Artificial Intelligence (AI).Baidu Baike is introduced as follows: It is a new technological science that researches and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. I will explain the implementation of artificial intelligence in terms of technology: ASR, NLP and other technologies are commonly used in artificial intelligence: ASR technology: Speech recognition technology, also known as Automatic Speech Recognition Automatic Speech Recognition (ASR), aims
Reference: https://github.com/qqwweee/keras-yolo3 This article uses anaconda (installer) to create a virtual environment to achieve the purpose of isolation from other environments. The premise is to install anaconda (installer). If there is anything unclear, check other tutorials. In github tutorial, the recommended environment is Python 3.5.2 Keras 2.1.5 tensorflow 1.6.0 In addition, this article uses keras version of yolo3 (aphorism) 3 instead of directly using darknet for training, which has the
In the era of Internet big data, especially in the current hottest era of big data+Artificial Intelligence Plus online education+online knowledge payment+online health, how can we quickly find a field to find suitable entrepreneurial projects and seize opportunities.The online knowledge payment project, online health project, online consultation and payment project, and online it technology project are currently the hottest project fields after the relay business. Therefore, Tencent has opened Tencent courses and various online courses on various platforms.
In the process of data analysis, it is necessary to use various charts for data exploration.Descriptive statistics include histogram, scatter chart and other tools to explore continuous data. For classified data, bar chart, cross grouping table and other tools can be used.The so-called " pivot table" in Excel is actually an interactive cross-grouping table.In R language, it is easy to use functions such as table () to get corresponding results.For
Transferred from http://blog.csdn.net/tfygg/article/details/51760640?utm_source=itdadao&utm_medium=referral
Moving Object Detection refers to the process of eliminating redundant information in time and space in video by computer vision, and effectively extracting objects with spatial position changes.It has always been a very popular research field. If you enter " motion detection" on IEEE Xplore for quick search, you will return more than 18,000 documents.After decades of researchers' efforts, moving target detection technology has achieved good results and has been widely used in intelligent monitoring, multimedia applications and other fields.
R Language reshape2 Pack-Official Document Learning Introduction reshape2 package is a package developed by Hadley Wickham for data reconstruction. Its main functional functions are melt and cast, which realize the conversion between long data and wide data. The package also contains other functions and data sets Used properly, reshape2 Pack is a powerful tool for data processing. core function Long Data and Wide Data Before learning how to use the
From Microsoft Xiaoice, cortano (Cortana) a few years ago to Baidu Du Mi, a small robot in The Brain (game show), DuerOS； in the past two years;Ali's Smart City with Intelligent Recognition of Video Big Data as the Core;Google's AlphaGo, which defeated Li Shishi, etc.Coupled with the gradual maturity of big data and the continuous development of cloud computing, artificial intelligence seems to have come to the forefront. Technology companies, big and small, and even government agencies all speak big data.
From: The Go Programming Language （Golang） Programming 32 Pages We have already mentioned the characteristics of arrays in the previous section of inside: the length of arrays cannot be modified again after being defined;Arrays are value types and each pass will produce a copy.Obviously, this data structure cannot fully meet the real needs of developers.Don't be disappointed, The Go Programming Language （G
the Deep Network here includes: convolutional spark coding, deep rbm, tcnn, Sparse Autoencoder, etc.
Basic knowledge in this area can be consulted
Recently, I have been studying this aspect and accumulated some code. I put it up for file and share.
The main codes are available in deeplearning.net, but since the website has not been updated for two years, and Deep Learning is the most popular one recently, the metabolism of the codes is quite fast, so I'd like to share some good codes recently found (mainly from matlab):