不二晨c

我想做的你们都没做,你们做了的有好有差,如果说还有什么可以做的,我也会提供不错的答案 (偶尔玩玩知乎、微博、人人,总是觉得自己能做很多)

福建 厦门

感兴趣的主题: 动漫 旅行 想爱的人能快乐 想要环游世界 想要简单的生活 关注生活 软件设计 电影

1个粉丝

不二晨c 的课程评论

更多评论

不二晨c 关注的课程

機器學習基石 (Machine Learning Foundations) (Coursera) 10 个评论 关注

开始时间: 09/08/2015 持续时间: 8 weeks

主页: https://www.coursera.org/course/ntumlone

简介: Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. The course teaches the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know. [機器學習旨在讓電腦能由資料中累積的經驗來自我進步。本課程將介紹各領域中的機器學習使用者都應該知道的基礎演算法、理論及實務工具。]

Introduction to Hadoop and MapReduce (Udacity) 1 个评论 关注

开始时间: 随时 持续时间: 自主

主页: https://www.udacity.com/course/ud617

简介: The Apache™ Hadoop® project develops open-source software for reliable, scalable, distributed computing. Learn the fundamental principles behind it, and how you can use its power to make sense of your Big Data.

Neural Networks for Machine Learning (Coursera) 5 个评论 关注

开始时间: 待定 持续时间: 8 weeks

主页: https://www.coursera.org/course/neuralnets

简介: Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well.

Coding the Matrix: Linear Algebra through Computer Science Applications (Coursera) 9 个评论 关注

开始时间: 02/02/2015 持续时间: 10 weeks

主页: https://www.coursera.org/course/matrix

简介: Learn the concepts and methods of linear algebra, and how to use them to think about computational problems arising in computer science. Coursework includes building on the concepts to write small programs and run them on real data.

Discrete Inference and Learning in Artificial Vision (Coursera) 0 个评论 关注

开始时间: 待定 持续时间: Unknown

主页: https://www.coursera.org/course/artificialvision

简介: We will present the state of the art energy minimization algorithms that are used to perform inference in modern artificial vision models: that is, efficient methods for obtaining the most likely interpretation of a given visual input. We will also cover the popular max-margin framework for estimating the model parameters using inference.

更多课程