TGINSIGHT SIMILAR POSTS
Source channel @olddriverGDstudy · Post #29 · Mar 17
搜索使用说明 #搜索指南 因为电报软件对中文搜索支持不好，大队特别对队内资源搜索进行了整理汇集，使用方法说明如下： 1.1 原理： 电报对中文搜索支持不佳，汉字只有在前后含有asic码字符的前提下可以被正确搜索出，如 _广州修车大队_ (“_”指代空格）、(广州修车大队）;等形式可以搜索“广州修车大队”搜索出相关信息；搜索“广州”等未被asic码间隔的汉字无法正确显示。 为正确搜索，在编制频道资源时，对重要信息可以采取Hashtag的形式已方便搜索，即以"#"字符开头，接汉字，以“空格字符”结尾的形式，点击一个hashtag即可快速定位该频道或聊天群内所有相同标签，建议所有管理在编辑重要资料包括ls信息、广播台、学习频道时正确使用hashtag。 ！！注意标签不要随意编写，要参考搜索指南中有的标签类型！！ 1.2 JS资源定位： JS目前支持 Hasgtag（#K老师）、数字标签（#GZ003）的搜索方式，在对应榜单和报告区中试用上述方式均可查找到JS的相关信息。 使用举例：在“广州公开榜”或“广州修车大队”的搜索栏中输入 #K老师 或 #GZ003，均可定位到K老师资料页；在报告区的搜索栏中输入#K老师 或 #GZ003，均可定位到K老师的验证报告。这两者是快速了解JS基本信息和评价的便捷办法。 1.3 标签查找 公榜榜单目前均支持标签查找，可以快速定位某种类型或地区的所有JS，目前仅支持Hashtag查找，目前常用标签解释如下： 地区标签： 一定要使用一级标签，例如 #天河区（注意不要有错别字） #颜值： 不解释 #服务： 评价中92、95的，有场子出身花式水平的，均会归入此类； #大胸： 不解释，一般D以上归入此类； #长腿： 不解释，一般168以上归入此类； #身材： 不解释，较为宽松； #嫩妹： 22岁以下或者长相很嫩的，白小纯的，loli系的，cos系的归入此类； #熟女： 30岁以上风韵犹存的，归入此类； #特服： 提供3p、3t、wt、字母等特殊服务的JS归入此类。 使用举例：在红榜的搜索栏中输入 #长腿，可以快速查看“莉贝伦”等8位长腿JS。 类型标签评价目前非常主观，有不妥之处请队内私信 JackJack 或其他管理人员修改。 1.4 资料查找 目前学习频道中试用hashtag来快速定位资料，目前使用的标签有如下几种： #安全CJ#素质CJ#卫生CJ #搜索指南 #大队玩法 #语录#秀哥语录 #技巧#知识
Search: #matplotlib
@djangoproject · Post #127 · 08/31/2016, 03:27 PM
http://scikit-learn.org/stable/ scikit-learn #Machine#Learning in Python Simple and efficient tools for data mining and data analysis Accessible to everybody, and reusable in various contexts Built on #NumPy, #SciPy, and #matplotlib Open source, commercially usable - BSD license
@djangoproject · Post #423 · 08/26/2017, 08:39 AM
http://scitools.org.uk/iris/docs/latest/userguide/index.html Iris seeks to provide a powerful, easy to use, and community-driven Python library for analysing and visualising #meteorological and #oceanographic data sets. With Iris you can: Use a single #API to work on your data, irrespective of its original format. Read and write (CF-)netCDF, GRIB, and PP files. Easily produce graphs and maps via integration with #matplotlib and #cartopy.
@djangoproject · Post #424 · 08/26/2017, 08:43 AM
http://scitools.org.uk/cartopy/docs/latest/index.html Cartopy is a Python package designed to make drawing maps for data analysis and visualisation as easy as possible. #Cartopy makes use of the powerful #PROJ.4, #numpy and #shapely libraries and has a simple and intuitive drawing interface to #matplotlib for creating publication quality maps. Some of the key features of cartopy are: object oriented projection definitions point, line, vector, polygon and image transformations between projections integration to expose advanced mapping in matplotlib with a simple and intuitive interface powerful vector data handling by integrating shapefile reading with Shapely capabilities
@djangoproject · Post #448 · 09/18/2017, 11:30 AM
https://medium.com/@GalarnykMichael/logistic-regression-using-python-sklearn-numpy-mnist-handwriting-recognition-matplotlib-a6b31e2b166a Logistic Regression using Python (#Sklearn, #NumPy, #MNIST, Handwriting #Recognition, #Matplotlib) #machine_learning.
@djangoproject · Post #130 · 08/31/2016, 03:39 PM
http://matplotlib.org/ #matplotlib is a python #2D#plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. matplotlib can be used in #python scripts, the python and #ipython shell (ala MATLAB®* or Mathematica®†), web application servers, and six #graphical user interface toolkits. screenshots
@djangoproject · Post #507 · 11/26/2017, 10:08 PM
http://devarea.com/machine-learning-with-python-introduction/#.Whs6iCehU8o #Machine_Learning With Python – Introduction #Numpy is package for multi dimension arrays – very effective implementation #Scipy – package for scientific programming , mathematics , signal processing and more #Pandas – package for data handling #Matplotlib – package for data visualization (graphs) #Seaborn – extend Matplotlib with statistical graphs #Scikits – many extensions to spicy for specific fields like x-ray, image processing , deep learning and many more
@djangoproject · Post #352 · 06/25/2017, 08:57 AM
https://stxnext.com/blog/2017/04/12/most-popular-python-scientific-libraries/ The most popular Python scientific libraries: #Astropy #Biopython #Cubes #DEAP #SCOOP #PsychoPy #Pandas #Mlpy #matplotlib #NumPy #NetworkX #TomoPy #Theano #SymPy #SciPy #scikit_learn #scikit_image #ScientificPython #SageMath #Veusz #graph_tool #SunPy #Bokeh
@djangoproject · Post #513 · 11/30/2017, 10:00 PM
#AI#Artificial_Intelligence #AJAX #aiohttp #Anaconda #AngularJS #API #Atom #AWS #asyncio (#Asynchronous) #audio #automated_testing #automation #atexit #BeeWare #Big_Data #bitcoin #blockchain #Bluemix #Brython #button #Celery #client #class #classmethod #concurrency #Coroutine #cron #CSS #curl #data_analysis #data_mining #data_processing #database #Deep_Learning#deep_learning #Debian #decorator #deploy #dict #dispatch #django #django_cms #Django_REST_Framework #dropdownbox #Docker #event #Firefox #Flask #form #functions #Generator #GeoDjango #git #Google #GPU #GUI #Gym #host #HTML #httplib #learn #Image_processing #intelligence #input #Instagram #IOT #iPython #Jupyter #lambda #learn #License #Linux #lists #machine_learning #Magenta #map #Matplotlib #Metaprogramming #Micro_services #Micropython #mind #monitoring #MongoDB #modules #Mozilla #Multipart #multi_touch_apps #multiprocessing #Nodes #NoSQL #numeric_computation #numerical #NumPy #network #neural_network #OAuth #object_serialization #OCR #overloading #package #parallel #pipeline #protocols #PostGIS #pyAudioAnalysis #pycon #Pyflakes #PyInstaller #PyPI #PyQt #PySide #PyTorch #pytest #python #Pyvideo_archives #Qt #Raspberry_Pi #React #Redis #random #request #Regular_Expressions (#re) #REST #RSS #satellite #scikit_learn #SciPy #scrapy #searching #selectbox #Selenium #serialization #server #sessions #single_responsibility_principle #socket #Spark #str #submit #task #telegram #template #TensorFlow #test #text_boxes #text #tuples #unicode #Universe #Unix #unit_test #urllib #upload #uWSGI #Web #WSGI
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