How To Make A Machine Learning Program

"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 ...

Machine learning is the most advanced aspect of the field of artificial intelligence today, and more beginners have begun to enter this field.

Machine Learning (ML) studies these patterns and encodes human decision processes into algorithms. These algorithms can be applied to several instances to arrive at meaningful conclusions.

At the heart of machine learning is "using algorithms to parse data, learn from it, and then make decisions or predictions about something in the world." This means that instead of explicitly writing a program to perform certain tasks, it is better to teach the computer how to develop an algorithm to accomplish the task.

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.

Developing new machine learning algorithms and describing how they work and why work is a science is often not necessary when developing a learning system.

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.

Since 2006, a topic called deep learning in the field of machine learning has begun to receive widespread attention in the academic world. Today it has become a boom in Internet big data and artificial intelligence.

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.

The past spring Festival has allowed programmers to have a rare holiday break, but artificial intelligence in the holiday has been improving, we saw the Facebook AI director Yann LeCun, the Hong Kong University of Science and Technology, Director of the Department of Computer and Engineering Yangqiang and other artificial intelligence Daniel's cool thinking about the upsurge of artificial intelligence, Google has also seen the development of artificial intelligence gaming systems that transcend human levels in specific conditions. 　　Here's a look at the new Year's inspiration from Daniel's artificial intelligence. Yann LECUN:IBM True North is "the straw race science" ...