Machine Learning Github

Open source machine learning tools also allow you to migrate learning, which means you can solve machine learning problems by applying other aspects of knowledge.

In machine learning applications, privacy should be considered an ally, not an enemy. With the improvement of technology. Differential privacy is likely to be an effective regularization tool that produces a better behavioral model. For machine learning researchers, even if they don't understand the knowledge of privacy protection, they can protect the training data in machine learning through the PATE framework.

The "Editor's note" machine learning seems to have turned from obscurity to the limelight overnight, as well as more open source tools for machine learning, but the challenge now is how to get developers interested in machine learning and the data they are prepared to use to actually use them, This paper collects the common and practical open source machine learning tools in several languages, which is worth paying attention to, which is from InfoWorld. The following is the original: After decades of development as a professional discipline, machine learning seems to appear overnight as a popular business tool ...

Machine learning (ML) and artificial intelligence (AI) are now hot topics in the IT industry. Similarly, containers have become one of the hot topics. We introduce both machine learning and containers into the image, and experiment to verify that they will work together to accomplish the classification task.

This article is by no means comprehensive, but rather highlights the pitfalls we have seen over and over. For example, we won't talk about how to choose a good project. Some of our recommendations are generally applicable to machine learning, especially for deep learning or reinforcement learning research projects.

Introduction: It is well known that R is unparalleled in solving statistical problems. But R is slow at data speeds up to 2G, creating a solution that runs distributed algorithms in conjunction with Hadoop, but is there a team that uses solutions like python + Hadoop? R Such origins in the statistical computer package and Hadoop combination will not be a problem? The answer from the king of Frank: Because they do not understand the characteristics of R and Hadoop application scenarios, just ...

Shogun is a machine learning toolkit that focuses on large kernel methods and support vector Machine (SVM) toolkits. It provides a universal SVM object interface connected to different SVM implementations and efficient kernel implementations. In addition to supporting SVMS and regression analysis, Shogun has some linear methods such as linear discriminant analysis (LDA), linear programming Machine (LPM), (kernel) perceptron and algorithm hidden Markov model. Shogun can be used for c++++, Matlab, R, Octave, and Python. ...

Start-up company Rare Technologies recently released a hyperscale machine learning benchmark that focuses on GPUs and compares the performance of machine learning costs, ease of use, stability, scalability and performance with several popular hardware providers.

Recently, Clay.io's Zoli Kahan began writing "10X" series of posts. Through this series of posts, Zoli will share how to use only a small team to support Clay.io's large-scale applications. The first share is an inventory of the technology used by Clay.io. CloudFlare CloudFlare is primarily responsible for supporting DNS and as a buffer proxy for DDoS attacks while cloud ...

The development of spark for a platform with considerable technical threshold and complexity, spark from the birth to the formal version of the maturity, the experience of such a short period of time, let people feel surprised. Spark was born in Amplab, Berkeley, in 2009, at the beginning of a research project at the University of Berkeley. 　　It was officially open source in 2010, and in 2013 became the Aparch Fund project, and in 2014 became the Aparch Fund's top project, the process less than five years time. Since spark from the University of Berkeley, make it ...