ãã®ãã©ã¦ã¶ã¼ã¯ãµãã¼ããããªããªãã¾ããã Microsoft Edge ã«ã¢ããã°ã¬ã¼ãããã¨ãææ°ã®æ©è½ãã»ãã¥ãªãã£æ´æ°ããã°ã©ã ãããã³ãã¯ãã«ã« ãµãã¼ããå©ç¨ã§ãã¾ãã
ãã®ãã©ã¦ã¶ã¼ã¯ãµãã¼ããããªããªãã¾ããã Microsoft Edge ã«ã¢ããã°ã¬ã¼ãããã¨ãææ°ã®æ©è½ãã»ãã¥ãªãã£æ´æ°ããã°ã©ã ãããã³ãã¯ãã«ã« ãµãã¼ããå©ç¨ã§ãã¾ãã
æç³»åãã¼ã¿ã使ãããç¯å²ã¯åºããå»çãã¼ã¿ãéèåæãçµæ¸äºæ¸¬ã天æ°äºå ±ãªã©ããã¾ãã¾ãªåéã§ä½¿ããã¦ãã¾ããæ¬æ¸ã¯æç³»åãã¼ã¿ãéãã¦ãã¼ã¿è§£æææ³ãå¦ãã§ããã¢ããã¼ãã§ããã¼ã¿ã®ã¯ãªã¼ãã³ã°ãããããã®æ¹æ³ãå ¥åºåãªã©åºæ¬çãªãããã¯ã«ã¤ãã¦ã²ã¨ã¨ããã«ãã¼ãã¦ããããã¾ãã¾ãªåéã®äºä¾ãæ°å¤ãåãä¸ããçµ±è¨çææ³ã¨æ©æ¢°å¦ç¿ææ³ã®ä¸¡æ¹ãæç³»åãã¼ã¿ã«é©ç¨ããã¾ã人æ°ã®ãªã¼ãã³ã½ã¼ã¹ãã¼ã«ãç©æ¥µçã«åãå ¥ããææ³ãç´¹ä»ãã¾ããããã°ã©ã ã«ã¯Rã¨Pythonã®ä¸¡æ¹ãå©ç¨ããã¼ã¿ã»ãããã³ã¼ãã¯GitHubãããã¦ã³ãã¼ãå¯è½ã§ãã ã¯ããã« 1ç« ãæç³»åã®æ¦è«ã¨ç°¡åãªæ´å² 1.1ãæç³»åã®å¤æ§ãªç¨éã®æ´å² 1.1.1ãæç³»ååé¡ã¨ãã¦ã®å»å¦ 1.1.2ãæ°è±¡äºæ¸¬ 1.1.3ãçµæ¸æé·ã®äºæ¸¬ 1.1.4ã天æå¦ 1.2ãæç³»å解æã®äººæ°ã«ç«ãã¤ã 1.3ãçµ±è¨çæç³»å解æã®èµ·æº 1.4ã
ð Travel around the world as we explore Machine Learning by means of world cultures ð Cloud Advocates at Microsoft are pleased to offer a 12-week, 26-lesson curriculum all about Machine Learning. In this curriculum, you will learn about what is sometimes called classic machine learning, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our AI for Beginners
Streamlitã¯Pythonã ãã§webã¢ããªãä½ããã¨ãã§ãããã¼ã«ï¼ã©ã¤ãã©ãªï¼ã§ããããã³ãã«é¢ããç¥èãã»ã¨ãã©ä¸è¦ãªãããç°¡åãªããã·ã¥ãã¼ãããã¢ã¢ããªãä½ãã®ã«é©ãã¦ãã¾ããå ¬å¼ã®ãã¼ã¸ã§ã¯æ§ã ãªãµã³ãã«ã¢ããªãå ¬éããã¦ãã¾ãã ã¨ããã§æ©æ¢°å¦ç¿ï¼ç¹ã«æ·±å±¤å¦ç¿ï¼ã¢ãã«ã§ã¯ãä¾ãã°ç»å1æãããæ°ç§ã®æ¨è«æéãããããã¨ãããã¾ããStreamlitã¯æ©æ¢°å¦ç¿ã®ãã¢ã¢ããªç¨éã¨ãã¦ãé©ãã¦ããã¨æãã¾ãããæ¨è«ã«æéããããå ´åã«ãã¡ãã¡æ¨è«å®äºãå¾ ã¤ã®ã¯éå±ããããã¾ãããããã§ã¯Pythonã®webãã¬ã¼ã ã¯ã¼ã¯ã§ããFastAPIãçµã¿åããããã¨ã§ãæ¨è«ãéåæã§è¡ãç»åèªèã¢ããªã±ã¼ã·ã§ã³ãä½ãã¾ãã ã³ã¼ãã¯ãã¡ãã«é ç½®ãã¾ããã ã¢ããªå 容 Streamlitã«ããGUIã¯ä»¥ä¸ã®ããã«ãªãã¾ããç»åãã¢ãããã¼ããããSubmitããã¿ã³ãæ¼ããã¨ã§ç»åèªè
memo.sugyan.com ã®è¨äºã®ç¶ã(ï¼)ã ããç¨åº¦ã®å¦ç¿ãã¼ã¿ãåéãã¦å¦ç¿ãããã¢ãã«ãåºæ¥ãã®ã§ãããã使ã£ã¦å®éã«è²ã ãã£ã¦ã¿ãã StyleGAN2-ADA å¦ç¿ mappingåºåã¨çæç»å çæç»åã®å±æ§æ¨å®çµæããæ½å¨ç©ºéã®åããæ½åº 表æ æ¨å® é¡å§¿å¢æ¨å® 髪é åæ¨å® (é¡è§£æ) å¹´é½¢ (ä¸æãããã) è¤å Repository StyleGAN2-ADA ååã®è¨äºã§ãæ¸ãããã©ãå³é¸ãã16,000æã®ç»åã使ã£ã¦ StyleGAN2-ADA ã使ã£ã¦çæã¢ãã«ãå¦ç¿ããã¦ã¿ãã github.com ãã㯠StyleGAN2 ããé²åãããã®ã§ãããå°ãªãææ°ããã§ãå®å®ãã¦å¦ç¿ãæåããããã«ãªã£ã¦ãã¦ãããã«parameteræ°ãªã©èª¿æ´ããã¦å¦ç¿ãæ¨è«ãããæ©ããªã£ã¦ãããã¨ã®ãã¨ã ããã¾ã§ã®StyleGANã·ãªã¼ãºã¯TensorFlowã§å®è£ ãã
ãªãã¼ã¿ã§ãããã¤ãã¿ã¼ã§äººå·¥ç¥è½ã®ãã¨ãä»åªä½ã®è¨äºãªã© ãç´¹ä»ãã¦ãã¾ãã®ã§ã人工ç¥è½ã®ãã¨ããã£ã¨ç¥ãããæ¹ãªã©ã¯ @omiita_atiimoãã覧ãã ããï¼ ä»ã«ã次ã®ãããªè¨äºãæ¸ãã¦ãã¾ãã®ã§èå³ãããã°ãã²ï¼ ã2020決å®çãã¹ã¼ãã¼ããããããæé©åã¢ã«ã´ãªãºã -æ失é¢æ°ããAdamã¨ãã¥ã¼ãã³æ³- ã¤ãã«Adamãè¶ ããï¼ææ°ã®æé©åã¢ã«ã´ãªãºã ãRAdamã解説 æ°ããªæ´»æ§åé¢æ°ãFReLUãèªç&è§£èª¬ï¼ 2019å¹´æå¼·ã®ç»åèªèã¢ãã«EfficientNet解説 ç»åèªèã®å¤§é©å½ãAIçã§è©±é¡ççºä¸ã®ãVision Transformerããè§£èª¬ï¼ SGD+Momentum(ç·)ã¨Adam(赤)ã¨AdaBelief(é)ã®æ¯è¼ãéãä¸çªæ©ãåæãã¦ãããã¨ããããã¾ãã "AdaBelief Optimizer: Adapting Stepsizes by th
æ¦è¦ Google翻訳APIãPythonã§å®è¡ããã§ã¯ãåè¦å «è¦ããªããããGoogle翻訳APIã«ãããããã¹ããã¡ã¤ã«ã«æ¸ãããè±æãæ¥æ¬èªã«ç¿»è¨³ããPythonã¹ã¯ãªãããæ¸ããã å ã ã®åæ©ã¯è«æã®ç¿»è¨³ããéã«ãã¡ã¾ã¡ã¾Google翻訳ã«ã³ããããã®ãé¢åãããã¨ãããã¨ã§ãã£ãã ããã§ä»åã¯ãPythonã¹ã¯ãªãããæ¡å¼µããPDFã®è«æãä¸æ°ã«ç¿»è¨³ããããã«ããã®ã§å ±æãããã ãããããªãã§æ¥æ¬èªã«ç¿»è¨³ãã¦è«æãèªãã®ï¼ ãã¡ãããç´°ããå 容ã¯åæãç²¾èªããå¿ è¦ãããããããããã ã æ¥æ¬èªã§èªãçç±ã¯ãªãã¨ãã£ã¦ããè«æã®å 容ã俯ç°çã«ææ¡ã§ããã¨ãããã¨ã«å°½ããã 俯ç°çã«ææ¡ã§ãããã¨ã§ã以ä¸ã®ã¡ãªãããããã 俯ç°çã«ææ¡ããä¸ã§åæãèªããã¨ã«ãªããããããæ©ãç解ãããã¨ãã§ããã 俯ç°çã«ææ¡ã§ãããããåæãèªãåã«ãèªåã«ã¨ã£ã¦èªãå¿ è¦ãããè«æãã©ããã
ã¢ã«ã´ãªãºã ã®èª¬æ â å調ãã£ã«ã¿ãªã³ã°ã¨ã¯ ã¢ã¤ãã å©ç¨è ã®è¡åå±¥æ´ãå ã«ã¬ã³ã¡ã³ãããæ¹æ³ã§ããAmazonã®ããã®ååãè²·ã£ã人ã¯ããããªååããæ©è½ãæåã§ããå調ãã£ã«ã¿ãªã³ã°ã«ããã¬ã³ã¡ã³ãã¯ã¦ã¼ã¶ã®è¡åãå ã«ã¬ã³ã¡ã³ãããæ¹æ³ã§ãã â å 容ãã¼ã¹ï¼ã³ã³ãã³ããã¼ã¹ï¼ãã£ã«ã¿ãªã³ã°ã¨ã¯ ã¢ã¤ãã ã®ç¹å¾´ãã¯ãã«ã§é¡ä¼¼åº¦ã½ã¼ããã¦ã¬ã³ã¡ã³ãããæ¹æ³ã§ãã ã°ã«ã¡ãµã¤ãã§ã¦ã¼ã¶ãå ¥åãããæ°å®¿ã»ã¨ã¹ããã¯æçãã¨ãããã¼ã¯ã¼ãã«é¢é£ä»ãããããåºã表示ãããå ´åã該å½ãã¾ããå 容ãã¼ã¹ã«ããã¬ã³ã¡ã³ãã¯ã¢ã¤ãã ã®ç¹å¾´ãå ã«ã¬ã³ã¡ã³ãããæ¹æ³ã§ãã ç¹æ§ã®è©³ç´°ã«ã¤ã㦠â å¤æ§æ§ å調: o å 容ãã¼ã¹: x å 容ãã¼ã¹ã§ã¯ååå 容ã«è¨è¼ããã¦ããªãæ å ±ã¯ã¬ã³ã¡ã³ãããã¾ããããå調ãã£ã«ã¿ãªã³ã°ã§ã¯ä»ã®å©ç¨è ãéãã¦ã¬ã³ã¡ã³ããããããèªèº«ããããªãæ å ±ã§ãã¬ã³ã¡ã³ãåºæ¥ã¾ãã
Python ã® scikit-learn(sklearn)ã使ã£ã¦ã©ã³ãã ã»ãã©ã¬ã¹ãåæãè¡ãéã®æ å ±ã«ä¹ããã£ããããã¦ã§ãã«æ²è¼ããã¦ããæ å ±æºã®ç°¡æãªãã¸ããªãã¤ãã£ã¦ã¿ã¾ããã 1. Python sklearnã¢ã¸ã¥ã¼ã«ã§ ã©ã³ãã ã»ãã©ã¬ã¹ãâ¢ã¢ãã« ãè¡ãã³ã¼ãæ¸å¼ tma15 ãã Qiitaè¨äº ãPythonã§Random Forestã使ãã from sklearn.ensemble import RandomForestClassifier trainingdata = [[1, 1], [2, 2], [-1, -1], [-2, -2]] traininglabel = [1, 1, -1, -1] testdata = [[3, 3], [-3, -3]] model = RandomForestClassifier() model.fit(traini
使ç¨ãã¦ãããã¼ã¿ã»ããã¯scikit-learnã®ææ¸ãæåèªèç¨ã®ãã®ã§ãã ä¸ãSCWãä¸ãscikit-learnã®SVCã§å¦ç¿ãåé¡ããçµæã§ããtimeã¯å¦ç¿ã«ããã£ãæéãaccuracyã¯ç²¾åº¦ã表ãã¦ãã¾ãã çµæãè¦ãã°ãããããã«ãSCWã¯é常ã«é«éã«å¦ç¿ãããã¨ãã§ãã¾ãã ã¾ããSCWã¯é次å¦ç¿ãå¯è½ã§ããããªãã¡ããã¼ã¿ãã²ã¨ã¤ãã¤å ¥åãã¦ãå¦ç¿ãããã¨ãã§ãã¾ããã¤ã¾ãããã¼ã¿ãå ¨ã¦ã¡ã¢ãªä¸ã«å±éãã¦å¦ç¿ãããªãã¦ãããã®ã§ãã 精度ã¯ãã¼ã¿ã»ããã«ä¾åãã¾ããã¨ããã®ããSCWã¯ç·å½¢åé¡å¨ã ããã§ãã ç·å½¢åé¢ä¸å¯è½ãªãã¼ã¿ã«å¯¾ãã¦ã¯SCWã§ã¯ç²¾åº¦ãè½ã¡ã¦ãã¾ãã¾ãããç·å½¢åé¢å¯è½ããããã¯ããã«è¿ãããã¡ã§åå¸ãã¦ãããã¼ã¿ã«å¯¾ãã¦ã¯é«ã精度ãå¾ããã¨ãã§ãã¾ãã scikit-learnã®ææ¸ãæåèªèãã¼ã¿ã»ããã¯ç·å½¢åé¢å¯è½ã ã£ãããã§ã精度100%ã¨
2. ç´¹ä» ï½ å²¡  å³â¾¥éï§©æµ Â (æ©â¼¤å¤§ç理⼯工M1) ï½ åºâ¾èº«ãä½ã¾ãç Â Â Â Â Â æ¨ªæµ ï½ è¶£å³âãæ ç»éè³,  ã·ã³ã»  /  kaggleæ´ Â 3ã¶â½æ ï½ å¥½ããªç©âãredbullã¨æè¿ã¯ãã¯ã ï½ @0kayu ç 究   è³ç»åã⽤ç¨ãã診æè£å©æ³ã®éçº 2 3. DEEP LEARNING 1.  Deep  Learning  㮠 ä»çµã¿ ã¹ã©ã¤ã ããããã! 2.  Deep  Learning  ãã©ã¡ã¼ã¿/å¦ç¿æ³  ã«ã¤ã㦠3.  å®è£ :  ããã±ã¼ã¸èª¿ã¹  â ä»æ¥ã¯ãã!!!!!!!!!!! ãã£ã¼ãã©ã¼ãã³ã°ããã£ã¨ããã£ã¨æ軽㫠3
5. ç®æ¬¡ãã¿ã㨠â æ¦å¨ã¨é²å ·ã®è£ å â ãããããªãã¯ã¹ã¸ â 8ã¤ã®éæ³ã®ç¿å¾ â ãªãã£ã¹ã¨ãã¤ã¬ã®é¢ä¿ â ã©ã³ãã¿ã¤ã ã®çµæ¸å¦è ã«ãªãæ¹æ³ â ã¡ã¼ã«ããèªåãçºè¦ãã â å¿èã®é¼å â é³¥ã®ç¾¤ãã表ç¾ãã â ãéã¨æ§å¥ã¨é²å
ãªãªã¼ã¹ãé害æ å ±ãªã©ã®ãµã¼ãã¹ã®ãç¥ãã
ææ°ã®äººæ°ã¨ã³ããªã¼ã®é ä¿¡
å¦çãå®è¡ä¸ã§ã
j次ã®ããã¯ãã¼ã¯
kåã®ããã¯ãã¼ã¯
lãã¨ã§èªã
eã³ã¡ã³ãä¸è¦§ãéã
oãã¼ã¸ãéã
{{#tags}}- {{label}}
{{/tags}}