• Interceptor intercept exploration in angular2


    original intention I saw angular2 officially released before. I looked at it in the past and felt good, so I went into the pit.In the process of using it, I want to write an interceptor like angular1 Interceptor in angular1 .factory(‘HttpRequestInterceptor’, [‘$q’, ‘$injector’, ‘ConfigService’, ‘DialogService’, function($q, $injector, ConfigService, DialogService) { return { request: function(config) { […]

  • Is there any new CNN variant that can improve the accuracy of the model?


    Is there any new CNN variant that can improve the accuracy of the model? -Answer to amaze2 – Zhihuhttps://www.zhihu.com/question/397944899/answer/1314983195 We can just introduce a one-dimensional CNN model called deep residual shrink networks. Deep residual shrinkage network was originally used in fault diagnosis based on one-dimensional vibration signal, which is an improvement of squeeze and exception […]

  • Ray and rllib for fast parallel reinforcement learning


    By Christian HubbsCompile | VKSource: toward Data Science Ray is more than just a library for multiprocessing. Ray’s real strength comes from the rllib and tune libraries, which take advantage of reinforcement learning. It allows you to extend your training to large distributed servers, or take advantage of parallelization to use your own laptop more […]

  • On distributed system


    Centralized Corresponding to distribution, it is centralized.Centralized means that all projects are stored in one server. Although the deployment is relatively simple, if there is a failure, the whole service will not be available. Moreover, it is not conducive to capacity expansion, which leads to the development of the latter distributed system. Distributed Distributed system […]

  • Building a blockchain with go — Part 7: Network


    A series of translated articles I have put on GitHub:blockchain-tutorialAny subsequent updates will be on GitHub and may not be synchronized here. If you want to run the code directly, you can also run it in the SRC directory from the tutorial repository on clone GitHubmakeThat’s fine. introduction So far, our prototype has all the […]

  • Brief introduction of yolov1 / V2 / V3 | target detection


    Yolo series is a very classic structure in the field of target detection. Although there are many higher quality and more complex networks, the structure of Yolo can still bring a lot of inspiration to Algorithm Engineers. These three papers look like a parameter adjustment manual. They teach you how to use various tricks to […]

  • Jigsaw pre training: get rid of Imagenet, pre training method of jigsaw backbone network | ECCV 2020


    Jigsaw pre training generates data sets for backbone network pre training in a jigsaw way from the detection data set, without the need for additional pre training data sets, such as Imagenet. In addition, in order to make the network better adapt to the mosaic data, this paper proposes the ERF adaptive dense classification method, […]

  • Interview summary of IOS senior engineer


    Interview form: telephone interview As a developer, it is very important to have a learning atmosphere and a communication circle. This is my ownIOS communication groupNo matter you are Xiaobai or Daniel, welcome to join us. Share bat, Ali’s interview questions, interview experience, discuss technology, let’s communicate, learn and grow together! 1. How do you […]

  • [front end knowledge of web development] – Web2.0 (1)


    Web1.0 Web 1.0 refers to the first stage of the development of the world wide web, from 1991 to 2004. “In Web 1.0, there are few content creators, and the vast majority of users are just consumers of content.” (in the era of Web 1.0, people can only browse content passively. )。 Web2.0 Introduction to […]

  • Paper reading: dcgan network


    introduction The dcgan network introduced in this paper is based on GaN network and introduces image convolution operation, that is, it combines CNN and Gan network well, so that the network generator can finally generate a false image from random noise. The author of the paperAlec Radford & Luke Metz。 In this paper, the author […]

  • Genetic CNN: Classic NAS algorithm, standard application of genetic algorithm | iccv 2017


    In this paper, the standard genetic algorithm is applied to the neural network structure search. Firstly, the network is coded and expressed, and then the genetic operation is carried out. The overall method is very simple, and the search space design is very simple, which is basically equivalent to only searching the connection mode between […]

  • Qianlang: network model of traditional data center


    Shanchuan, senior network engineer of Getu operation and maintenance platform With the expansion of the scale of Internet companies, the demand for cost control and data security is increasing. Most companies tend to build their own computer rooms instead of renting cloud servers. Personal push has experienced several iterations and changes in the network planning […]