以下是目前主流的 15 个机器学习框架简介:讯伽: 具备通用性的分布式深度学习平台, 对卷积神经网络、循环神经网络等一类模型予以支持有着分层抽..象性的设计。ML提供可视化向导降低ML使用门槛。Azure ML 可在Azure云端进行训练以及部署, 它能够支持微软的算法, 也支持第三方算法, 还提供8小时的免费试用机会。Caffe, 它是由伯克利所开发的, 此模型依据可不是编码而是配置, 在运行时能够实现CPU与GPU之间的无缝连接进行切换, 并且单个GPU一天内可以处理超过6000万张图片。水: 具备易于使用特性能够进行预测分析的平台, 对网络用户界面以及主流数据库予以支持, 能够扩展到。MOA, 是数据流挖掘方面的开源框架, 其中包含分类算法, 还有聚类算法, 也有推荐算法等, 它采用Java进行编写。Spark的MLlib, 是一个机器学习库, 具备可伸缩性, 涵盖了分类、包含回归、还有协同过滤等内容和方面。C库追求高效与易用提供命令行和C API。 Web挖掘组件支持爬虫、NLP、SVM等。Learn是基于Numpy/SciPy的库, 它涵盖聚类, 它涵盖分类, 它涵盖回归, 它开源, 它社区活跃。于1999年起始的C库, 借助SWIG对Java、R等多种语言予以支持。具有开源性质的数值计算库, 它是基于数据流图构建的, 它支持CPU, 它支持GPU, 它拥有C接口。数值计算库可定义优化表达式性能接近C。Torch基于和C/CUDAGPU优先生态丰富。Veles, 它是C分布式深度学习平台, 能起到辅助作用并且借助REST 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