一些經典的kaggle,數據科學的問題仍是須要收集案例學習一下。php
Search algorithm with CUDAjava
百萬量級時,經常使用的數據固然能夠提早存放在gpu mem裏。python
A Roadmap of Parallel Sorting Algorithms using GPU Computingreact
堆排序的並行優點感人。附送官方代碼:android
https://developer.nvidia.com/gpugems/GPUGems/gpugems_ch37.htmlios
Sorting with GPUs: A Surveygit
python須要系統的過一遍。
tensorflow也要熟練使用各類特性,包括caffe。
machine leanring相關的算法須要再完全整理一遍。
動物行爲識別:http://www.mousemotorlab.org/deeplabcut
React Native攝像頭功能實現!
如下的都暫時忽略。
### 大數據年末職位 ###
技術棧:Java, Scala, hadoop, spark, steaming/hive/tensorflow
Docker 學習
https://app.codility.com/programmers/
### 數據分析系統 ###
目前想作的是:Jan 12, 2019
將本身的知識體系完全整理一遍,把大部分實體書都電子化。
大數據結合gpu這塊是個有意思的地方,須要research。
既然是實踐體系,就要有手頭工具書的效果。開這一篇章,源於一個經歷,share with YOU。
自認爲Machine Learning and Statistical Inference在IT界還算玩得溜,初次闖蕩Data Science也是resume紛飛廣撒網,終被一獵頭相中,拋來一test。
打開aws一瞧,data size = 50G! ... 蒙了。原覺得下載本地,用Python折騰一圈也就是一天的活兒。不過下載這50G的parquet files也不太現實。
Google一圈後,才明白這是scala+spark+aws analysis api的套路。雖然熟悉這套路也就十天半月的事情,但此次機會也算是飛了。
頓悟,縱然理論玩得溜,實驗作得歡,但與工業界脫軌實則是一大方向性錯誤。故,除了理論體系還需一套實踐體系支撐自我。
Ubuntu + Visual Studio Code with vim mode.
VSCode's GitLens,實時查看修改記錄,棒棒噠!
Expo.io: 發佈測試容器。
Goto: 個人軟件工程導航
首先,/boot空間仍是儘可能空間分配大點吧,至少一個G。
內核下載:https://kernel.ubuntu.com/~kernel-ppa/mainline/
內核掛掉不要慌:Ubuntu 16.04 Linux系統內核升級方法
#
# qmake configuration for win32-msvc2008
#
# Written for Microsoft VC2005.NET
#
MAKEFILE_GENERATOR = MSVC.NET
TEMPLATE = app
CONFIG += qt warn_on release incremental flat link_prl precompile_header autogen_precompile_source copy_dir_files debug_and_release debug_and_release_target embed_manifest_dll embed_manifest_exe
QT += core gui
DEFINES += UNICODE WIN32 QT_LARGEFILE_SUPPORT
QMAKE_COMPILER_DEFINES += _MSC_VER=1500 WIN32
QMAKE_CC = cl
QMAKE_LEX = flex
QMAKE_LEXFLAGS =
QMAKE_YACC = byacc
QMAKE_YACCFLAGS = -d
QMAKE_CFLAGS = -nologo -Zm200 -Zc:wchar_t-
QMAKE_CFLAGS_WARN_ON = -W3
QMAKE_CFLAGS_WARN_OFF = -W0
QMAKE_CFLAGS_RELEASE = -MT
QMAKE_CFLAGS_RELEASE_WITH_DEBUGINFO += -MT -Zi
QMAKE_CFLAGS_DEBUG = -Zi -MDd
QMAKE_CFLAGS_YACC =
QMAKE_CFLAGS_LTCG = -GL
QMAKE_CFLAGS_MP = -MP
QMAKE_CXX = $QMAKE_CC
QMAKE_CXXFLAGS = $QMAKE_CFLAGS
QMAKE_CXXFLAGS_WARN_ON = $QMAKE_CFLAGS_WARN_ON -w34100 -w34189
QMAKE_CXXFLAGS_WARN_OFF = $QMAKE_CFLAGS_WARN_OFF
QMAKE_CXXFLAGS_RELEASE = $QMAKE_CFLAGS_RELEASE
QMAKE_CXXFLAGS_RELEASE_WITH_DEBUGINFO += $QMAKE_CFLAGS_RELEASE_WITH_DEBUGINFO
QMAKE_CXXFLAGS_DEBUG = $QMAKE_CFLAGS_DEBUG
QMAKE_CXXFLAGS_YACC = $QMAKE_CFLAGS_YACC
QMAKE_CXXFLAGS_LTCG = $QMAKE_CFLAGS_LTCG
QMAKE_CXXFLAGS_MP = $QMAKE_CFLAGS_MP
QMAKE_CXXFLAGS_STL_ON = -EHsc
QMAKE_CXXFLAGS_STL_OFF =
QMAKE_CXXFLAGS_RTTI_ON = -GR
QMAKE_CXXFLAGS_RTTI_OFF =
QMAKE_CXXFLAGS_EXCEPTIONS_ON = -EHsc
QMAKE_CXXFLAGS_EXCEPTIONS_OFF =
QMAKE_INCDIR =
QMAKE_INCDIR_QT = $[QT_INSTALL_HEADERS]
QMAKE_LIBDIR_QT = $[QT_INSTALL_LIBS]
QMAKE_RUN_CC = $(CC) -c $(CFLAGS) $(INCPATH) -Fo$obj $src
QMAKE_RUN_CC_IMP = $(CC) -c $(CFLAGS) $(INCPATH) -Fo$@ {1}lt;
QMAKE_RUN_CC_IMP_BATCH = $(CC) -c $(CFLAGS) $(INCPATH) -Fo$@ @<<
QMAKE_RUN_CXX = $(CXX) -c $(CXXFLAGS) $(INCPATH) -Fo$obj $src
QMAKE_RUN_CXX_IMP = $(CXX) -c $(CXXFLAGS) $(INCPATH) -Fo$@ {1}lt;
QMAKE_RUN_CXX_IMP_BATCH = $(CXX) -c $(CXXFLAGS) $(INCPATH) -Fo$@ @<<
QMAKE_LINK = link
QMAKE_LFLAGS = /NOLOGO
QMAKE_LFLAGS_RELEASE = /NODEFAULTLIB:msvcrt.lib /INCREMENTAL:NO
QMAKE_LFLAGS_RELEASE_WITH_DEBUGINFO = /NODEFAULTLIB:msvcrt.lib /DEBUG /OPT:REF
QMAKE_LFLAGS_DEBUG = /DEBUG
QMAKE_LFLAGS_CONSOLE = /SUBSYSTEM:CONSOLE
QMAKE_LFLAGS_WINDOWS = /SUBSYSTEM:WINDOWS \"/MANIFESTDEPENDENCY:type=\'win32\' name=\'Microsoft.Windows.Common-Controls\' version=\'6.0.0.0\' publicKeyToken=\'6595b64144ccf1df\' language=\'*\' processorArchitecture=\'*\'\"
QMAKE_LFLAGS_DLL = /DLL
QMAKE_LFLAGS_LTCG = /LTCG
QMAKE_LIBS_CORE = kernel32.lib user32.lib shell32.lib uuid.lib ole32.lib advapi32.lib ws2_32.lib
QMAKE_LIBS_GUI = gdi32.lib comdlg32.lib oleaut32.lib imm32.lib winmm.lib winspool.lib ws2_32.lib ole32.lib user32.lib advapi32.lib
QMAKE_LIBS_NETWORK = ws2_32.lib kernel32.lib user32.lib advapi32.lib gdi32.lib crypt32.lib
QMAKE_LIBS_OPENGL = glu32.lib opengl32.lib gdi32.lib user32.lib
QMAKE_LIBS_COMPAT = advapi32.lib shell32.lib comdlg32.lib user32.lib gdi32.lib ws2_32.lib
QMAKE_LIBS_QT_ENTRY = -lqtmain
QMAKE_MOC = $[QT_INSTALL_BINS]\\moc.exe
QMAKE_UIC = $[QT_INSTALL_BINS]\\uic.exe
QMAKE_IDC = $[QT_INSTALL_BINS]\\idc.exe
QMAKE_IDL = midl
QMAKE_LIB = lib /NOLOGO
QMAKE_RC = rc
QMAKE_ZIP = zip -r -9
QMAKE_COPY = copy /y
QMAKE_COPY_DIR = xcopy /s /q /y /i
QMAKE_MOVE = move
QMAKE_DEL_FILE = del
QMAKE_DEL_DIR = rmdir
QMAKE_CHK_DIR_EXISTS = if not exist
QMAKE_MKDIR = mkdir
VCPROJ_EXTENSION = .vcproj
VCSOLUTION_EXTENSION = .sln
VCPROJ_KEYWORD = Qt4VSv1.0
load(qt_config)
TEMPLATE = app CONFIG += console CONFIG -= app_bundle CONFIG -= qt SOURCES += main.cpp INCLUDEPATH += /usr/local/include/opencv LIBS += -L/usr/local/lib \ -lopencv_shape -lopencv_stitching -lopencv_objdetect \ -lopencv_superres -lopencv_videostab -lopencv_calib3d \ -lopencv_features2d -lopencv_highgui -lopencv_videoio \ -lopencv_imgcodecs -lopencv_video -lopencv_photo \ -lopencv_ml -lopencv_imgproc -lopencv_flann -lopencv_core
ctrl+e & ctrl+y: 頁面滾動
tab --> 4 blank spaces.
:set ts=4
:set expandtab
:%retab!
http://blog.csdn.net/linxing927/article/details/52956983
https://askubuntu.com/questions/232086/remove-full-path-from-terminal
vim /etc/hostname
http://blog.csdn.net/sabrecode/article/details/50460382
source activate py27
sudo chown -R unsw:unsw /usr/local/
Ubuntu下面掛載windows NTFS格式分區沒有可執行權限
lsof -i :8081 列出8081誰在用,而後殺掉,騰出地方。 kill -9 <PID>
關於編程ing的測試,極其重要!從兩處增強:
未整理
至關不錯的技術棧學習計劃。
Jobs:
(1)
3D AR應用。
(2)
金融分析。
java服務器 | 基本的back-end服務搭建,以及如何連接amazon api | 只需最基本的要求 |
python工程實踐 | 基於機器學習的全部相關編程經驗 | 能夠考慮結合kaggle,達到精英級別,積累代碼 |
Android攝像頭 | react native的攝像頭相關的編程 | 代碼積累 |
神經網路識別 | 模型實踐 | 自定義識別經驗積累 |
python金融數據編程 | 過一遍書,寫成英文博客。 | wordpress,github |
Ideally, you will have strong experience doing ETL (AWS Glue 是一項徹底託管的提取、轉換和加載 (ETL) 服務) with huge data sets, using tools such as Talend or AWS Glue. In addition, experience with Python, Linux, Shell scripting, Spark and other technologies in the Hadoop ecosystem will be valuable.
This team is responsible for working alongside a leading team of machine learning and data scientists to deliver analytics projects to large enterprises, such as Australia's biggest banks, insurance companies, telcos and retailers. The specific big data engineer contract is to assist with the ETL and data pipeline build with very large data sets.
You should have good knowledge of SQL datawarehouse environments and big data such as; Hadoop, Spark, hdfs, Hive, AWS, Talend/Informatica/AWS Glue, Teradata, amongst other enterprise data warehouse and big data tech.
Work Rights: Must have valid Australian Work Rights
Interviews and Start date: ASAP
Mode: PAYG or PTY Ltd
In this role, you will help us build novel architectures for classifying and understanding complex and dynamic visual environments. You will have access to the best sensor data in the world, and an incredible infrastructure for testing and validating your algorithms. We are creating new algorithms for segmentation, tracking, classification, and high-level scene understanding.
We're looking for engineers with advanced degrees and experience building perception pipelines that work with real data in rapidly changing and uncertain environments.
An ideal candidate would be also interested in working on broader artificial intelligence implementations, primarily new algorithms for reinforcement learning in real-world applications.
Qualifications
Additional qualifications
ABOUT Remi
Remi is an Artificial Intelligence firm working on a wide range of complex projects in both industry and academia. Remi aims to bring the next generation of A.I Research and Solutions to market. We're looking for top talent that shares our love of A.I and wants to be part of a fast-paced, growing, and ambitious team.
Qualifications
Who You'll Work With
You’ll be based in one of our Australian offices and will be a part of Digital McKinsey, working closely our Global Energy & Materials (GEM) practice.
Digital McKinsey brings together the best of McKinsey’s digital capabilities to help our clients use digital technology to transform their businesses. As part of this group, you’ll join a global team working on everything from IT modernization and strategy to agile, cloud, cybersecurity, and digital transformation.
You’ll typically work on projects across all industries and functions and will be fully integrated with the rest of our global firm.You’ll also work with colleagues from across McKinsey & Company to help our clients deliver breakthrough products, experiences, and businesses, both on technology and non-technology topics.
Our office culture is casual, fun and social, with an emphasis on education and innovation. We have the freedom to try new ideas, experiment and are expected to be constantly learning and growing. There is also a strong emphasis on mentoring others in the group, enabling them to grow and learn.
What You'll Do
You will play a key consultative and client-facing role in the development of big data and advanced analytics as a refined, yet evolving, capability of our client teams who advise the top firms in the Global Energy & Materials (GEM) sector.
In this role you will visualize and articulate the possibilities that analytics and digital present within these industry sectors, generating a feeling of excitement and opportunity with our clients. You will have the opportunity to apply solutions in one or more areas, such as predictive maintenance, yield improvement, big data strategy, technological innovation and related areas. You will be expected to bring a recognized and unique knowledge of core business drivers within oil & gas, power and mining sector, including applying data and analytics.
You will exhibit depth of knowledge in advanced analytics, including knowing how to 「translate」 complex data into easily digestible action items within a business context, external and internal audiences. You will work closely with other data scientists and other analytics focused consultants to build digital solutions, leveraging strong working knowledge of value levers and digital capabilities.
As a Front End Software Engineer at Google, you will specialize in building responsive and elegant web applications that scale to millions of users in dozens of languages.
In this role, you will work on Chrome OS, an open source and lightweight operating system designed for speed, simplicity and security, and Chromebooks, a newer, faster computer running Chrome OS as its operating system.
Google is and always will be an engineering company. We hire people with a broad set of technical skills who are ready to take on some of technology's greatest challenges and make an impact on millions, if not billions, of users. At Google, engineers not only revolutionize search, they routinely work on massive scalability and storage solutions, large-scale applications and entirely new platforms for developers around the world. From AdWords to Chrome, Android to YouTube, Social to Local, Google engineers are changing the world one technological achievement after another.
Responsibilities
Design and implement new user-facing features in Google’s products.
Write client-side code for web-based applications, creating fast, easy-to-use, high volume production applications.
Optimize web applications to maximize speed and scale.
Collaborate on scalability issues involving data.
Qualifications
Minimum qualifications:
BA/BS degree in Computer Science or related technical field, or equivalent practical experience.
Experience in web technologies (object-oriented JavaScript, HTML, CSS) and experience with HTML5 and CSS3.
Experience in software development; experience developing web-based applications.
Software development experience in one or more general purpose programming languages.
Preferred qualifications:
Master's Degree or PhD in related field.
Understanding of algorithms and data structures, and their time and space performance
Excellent problem-solving, analytical and troubleshooting skills; ability to work with minimum guidance.
Everything here starts from 2011
Android計劃書:
函數式編程與RxJava(附demo)【設計模式的必要性】
Android 插件框架機制之預熱篇【理解框架】
最好的5個Android ORM框架【理解框架】
Android 必須知道2018年流行的框架庫及開發語言,看這一篇就夠了!【各類功能框架】
軟件工程計劃書:
Android:
https://github.com/hieuapp/android-firebase-chat【已測試,沒voice message】
Voice message相關:
Iphone:
https://github.com/relatedcode/RealtimeChat【功能全,可檢測】
Home | BitTiger - 官網
【面試中】系統設計怎麼考?系統設計題怎麼答? - 系統設計方法
正則表達式判斷數字:(整數和浮點)
Regular Expression approach: Check if a given string is a valid number (Integer or Floating Point) in Java
推薦方式:
import sys try: sys.exit(0) except: print 'die' finally: print 'cleanup'
Python下opencv使用筆記(六)(圖像的形態學轉換)
python-opencv的博客,有必要擼一遍!謝謝!
展現了os模塊列表。
文件和文件夾是否存在、建立文件夾、權限判斷等。
# 記錄log
logging.debug(...)
logging.info(...)
logging.warn(...)
logging.error(...)
logging.critical(...)
n = int( input() )
print(*range(1, n + 1), sep="")
Ref: https://www.hackerrank.com/challenges/python-print/tutorial
N = int( input() ) A = [0,1] for i in range(2,N): A.append(A[i-1]+A[i-2]) print map(lambda a: a*a*a, A)[:N]
與reduce比較:可見這是一個pre-result做爲當前參數的一種迭代方式。
from functools import reduce
def add(x, y) : # 兩數相加 return x + y >>> reduce(add, [1,2,3,4,5]) # 計算列表和:1+2+3+4+5 15 >>> reduce(lambda x, y: x+y, [1,2,3,4,5]) # 使用 lambda 匿名函數 15
二者的結合
def charToNumber(s): def charToNum(str): return int(str) def numToNumber(x,y): return x+y return reduce(numToNumber,list(map(charToNum,s))) print(charToNumber('345789'))
import re mailList = list()
for i in range(int(input())): mailLst.append(input())
print ( sorted(list( filter(lambda x: re.search(r'^[\w\d-]+@[A-Za-z0-9]+\.\w?\w?\w$', x), mailList) )) )
C++中,若是父類中的函數前邊標有virtual,才顯現出多態。
若是父類func是virtual的,則
Super *p =new Sub(); p->func(); // 調用子類的func
若是不是virtual的,p->func將調用父類原來的函數。
Java中,無論寫不寫virtual都是多態的,子類的同名函數會override父類的。