Machine Learning Hardware

We compare deep learning with machine learning and discuss their differences in all aspects. In addition to the comparison of deep learning and machine learning, we will also study their future trends.

"Csdn Live Report" December 2014 12-14th, sponsored by the China Computer Society (CCF), CCF large data expert committee contractor, the Chinese Academy of Sciences and CSDN jointly co-organized to promote large data research, application and industrial development as the main theme of the 2014 China Data Technology Conference (big Data Marvell Conference 2014,BDTC 2014) and the second session of the CCF Grand Symposium was opened at Crowne Plaza Hotel, New Yunnan, Beijing. 2014 China large data Technology ...

"Editor's note" Nvidia links GPU to machine learning more closely with the release of the Cudnn library, while achieving direct integration of the CUDNN and depth learning frameworks, allowing researchers to seamlessly utilize the GPU on these frameworks, ignoring low-level optimizations in the deep learning system, Focus more on more advanced machine learning issues. By releasing a set of libraries called CUDNN, nvidia links the GPU to machine learning more closely. It is reported that CUDNN can be directly integrated with the current popular depth learning framework. Nvid ...

In this article, I want to share with you 8 neural network architectures. I believe that any machine learning researcher should be familiar with this process to promote their work.

During the 2017 YunQi Computing Conference held in Shenzhen, Alibaba Cloud’s Chief Science Officer Dr Jingren Zhou officially launched the updated version of its machine learning platform “PAI 2.0”.

Artificial intelligence has entered everything – from autonomous cars to automatic emails to smart homes. You seem to get any merchandise (such as medical health, flight, travel, etc.) and make it smarter through the special application of artificial intelligence.

Some tasks are more complicated to code directly. We can't handle all the nuances and simple coding. Therefore, machine learning is necessary. Instead, we provide a large amount of data to machine learning algorithms, allowing the algorithm to continuously explore the data and build models to solve the problem.

Spam filtering, face recognition, recommendation engine-when you have a large dataset and want to use them to perform predictive analysis and pattern recognition, machine learning is the only way. In this science, computers can learn, analyze and manipulate data independently without prior planning, and more and more developers are now concerned with machine learning. The rise of machine learning technology is also important not only because hardware costs are getting cheaper and more powerful, but free software surges that machine learning is easily deployed on stand-alone or large-scale clusters The diversity of machine learning libraries means that whatever language you like ...

Medical information technology has always been a difficult point in the information process, before the development of medical information technology and the future trends, in this article, the reader will see how a group of engineers in the United States to use artificial intelligence and machine learning to promote medical information. Steven Horng, an emergency doctor and engineer at Beth Israel Deaconess Medical Center in Boston, is a member of the department that aims to introduce machine learning and artificial intelligence (AI) to the emergency room. "A lot of people are talking about getting a computer to diagnose a patient or replace a doctor, but I think it's a ...

In this article we analyzed the advantages and disadvantages of 13 algorithms of machine learning, including: Regularization Algorithms, Ensemble Algorithms, Decision Tree Algorithm, Artificial Neural Network, Deep Learning, etc.