Skip to main content

Network Programming - Part 1

Starting off with Network Programming I thought of discussing basics and the environment for the Network Programming.

Why Network Programming?

With the advancement of Technology and the ground breaking innovations we have come to a point that most of the thing we do are inter connected with each other. This is applicable for programs as well. Most of the programs we use has two modes now a days. Offline and Online. There for we can say that Programs are Network Aware. For example: Playing Dota 2 or LoL online. real time updates in servers, real time stock update etc.

Lets talk about few of the simple things that can be done using a Network Program. A Network Program can retrieve data from a remote location as well as vise versa. Other than that if we talk about it further more, it allows peer-to peer interaction which supports us in chats, messaging, video conferencing etc.Another thing is Spiders in web searching. Let me explain somethings more on the next paragraph.

For some who do SEO the term web Spider (Web Crawler) might not be an alien term. Web spider is a bot used by search engines for their optimization. When user searches something in the search engine it does not go here and there in the WWW searching for the content. What it does is looking in an index and return the results which is more faster. Google does it. So these web spiders go here and there on the WWW and they remember the pages they visited. Then they store it to process later and index. In simply Web Spiders (Web Crawlers) Helps to bring back the web content.

More than this you can learn more on this video done by Matt Cutts.



So I think this is more than enough with an introduction. More Network Programming with next posts. Good Bye for now.





Labels

Labels:
Location: Sri Jayawardenepura Kotte, Sri Lanka

Popular posts from this blog

Natural Language Processing with Python NLTK part 5 - Chunking and Chinking

Natural Language Processing Using regular expression modifiers we can chunk out the PoS tagged words from the earlier example. The chunking is done with regular expressions defining a chunk rule. The Chinking defines what we need to exclude from the selection. Here are list of modifiers for Python: {1,3} = for digits, u expect 1-3 counts of digits, or "places" + = match 1 or more ? = match 0 or 1 repetitions. * = match 0 or MORE repetitions $ = matches at the end of string ^ = matches start of a string | = matches either/or. Example x|y = will match either x or y [] = range, or "variance" {x} = expect to see this amount of the preceding code. {x,y} = expect to see this x-y amounts of the preceding code source: https://pythonprogramming.net/regular-expressions-regex-tutorial-python-3/ Chunking import nltk from nltk.tokenize import word_tokenize # POS tagging sent = "This will be chunked. This is for Test. World is awesome. Hello world....
Read more

Natural Language Processing with Python NLTK part 1 - Tokenizer

Natural Language Processing Starting with the NLP articles first we will try the  tokenizer  in the NLTK package. Tokenizer breaks a paragraph into the relevant sub strings or sentences based on the tokenizer you used. In this I will use the Sent tokenizer, word_tokenizer and TweetTokenizer which has its specific work to do. import nltk from nltk.tokenize import sent_tokenize, word_tokenize, TweetTokenizer para = "Hello there this is the blog about NLP. In this blog I have made some posts. " \ "I can come up with new content." tweet = "#Fun night. :) Feeling crazy #TGIF" # tokenizing the paragraph into sentences and words sent = sent_tokenize(para) word = word_tokenize(para) # printing the output print ( "this paragraph has " + str(len(sent)) + " sentences and " + str(len(word)) + " words" ) # print each sentence k = 1 for i in sent: print ( "sentence ...
Read more

Natural Language Processing with Python NLTK part 4 - PoS tagging

Natural Language Processing  PoS tagging or Part of Speech tagging is a commonly used mechanism. This will allow NLTK to tag the words that is in your corpus and give the tags accordingly. There are many tags predefined by the NLTK and here are the list. Number Tag Description 1. CC Coordinating conjunction 2. CD Cardinal number 3. DT Determiner 4. EX Existential  there 5. FW Foreign word 6. IN Preposition or subordinating conjunction 7. JJ Adjective 8. JJR Adjective, comparative 9. JJS Adjective, superlative 10. LS List item marker 11. MD Modal 12. NN Noun, singular or mass 13. NNS Noun, plural 14. NNP Proper noun, singular 15. NNPS Proper noun, plural 16. PDT Predeterminer 17. POS Possessive ending 18. PRP Personal pronoun 19. PRP$ Possessive pronoun 20. RB Adverb 21. RBR Adverb, comparative 22. RBS ...
Read more