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mrkn 2021/05/20
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python
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mrkn 2020/07/06
Python ç‰ˆã® Scratch ã‚‚ã‚ã£ãŸã®ã‹ã€œ

education

python
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Python ãƒ©ã‚¤ãƒ–ãƒ©ãƒªã‚’ Ruby ã‹ã‚‰ä½¿ã†
English version is available at github PyCall ã¯ Ruby ã®ä¸ã‹ã‚‰æ—¢å˜ã® Python ãƒ©ã‚¤ãƒ–ãƒ©ãƒªã‚’ä½¿ã†ã«ã¯ã¨ã¦ã‚‚ä¾¿åˆ©ãªãƒ©ã‚¤ãƒ–ãƒ©ãƒªã§ã™ã€‚ ã¨ã¯ã„ãˆã€ã“ã‚Œã¯ Ruby ã‹ã‚‰ Python ã®é–¢æ•°ã‚’å‘¼ã¶ãŸã‚ã®ãƒ©ã‚¤ãƒ–ãƒ©ãƒªãªã®ã§ã€Python ã®é–¢æ•°ã‚’ï¼‘å›žå‘¼ã¶åº¦ã« Ruby ã«åˆ¶å¾¡ãŒæˆ»ã£ã¦ãã¾ã™ã€‚ ã“ã‚ŒãŒå…ƒã§ã¡ã‚‡ã£ã¨é–“é•ãˆã‚„ã™ã„ã“ã¨ã‚‚ã‚ã‚‹ã‚ˆã†ã§ã™ã€‚ ä¾‹ãˆã°ä¸‹ã®ã‚ˆã†ãªè¨˜äº‹ã‚’è¦‹ã¤ã‘ã¾ã—ãŸã€‚ RubyÃ—PyCallã§Tensorflowã®MNISTãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«ã€Œé€£æƒ³é…åˆ—ã®é•ã„ã§æ‰‹é–“å–ã£ãŸã€ ãã“ã§ Ruby ã®æŸ”è»Ÿãªæ§‹æ–‡ã‚’æ´»ã‹ã—ã¦ã€Python ã£ã½ã„ã‚³ãƒ¼ãƒ‰ã‚’ Ruby ãƒ—ãƒã‚°ãƒ©ãƒ ã®ä¸ã«åŸ‹ã‚è¾¼ã‚“ã§ãŠãã¨ã€ãã“ã ã‘ã¾ã¨ã‚ã¦åˆ‡ã‚Šå‡ºã—ã¦ Python ã«é€ã£ã¦å®Ÿè¡Œã™ã‚‹ DSL ã‚’ä½œã£ã¦ã¿ã¾ã—ãŸã€‚ Yadriggy ã‚’ä½¿ã£ã¦ä½œã£ã¦ã„ã¾ã™ã€‚ ã“ã® DSL ã®å®Ÿè£…ã¯
mrkn 2018/12/09
PyCall ã‚’ä½¿ã£ã¦ä½œã‚‰ã‚ŒãŸé»’é”è¡“ãŒå‡„ã‹ã£ãŸã€‚

python

ruby

pycall
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mrkn 2017/12/18
Excel ã®ä¸ã§ pandas ãŒä½¿ãˆãŸã‚‰ã™ã”ãä¾¿åˆ©ãã†ã ãªï¼ï¼

excel

python
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ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆã®å¦ç¿’éŽç¨‹ã®å¯è¦–åŒ–ã‚’é¡Œæã«ã€Jupyter + Bokeh ã§å‹•çš„ãªæç”»ã‚’è¡Œã†æ–¹æ³•ã®ç´¹ä»‹ [Jupyter Advent Calendar 2017] | LESS IS MORE
ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆã®å¦ç¿’éŽç¨‹ã®å¯è¦–åŒ–ã‚’é¡Œæã«ã€Jupyter + Bokeh ã§å‹•çš„ãªæç”»ã‚’è¡Œã†æ–¹æ³•ã®ç´¹ä»‹ [Jupyter Advent Calendar 2017] å‰ç½®ã Jupyter Advent Calendar 2017 14æ—¥ç›®ã®è¨˜äº‹ã§ã™ã€‚ã“ã®è¨˜äº‹ã¯ã€Jupyter notebookã§ä½œæˆã—ãŸã‚‚ã®ã‚’nbconvertã§markdownã«å¤‰æ›ã—ã€æ‰‹ã§å°‘ã—ä¿®æ£ã—ã¦ä½œã‚Šã¾ã—ãŸã€‚èªã¿ç‰©ã¨ã—ã¦ã¯ã“ã®è¨˜äº‹ã‚’ã€å®Ÿè¡Œã™ã‚‹ã«ã¯ãƒŽãƒ¼ãƒˆãƒ–ãƒƒã‚¯ã®æ–¹ã‚’å‚ç…§ã—ã¦ã„ãŸã ãã®ãŒè‰¯ã„ã‹ã¨æ€ã„ã¾ã™ã€‚ ãƒŽãƒ¼ãƒˆãƒ–ãƒƒã‚¯ (gist) nbviewer æ¦‚è¦ é©å½“ãªãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆã®å¦ç¿’éŽç¨‹ã®å¯è¦–åŒ–ï¼ˆãƒã‚¹ã€æ£è§£çŽ‡ã®é·ç§»ç‰ï¼‰ã‚’é¡Œæã«ã—ã¦ã€Bokehã‚’ä½¿ã£ã¦å‹•çš„ã«ã‚°ãƒ©ãƒ•ã‚’æ›´æ–°ã—ã¦ã„ãã“ã¨ã«ã‚ˆã‚‹å¯è¦–åŒ–ã®å®Ÿç”¨ä¾‹ã‚’ç´¹ä»‹ã—ã¾ã™ã€‚ã“ã®ãƒŽãƒ¼ãƒˆãƒ–ãƒƒã‚¯ã®å†’é ã«ã€æœ€å¾Œã¾ã§å®Ÿè¡Œã™ã‚‹ã¨å¾—ã‚‰ã‚Œã‚‹ã‚°ãƒ©ãƒ•ä¸€è¦§ã‚’ã¾ã¨ã‚ã¾ã—ãŸã€‚ã©ã†ã‚„ã£ã¦ã‚°ãƒ©ãƒ•ã‚’ä½œã‚‹ã®ã‹
mrkn 2017/12/15
å‚è€ƒã«ãªã‚‹

python

bokeh

jupyter_notebook

data_science
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Latent Predictor Networks for Code Generation
Many language generation tasks require the production of text conditioned on both structured and unstructured inputs. We present a novel neural network architecture which generates an output sequence conditioned on an arbitrary number of input functions. Crucially, our approach allows both the choice of conditioning context and the granularity of generation, for example characters or tokens, to be
mrkn 2017/11/09
deep_learning

python

program_generation
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A map of the PyData Stack
One question you have when you use Python is what do I do with my data. How do I process it and analyze it. The aim of this flow chart is to simply provide a simple to use â€˜mapâ€™ of the PyData stack. At PyData Amsterdam Iâ€™ll present this and explain it in more detail but I hope this helps. Thanks to Thomas Wiecki, Matt Rocklin, Stephan Hoyer and Rob Story for their feedback and discussion over the
mrkn 2017/10/04
python

pydata

pydata-stack
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wheelã®ã‚ã‚ŠãŒãŸã•ã¨Anacondaã¸ã®è¦æœ› - YAMAGUCHI::weblog
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mrkn 2017/09/08
python

package_management

pyconjp
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RejectKaigi 2017ã§ã€ŽRubyã§æ©Ÿæ¢°å¦ç¿’ã‚’è¡Œã†ãŸã‚ã®ã€Œå·¨äººã®è‚©ã«ä¹—ã‚‹ã€åˆ¥ã®æ–¹æ³•ã€ã¨ã„ã†ç™ºè¡¨ã‚’ã—ã¾ã—ãŸ - @naitohã®æ—¥è¨˜
RubyKaigi 2017 ã« CFP ã‚’å‡ºã—ãŸã¨ã“ã‚æ®‹å¿µãªãŒã‚‰ Reject ã•ã‚Œã¦ã—ã¾ã„ã€ã“ã®å†…å®¹ã‚’ç™ºè¡¨ã™ã‚‹å ´ãŒç„¡ã„ã‹ãªã¨è€ƒãˆã¦ã„ãŸã¨ã“ã‚ã€ RejectKaigi 2017 ã«ç™ºè¡¨ã®æ©Ÿä¼šã‚’é ‚ãã¾ã—ãŸã®ã§ã€ãŠè©±ã•ã›ã¦é ‚ãã¾ã—ãŸã€‚ ã‚¹ãƒ©ã‚¤ãƒ‰ã¯ä¸‹è¨˜ã«å…¬é–‹ã—ã¦ã¾ã™ã®ã§ã€ã”è¦§ãã ã•ã„ã€‚ Reject kaigi2017 naitoh from Jun Naitoh www.slideshare.net ãŠè©±ã—ãŸå†…å®¹ã¯ Ruby ã§æ©Ÿæ¢°å¦ç¿’ã‚’ã—ãŸã„ã¨æ€ã£ãŸå ´åˆã€ç¾çŠ¶ã§ã¯ä¸å¯èƒ½ãªãŸã‚ã€ä¸‹è¨˜ã®ä¸‰ã¤ã®å†…å®¹ã«ã¤ã„ã¦ Ruby ã‚³ãƒŸãƒ¥ãƒ‹ãƒ†ã‚£ã§å–ã‚Šçµ„ã¿ãŒè¡Œã‚ã‚Œã¦ãŠã‚Šã€ æ—¢å˜ã®gemã‚’ãªã‚“ã¨ã‹ã™ã‚‹ã“ã¨ Rubyã®ãŸã‚ã®ä»•çµ„ã¿ã‚’ã¤ãã£ã¦ã„ãã“ã¨ å·¨äºº (Python) ã®è‚©ã«ä¹—ã‚‹æ–¹æ³• ã“ã®ä¸ã®ç¾å®Ÿçš„ãªè§£ãŒã€Œå·¨äºº (Python) ã®è‚©ã«ä¹—ã‚‹æ–¹æ³•ã€ã‚’å®Ÿç¾ã—ã¤ã¤ã‚ã‚‹ PyCall ã«ãªã‚Šã¾ã™ã€‚ PyCall ã®è©³
mrkn 2017/08/21
ruby

rejectkaigi2017

python
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Prophetã‚’ä½¿ã£ã¦ã“ã®å…ˆã‚’å ã£ã¦ã¿ãŸ - Money Forward Developers Blog
ã“ã‚“ã«ã¡ã¯ã€‚ ã‚¨ãƒ³ã‚¸ãƒ‹ã‚¢ã®ä¼Šè—¤ã§ã™ã€‚ ä»Šã¯æ–°ã‚µãƒ¼ãƒ“ã‚¹ã®ç«‹ã¡ä¸Šã’ã‚’è¡Œã£ã¦ã„ã¾ã™ã€‚ ä½•äº‹ã‚‚ãã†ãªã®ã§ã™ãŒã€ä¸ç¢ºå®Ÿãªã“ã¨ãŒã‚ã‚‹ã¨ã©ã†ã—ã¦ã‚‚å…ˆè¡Œãã‚’å ã£ã¦ã¿ãŸããªã‚‹â€¥ ãã‚ŒãŒäººé–“ã®å¿ƒç†ã¨ã„ã†ã‚‚ã®ã§ã¯ãªã„ã§ã—ã‚‡ã†ã‹ã€‚ (ã‚³ãƒ¼ãƒ‰ã‚’æ›¸ãã“ã¨ã«ã‚ˆã‚‹å®‰å¿ƒæ„ŸãŒãã‚Œã«å‹ã‚‹ã®ã‹ã‚‚ã—ã‚Œã¾ã›ã‚“) ãã‚“ãªä¸ã€ä»Šå¹´ã®2æœˆã«Facebookã®æ–¹ãŒæ™‚ç³»åˆ—è§£æžã®ãƒ©ã‚¤ãƒ–ãƒ©ãƒªã‚’Rã¨Pythonã§ãƒªãƒªãƒ¼ã‚¹ã—ã¦ã„ãŸã®ã§ã€ä»Šå›žã¯ãã‚Œã‚’æ´»ç”¨ã—ã¦ã€ã“ã®å…ˆã®è¦‹é€šã—ã‚’è¡Œã£ã¦ã¿ãŸã„ã¨æ€ã„ã¾ã™ã€‚ ãã®åã‚‚ Prophetã§ã™ã€‚ ã‚½ãƒ¼ã‚¹ã®Githubã®ãƒªãƒ³ã‚¯ã¯ ã“ã¡ã‚‰ Prophetã¨ã¯ã€ç›´è¨³ã™ã‚‹ã¨é è¨€è€…ã¨ã„ã†æ„å‘³ã¨ãªã‚Šã€ã¾ã•ã«ã“ã®å…ˆã‚’å ã†ã«ã¯ã†ã£ã¦ã¤ã‘ã®ãƒ©ã‚¤ãƒ–ãƒ©ãƒªã®åç§°ã§ã™ãã€‚ æ™®æ®µã®ç§ã§ã‚ã‚Œã°Rè¨€èªžã§ã‚µãƒãƒ¼ãƒˆã•ã‚Œã¦ã„ã‚‹ãƒ‘ãƒƒã‚±ãƒ¼ã‚¸ã‚’ä½¿ã†ã®ã§ã™ãŒã€AIãƒ•ã‚¡ãƒ¼ã‚¹ãƒˆã¨ã„ã†å¤§å·ä»¤ã‚‚å‡ºãŸã“ã¨ãªã®ã§ã€Pythonã®ãƒ©ã‚¤ãƒ–ãƒ©ãƒªã§ä»Šå›žã¯ãã®äºˆè¨€ã®ã»ã©ã‚’è©¦ã—ã¦ã¿ãŸã„
mrkn 2017/06/01
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python

prophet

time_series
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Working with Matplotlib on OSX â€” Matplotlib 3.1.0 documentation
Working with Matplotlib on OSXÂ¶ On OSX, two different types of Python builds exist: a regular build and a framework build. In order to interact correctly with OSX through the native GUI frameworks, you need a framework build of Python. At the time of writing the macosx and WXAgg backends require a framework build to function correctly. This can result in issues for a Python installation not build
mrkn 2017/02/18
--enable-framework ã‚’æŒ‡å®šã™ã‚‹å¿…è¦ãŒã‚ã‚‹

python

matplotlib
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â€” kennethreitz.org
kennethreitz.org Artificial Intelligence Essays Music Photos Poetry Software Talks Contact Kenneth Reitz My Family Great Books Mental Health On Music A Concise List of Personal Values Yoga & Meditation This website is a workâ€“inâ€“progress; it is a place where I share my thoughts, ideas, and projects with the world. Thanks for stopping by! I am a software engineer, writer, and speaker, and I am passi
mrkn 2017/01/24
Python ç•Œã® bundler ã‚’ç›®æŒ‡ã—ã¦ã‚‹ã®ã‹ãªï¼Ÿ

python

pipenv
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Jupyter Notebook Viewer
mrkn 2017/01/18
data_science

visualization

python

pandas
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Edward â€“ Home
A library for probabilistic modeling, inference, and criticism. Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields: Bayesian statistics and mach
mrkn 2016/12/15
python

probabilistic_programming
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http://www.goto.info.waseda.ac.jp/~fukusima/ruby/python-e.html
mrkn 2016/09/24
éŽåŽ»ã«å˜åœ¨ã—ã¦ã„ãŸ Ruby ã« Python ã‚’åŸ‹ã‚ã‚‹ã‚„ã¤

ruby

python
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Quantopian: The Place For Learning Quant Finance
Quantopian provides free education, data, and tools so anyone can pursue quantitative finance.
mrkn 2016/07/26
finance

algorithmic_trading

quantopian

python

zipline
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GitHub - AlpacaTechJP/selectivesearch: Selective Search Implementation for Python
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mrkn 2016/05/16
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computer_vision

python

selective_search
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Python ã‚’é€Ÿãã™ã‚‹å–ã‚Šçµ„ã¿ - methaneã®ãƒ–ãƒã‚°
é€Ÿã„ Python å®Ÿè£…ã¨ã„ãˆã° PyPy ãŒæœ‰åã§ã™ãŒã€ Python 3 ã¸ã®ã‚ãƒ£ãƒƒãƒã‚¢ãƒƒãƒ—ãŒé…ã„ã€ CPython ãŒæŒã£ã¦ã„ã‚‹ Python/C API ã®ã‚µãƒãƒ¼ãƒˆãŒã¾ã å¼±ãé…ã„ã€ãªã©ã®æ¬ ç‚¹ãŒã‚ã‚Šã¾ã™ã€‚ ã¾ãŸã€ Google ã®1å¹´ãƒ—ãƒã‚¸ã‚§ã‚¯ãƒˆã ã£ãŸ Unladen Swallow ã‚‚ã‚ã‚Šã¾ã—ãŸã€‚ã“ã‚Œã¯ CPython ã‚’ãƒ•ã‚©ãƒ¼ã‚¯ã—ã¦ LLVM ã§ JIT ã‚’å®Ÿè£…ã™ã‚‹ã‚‚ã®ã§ã—ãŸã€‚ã“ã® fork å®Ÿè£…ã¯çµ‚ã‚ã‚Šã¾ã—ãŸãŒã€ã“ã®æ™‚æœŸã¾ã ä¸å®‰å®šã ã£ãŸLLVMã¸ã®è²¢çŒ®ã¯å¤§ããã€ï¼ˆã¡ã‚ƒã‚“ã¨ãŠã£ã¦ãªã„ã®ã§æ†¶æ¸¬ã§ã™ãŒï¼‰ç¾ä»£LLVMã‚’åˆ©ç”¨ã—ãŸJITã‚’å®Ÿè£…ã—ã¦ã„ã‚‹ãƒ—ãƒã‚¸ã‚§ã‚¯ãƒˆã¯å…¨éƒ¨é–“æŽ¥çš„ã« Unladen Swallow ã®æˆæžœã®ä¸Šã«æˆã‚Šç«‹ã£ã¦ã„ã‚‹ã¨è¨€ãˆã‚‹ã‹ã‚‚ã—ã‚Œã¾ã›ã‚“ã€‚ çµ‚äº†ã—ãŸ JIT ãƒ—ãƒã‚¸ã‚§ã‚¯ãƒˆã¨ã„ãˆã°ã€ psyco ã‚‚ã‚ã‚Šã¾ã—ãŸã€‚ã“ã‚Œã¯ãƒ™ã‚¿ã« CPython ã® JIT ã‚’å®Ÿè£…ã—ã¦ã„ã¾ã—ãŸãŒã€
mrkn 2016/02/02
python
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Pipelines and Reuse with dask
tl;dr: We use dask to accelerate parameter searches over machine learning pipelines by naming consistently. This work builds on work done with Andreas Mueller, Olivier Grisel, with suggestions by Gael Varoquaux Disclaimer: this bl ogpost discusses experimental and buggy code. OutlineÂ¶Computational costs of machine learning applications are multiplied by the following: Pipelines of several transf orm
mrkn 2016/01/17
python

dask

sklearn

pipeline
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What is Anaconda? - Anaconda
Anaconda DistributionWith over 6 million users, the open source Anaconda Distribution is the fastest and easiest way to do Python and R data science and machine learning on Linux, Windows, and Mac OS X. It's the industry standard for developing, testing, and training on a single machine. Anaconda EnterpriseAnaconda Enterprise is an AI/ML enabl ement platform that empowers organizations to develop,
mrkn 2016/01/06
python

anaconda
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