djangoproject

#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

https://goo.gl/ucbkhT #Data_Science for #Big_Data with #Anaconda Enterprise Getting Python and R’s most popular data science libraries to work on a computational cluster can be a major challenge. And in a Big Data world, surmounting this challenge is key to leveraging data science within your organization to make smart, data-driven decisions.

http://www.blopig.com/blog/2016/08/processing-large-files-using-python/ Oxford Protein Informatics Group (OPIG) Processing large files using python In the last year or so, and with my increased focus on ribo-seq data, I have come to fully appreciate what the term #big_data means. The ribo-seq studies in their raw forms can easily reach into hundreds of GBs, which means that processing them in both a timely and efficient manner requires some thought. In this blog post, and hopefully those following, I want to detail some of the methods I have come up (read: pieced together from multiple stack exchange posts), that help me take on data of this magnitude. Specifically I will be detailing methods for #python and R, though some of the methods are transferrable to other languages.

https://impact.apartmentocean.com/20-buzzwords-know-artificial-intelligence/ 20 Buzzwords you have to know in #Artificial_Intelligence 1. #AI (Artificial Intelligence) 2. #Cloud_Computing 3. #Big_Data 4. Algorithm 5. #Python 6. #Data_Warehouse 7. #Machine_Learning 8. #Deep_Learning 9. #Artificial_Neural_Network 10. #Chatbot 11. #Data_Mining 12. Predictive Analytics 13. #OCR (#Optical_Character_Reader) 14. #AlphaGo and #Deepmind 15. #AWS (#Amazon_Web_Services) 16. #IBM_Watson 17. #Yottabyte 18. #NLP (#Natural_Language_Processing) 19. #IOT (#Internet_of_Things) 20. #Smart_City

http://jwork.org/dmelt/ #DataMelt, or #DMelt, is a software for #numeric_computation, statistics, analysis of large data volumes ("#big_data") and scientific visualization. The program can be used in many areas, such as natural sciences, engineering, modeling and analysis of financial markets. DMelt is a #computational_platform. It can be used with different programming languages on different operating systems. Unlike other statistical programs, it is not limited by a single programming language. DMelt can be used with several scripting languages, such as Python/Jython, BeanShell, Groovy, Ruby, as well as with Java.