網(wǎng)絡(luò)爬蟲
網(wǎng)絡(luò)爬蟲在CPP中爬行鏈接到你想要的深度。控制臺(tái)應(yīng)用程序
Ubuntu 14.04 LTS上編譯的程序
用g+編譯器編譯
相依性
卷曲
Boost圖書館
用于編譯的命令
G+爬蟲.cpp-lcurl-lost_regex-o爬蟲
輸入
URL：您想要抓取示例“dirghbuch.com”的URL
鏈接數(shù)：要從爬行中提取的每頁鏈接數(shù)
深度：我們想爬多深，在哪里深度可以定義為樹的深度。
輸出量
crawler.txt
限制
鏈接數(shù)最多可達(dá)100。
Does not work for website which has blocked curl crawling for example google.com yahoo.com
由于缺乏并行性，所以速度很慢。
沒有完整URL的鏈接被追加到用戶在大容量中插入的URLwww.xyz.com有/conatct-us的網(wǎng)址將是www.xyz.com/contact-us
唯一的單詞也包含html標(biāo)記。
可能的改進(jìn)，但尚未落實(shí)
限制共享變量的使用
改進(jìn)使其易于并行化
比卷曲更有效的爬行方式
Introduction
jSMPP is a java implementation (SMPP API) of the SMPP protocol (currently supports SMPP v3.4). It provides interfaces to communicate with a Message Center or an ESME (External Short Message Entity) and is able to handle traffic of 3000-5000 messages per second.
jSMPP is not a high-level library. People looking for a quick way to get started with SMPP may be better of using an abstraction layer such as the Apache Camel SMPP component: http://camel.apache.org/smpp.html
Travis-CI status:
History
The project started on Google Code: http://code.google.com/p/jsmpp/
It was maintained by uudashr on Github until 2013.
It is now a community project maintained at http://jsmpp.org
Release procedure
mvn deploy -DperformRelease=true -Durl=https://oss.sonatype.org/service/local/staging/deploy/maven2/ -DrepositoryId=sonatype-nexus-staging -Dgpg.passphrase=<yourpassphrase>
log in here: https://oss.sonatype.org
click the 'Staging Repositories' link
select the repository and click close
select the repository and click release
License
Copyright (C) 2007-2013, Nuruddin Ashr uudashr@gmail.com Copyright (C) 2012-2013, Denis Kostousov denis.kostousov@gmail.com Copyright (C) 2014, Daniel Pocock http://danielpocock.com Copyright (C) 2016, Pim Moerenhout pim.moerenhout@gmail.com
This project is licensed under the Apache Software License 2.0.
When digital media is perceived only as a tool to deliver content the potential for
using its affordances to explore meaning is lost. Rather than seeing media only as
an access point, we can view it as a way to enhance the expressiveness of content.
Today blogs, wikis, messaging, mash-ups, and social media (Facebook, Twitter,
YouTube and others) offer authors ways to create narrative meaning that refl ects
our new media culture. We can look to the past for similarities and parallels to
better understand how to use social media as a creative tool with which to
dialogue, collaborate, and create interactive narratives.
The growth of mobile technologies is remarkable. At a recent Mobile World Congress Conference, Eric
Schmidt, CEO of Google predicted that within three years, smart phones will surpass Personal Com-
puter sales. The number of mobile phones used worldwide has exceeded 4.6 billion with continued
growth expected in the future. In fact, in the United States alone, the numbers of mobile phone users
comprise over 80% of the population.
The information age is exploding around us,
giving us access to dizzying amounts of data the instant it becomes available.
Smart phones and tablets provide an untethered experience that offers stream-
ing video, audio, and other media formats to just about any place on the planet.
Even people who are not “computer literate” use Facebook to catch up with
friends and family, use Google to research a new restaurant choice and print
directions to get there, or Tweet their reactions once they have sampled the
fare. The budding Internet-of-things will only catalyze this data eruption.
The infrastructure supporting these services is also growing exponentially,
and the technology that facilitates this rapid growth is virtualization.
這是我在做大學(xué)教授期間推薦給我學(xué)生的一本書，非常好，適合入門學(xué)習(xí)。《python深度學(xué)習(xí)》由Keras之父、現(xiàn)任Google人工智能研究員的弗朗索瓦?肖萊（Franc?ois Chollet）執(zhí)筆，詳盡介紹了用Python和Keras進(jìn)行深度學(xué)習(xí)的探索實(shí)踐，包括計(jì)算機(jī)視覺、自然語言處理、產(chǎn)生式模型等應(yīng)用。書中包含30多個(gè)代碼示例，步驟講解詳細(xì)透徹。作者在github公布了代碼，代碼幾乎囊括了本書所有知識(shí)點(diǎn)。在學(xué)習(xí)完本書后，讀者將具備搭建自己的深度學(xué)習(xí)環(huán)境、建立圖像識(shí)別模型、生成圖像和文字等能力。但是有一個(gè)小小的遺憾：代碼的解釋和注釋是全英文的，即使英文水平較好的朋友看起來也很吃力。本人認(rèn)為，這本書和代碼是初學(xué)者入門深度學(xué)習(xí)及Keras最好的工具。作者在github公布了代碼，本人參照書本，對(duì)全部代碼做了中文解釋和注釋，并下載了代碼所需要的一些數(shù)據(jù)集（尤其是“貓狗大戰(zhàn)”數(shù)據(jù)集），并對(duì)其中一些圖像進(jìn)行了本地化，代碼全部測(cè)試通過。（請(qǐng)按照文件順序運(yùn)行，代碼前后有部分關(guān)聯(lián)）。以下代碼包含了全書約80%左右的知識(shí)點(diǎn)，代碼目錄：2.1: A first look at a neural network（ 初識(shí)神經(jīng)網(wǎng)絡(luò)）3.5: Classifying movie reviews（電影評(píng)論分類：二分類問題）3.6: Classifying newswires（新聞分類：多分類問題 ）3.7: Predicting house prices（預(yù)測(cè)房?jī)r(jià)：回歸問題）4.4: Underfitting and overfitting（ 過擬合與欠擬合）5.1: Introduction to convnets（卷積神經(jīng)網(wǎng)絡(luò)簡(jiǎn)介）5.2: Using convnets with small datasets（在小型數(shù)據(jù)集上從頭開始訓(xùn)練一個(gè)卷積網(wǎng)絡(luò)）5.3: Using a pre-trained convnet（使用預(yù)訓(xùn)練的卷積神經(jīng)網(wǎng)絡(luò)）5.4: Visualizing what convnets learn（卷積神經(jīng)網(wǎng)絡(luò)的可視化）
