Batch, Mini-Batch ve Stokastik Gradyan \u0130ni\u015fi<\/title><br \/>\n<\/head><br \/>\n<body><\/p>\n<p>Batch, Mini-Batch ve Stokastik Gradyan \u0130ni\u015fi: Makine \u00d6\u011frenmesinde Optimizasyon Y\u00f6ntemleri<\/p>\n<p>Makine \u00f6\u011frenmesinde, model e\u011fitiminin kalbi gradyan ini\u015f algoritmalar\u0131d\u0131r. Bu algoritmalar, modelin tahminlerinin ger\u00e7ek de\u011ferlere ne kadar yakla\u015ft\u0131\u011f\u0131n\u0131 \u00f6l\u00e7en hata fonksiyonunu minimize ederek \u00e7al\u0131\u015f\u0131r. Ancak, verilerin b\u00fcy\u00fckl\u00fc\u011f\u00fc ve karma\u015f\u0131kl\u0131\u011f\u0131, farkl\u0131 gradyan ini\u015f varyasyonlar\u0131n\u0131n kullan\u0131m\u0131n\u0131 gerektirir. Bu yaz\u0131da, Batch, Mini-Batch ve Stokastik Gradyan \u0130ni\u015fi’ni detayl\u0131 olarak ele alaca\u011f\u0131z ve aralar\u0131ndaki farklar\u0131 a\u00e7\u0131klayaca\u011f\u0131z.<\/p>\n<h2>Gradyan \u0130ni\u015fi Nedir?<\/h2>\n<p>Gradyan ini\u015fi, bir fonksiyonun minimum noktas\u0131n\u0131 bulmak i\u00e7in kullan\u0131lan bir iteratif algoritmad\u0131r. Temel fikir, fonksiyonun gradyan\u0131n\u0131 (e\u011fimini) hesaplayarak, en dik ini\u015f y\u00f6n\u00fcnde k\u00fc\u00e7\u00fck ad\u0131mlar atmakt\u0131r. Bu ad\u0131mlar, hata fonksiyonunu s\u00fcrekli olarak azaltarak, modelin performans\u0131n\u0131 iyile\u015ftirir. Ancak, gradyan ini\u015fi hesaplan\u0131rken t\u00fcm verilerin birden kullan\u0131lmas\u0131 her zaman m\u00fcmk\u00fcn veya verimli de\u011fildir. \u0130\u015fte bu noktada Batch, Mini-Batch ve Stokastik Gradyan \u0130ni\u015fi y\u00f6ntemleri devreye girer.<\/p>\n<h2>Batch Gradyan \u0130ni\u015fi (BGD)<\/h2>\n<p>Batch Gradyan \u0130ni\u015fi, her iterasyonda *t\u00fcm* e\u011fitim verilerini kullanarak gradyan\u0131 hesaplar. Bu y\u00f6ntem, gradyan\u0131n do\u011fru bir tahminini sa\u011flar ve genellikle global minimum’a yak\u0131nsama e\u011filimindedir. Ancak, b\u00fcy\u00fck veri setlerinde hesaplama maliyeti \u00e7ok y\u00fcksektir ve bellekte b\u00fcy\u00fck bir veri k\u00fcmesinin tutulmas\u0131 gerekebilir. Bu nedenle, b\u00fcy\u00fck verilerde zaman al\u0131c\u0131 ve pratik olmayabilir.<\/p>\n<h2>Stokastik Gradyan \u0130ni\u015fi (SGD)<\/h2>\n<p>Stokastik Gradyan \u0130ni\u015fi ise, her iterasyonda *tek bir veri noktas\u0131* kullanarak gradyan\u0131 hesaplar. Bu yakla\u015f\u0131m, hesaplama maliyetini \u00f6nemli \u00f6l\u00e7\u00fcde azalt\u0131r ve b\u00fcy\u00fck veri setlerinde daha h\u0131zl\u0131 yak\u0131nsama sa\u011flar. \u00d6te yandan, g\u00fcr\u00fclt\u00fcl\u00fc gradyan tahminleri nedeniyle, global minimum’a ula\u015fmak yerine, onun etraf\u0131nda bir noktada durma olas\u0131l\u0131\u011f\u0131 daha y\u00fcksektir. Ayr\u0131ca, \u00f6\u011frenme oran\u0131n\u0131n dikkatlice ayarlanmas\u0131 gerekir; aksi takdirde yak\u0131nsama zorla\u015fabilir veya ba\u015far\u0131s\u0131z olabilir.<\/p>\n<h2>Mini-Batch Gradyan \u0130ni\u015fi (MBGD)<\/h2>\n<p>Mini-Batch Gradyan \u0130ni\u015fi, BGD ve SGD’nin avantajlar\u0131n\u0131 birle\u015ftiren bir y\u00f6ntemdir. Her iterasyonda, rastgele se\u00e7ilen bir *veri alt k\u00fcmesi* (mini-batch) kullan\u0131larak gradyan hesaplan\u0131r. Bu yakla\u015f\u0131m, hem hesaplama maliyetini azalt\u0131r hem de g\u00fcr\u00fclt\u00fcy\u00fc azaltarak daha istikrarl\u0131 bir yak\u0131nsama sa\u011flar. Mini-batch boyutu, genellikle 32, 64, 128 veya 256 gibi g\u00fc\u00e7leri iki olan say\u0131larla belirlenir. Bu boyut, deneysel olarak belirlenebilir ve performans \u00fczerinde etkilidir. Uygun mini-batch boyutu se\u00e7imi, hesaplama s\u00fcresi ve yak\u0131nsama h\u0131z\u0131 aras\u0131nda bir denge kurmay\u0131 gerektirir.<\/p>\n<h2>Kar\u015f\u0131la\u015ft\u0131rma Tablosu<\/h2>\n<table>\n<tr>\n<th>Y\u00f6ntem<\/th>\n<th>Gradyan Hesaplama<\/th>\n<th>Hesaplama Maliyeti<\/th>\n<th>Yak\u0131nsama H\u0131z\u0131<\/th>\n<th>G\u00fcr\u00fclt\u00fc<\/th>\n<\/tr>\n<tr>\n<td>Batch GD<\/td>\n<td>T\u00fcm veriler<\/td>\n<td>Y\u00fcksek<\/td>\n<td>Yava\u015f<\/td>\n<td>D\u00fc\u015f\u00fck<\/td>\n<\/tr>\n<tr>\n<td>SGD<\/td>\n<td>Tek veri noktas\u0131<\/td>\n<td>D\u00fc\u015f\u00fck<\/td>\n<td>H\u0131zl\u0131 (ancak g\u00fcr\u00fclt\u00fcl\u00fc)<\/td>\n<td>Y\u00fcksek<\/td>\n<\/tr>\n<tr>\n<td>Mini-Batch GD<\/td>\n<td>Veri alt k\u00fcmesi<\/td>\n<td>Orta<\/td>\n<td>Orta (dengeli)<\/td>\n<td>Orta<\/td>\n<\/tr>\n<\/table>\n<p>Sonu\u00e7 olarak, hangi gradyan ini\u015f y\u00f6nteminin kullan\u0131laca\u011f\u0131, veri setinin b\u00fcy\u00fckl\u00fc\u011f\u00fc, hesaplama kaynaklar\u0131 ve istenen do\u011fruluk seviyesi gibi fakt\u00f6rlere ba\u011fl\u0131d\u0131r. K\u00fc\u00e7\u00fck veri setlerinde BGD tercih edilebilirken, b\u00fcy\u00fck veri setlerinde Mini-Batch GD genellikle en iyi performans\u0131 sa\u011flar. SGD ise h\u0131zl\u0131 bir prototip olu\u015fturma veya \u00e7ok b\u00fcy\u00fck veri setleri i\u00e7in uygun olabilir.<\/p>\n<p>Daha fazla bilgi i\u00e7in <a href=\"https:\/\/fatihsoysal.com\">fatihsoysal.com<\/a> sitesini ziyaret edebilirsiniz. Ayr\u0131ca, konuyla ilgili daha detayl\u0131 kaynaklar i\u00e7in <a href=\"https:\/\/dev.to\/hyperkai\/batch-mini-batch-stochastic-gradient-descent-5ep7\">buraya<\/a> bakabilirsiniz.<\/p>\n<h3>#Etiketler<\/h3>\n<p>#BatchGradyan\u0130ni\u015fi #MiniBatchGradyan\u0130ni\u015fi #StokastikGradyan\u0130ni\u015fi #Makine\u00d6\u011frenmesi #Derin\u00d6\u011frenme #Optimizasyon #Gradyan\u0130ni\u015fi #Algoritma<\/p>\n<p><\/body><br \/>\n<\/html><br \/>\n“`<\/p>\n","protected":false},"excerpt":{"rendered":"“`html Batch, Mini-Batch ve Stokastik Gradyan \u0130ni\u015fi Batch, Mini-Batch ve Stokastik Gradyan \u0130ni\u015fi: Makine \u00d6\u011frenmesinde Optimizasyon Y\u00f6ntemleri Makine…","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-4172","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\n<title>Batch, Mini-Batch ve Stokastik Gradyan \u0130ni\u015fi: Makine \u00d6\u011frenmesinde Optimizasyon Y\u00f6ntemleri<\/title>\n<meta name=\"description\" content=\"Makine \u00f6\u011frenmesinde, model e\u011fitiminin kalbi gradyan ini\u015f algoritmalar\u0131d\u0131r. 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Bu yaz\u0131da, Batch, Mini-Batch ve Stokastik Gradyan \u0130ni\u015fi'ni detayl\u0131 olarak ele alaca\u011f\u0131z ve aralar\u0131ndaki farklar\u0131 a\u00e7\u0131klayaca\u011f\u0131z.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fatihsoysal.com\/blog\/batch-mini-batch-ve-stokastik-gradyan-inisi-makine-ogrenmesinde-optimizasyon-yontemleri\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Batch, Mini-Batch ve Stokastik Gradyan \u0130ni\u015fi: Makine \u00d6\u011frenmesinde Optimizasyon Y\u00f6ntemleri\" \/>\n<meta property=\"og:description\" content=\"Makine \u00f6\u011frenmesinde, model e\u011fitiminin kalbi gradyan ini\u015f algoritmalar\u0131d\u0131r. 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{"id":4172,"date":"2024-11-18T02:30:14","date_gmt":"2024-11-17T23:30:14","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/batch-mini-batch-ve-stokastik-gradyan-inisi-makine-ogrenmesinde-optimizasyon-yontemleri\/"},"modified":"2024-11-18T02:30:14","modified_gmt":"2024-11-17T23:30:14","slug":"batch-mini-batch-ve-stokastik-gradyan-inisi-makine-ogrenmesinde-optimizasyon-yontemleri","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/batch-mini-batch-ve-stokastik-gradyan-inisi-makine-ogrenmesinde-optimizasyon-yontemleri\/","title":{"rendered":"Batch, Mini-Batch ve Stokastik Gradyan \u0130ni\u015fi: Makine \u00d6\u011frenmesinde Optimizasyon Y\u00f6ntemleri"},"content":{"rendered":"

“`html
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\n Batch, Mini-Batch ve Stokastik Gradyan \u0130ni\u015fi<\/title><br \/>\n<\/head><br \/>\n<body><\/p>\n<p>Batch, Mini-Batch ve Stokastik Gradyan \u0130ni\u015fi: Makine \u00d6\u011frenmesinde Optimizasyon Y\u00f6ntemleri<\/p>\n<p>Makine \u00f6\u011frenmesinde, model e\u011fitiminin kalbi gradyan ini\u015f algoritmalar\u0131d\u0131r. Bu algoritmalar, modelin tahminlerinin ger\u00e7ek de\u011ferlere ne kadar yakla\u015ft\u0131\u011f\u0131n\u0131 \u00f6l\u00e7en hata fonksiyonunu minimize ederek \u00e7al\u0131\u015f\u0131r. Ancak, verilerin b\u00fcy\u00fckl\u00fc\u011f\u00fc ve karma\u015f\u0131kl\u0131\u011f\u0131, farkl\u0131 gradyan ini\u015f varyasyonlar\u0131n\u0131n kullan\u0131m\u0131n\u0131 gerektirir. Bu yaz\u0131da, Batch, Mini-Batch ve Stokastik Gradyan \u0130ni\u015fi’ni detayl\u0131 olarak ele alaca\u011f\u0131z ve aralar\u0131ndaki farklar\u0131 a\u00e7\u0131klayaca\u011f\u0131z.<\/p>\n<h2>Gradyan \u0130ni\u015fi Nedir?<\/h2>\n<p>Gradyan ini\u015fi, bir fonksiyonun minimum noktas\u0131n\u0131 bulmak i\u00e7in kullan\u0131lan bir iteratif algoritmad\u0131r. Temel fikir, fonksiyonun gradyan\u0131n\u0131 (e\u011fimini) hesaplayarak, en dik ini\u015f y\u00f6n\u00fcnde k\u00fc\u00e7\u00fck ad\u0131mlar atmakt\u0131r. Bu ad\u0131mlar, hata fonksiyonunu s\u00fcrekli olarak azaltarak, modelin performans\u0131n\u0131 iyile\u015ftirir. Ancak, gradyan ini\u015fi hesaplan\u0131rken t\u00fcm verilerin birden kullan\u0131lmas\u0131 her zaman m\u00fcmk\u00fcn veya verimli de\u011fildir. \u0130\u015fte bu noktada Batch, Mini-Batch ve Stokastik Gradyan \u0130ni\u015fi y\u00f6ntemleri devreye girer.<\/p>\n<h2>Batch Gradyan \u0130ni\u015fi (BGD)<\/h2>\n<p>Batch Gradyan \u0130ni\u015fi, her iterasyonda *t\u00fcm* e\u011fitim verilerini kullanarak gradyan\u0131 hesaplar. Bu y\u00f6ntem, gradyan\u0131n do\u011fru bir tahminini sa\u011flar ve genellikle global minimum’a yak\u0131nsama e\u011filimindedir. Ancak, b\u00fcy\u00fck veri setlerinde hesaplama maliyeti \u00e7ok y\u00fcksektir ve bellekte b\u00fcy\u00fck bir veri k\u00fcmesinin tutulmas\u0131 gerekebilir. Bu nedenle, b\u00fcy\u00fck verilerde zaman al\u0131c\u0131 ve pratik olmayabilir.<\/p>\n<h2>Stokastik Gradyan \u0130ni\u015fi (SGD)<\/h2>\n<p>Stokastik Gradyan \u0130ni\u015fi ise, her iterasyonda *tek bir veri noktas\u0131* kullanarak gradyan\u0131 hesaplar. Bu yakla\u015f\u0131m, hesaplama maliyetini \u00f6nemli \u00f6l\u00e7\u00fcde azalt\u0131r ve b\u00fcy\u00fck veri setlerinde daha h\u0131zl\u0131 yak\u0131nsama sa\u011flar. \u00d6te yandan, g\u00fcr\u00fclt\u00fcl\u00fc gradyan tahminleri nedeniyle, global minimum’a ula\u015fmak yerine, onun etraf\u0131nda bir noktada durma olas\u0131l\u0131\u011f\u0131 daha y\u00fcksektir. Ayr\u0131ca, \u00f6\u011frenme oran\u0131n\u0131n dikkatlice ayarlanmas\u0131 gerekir; aksi takdirde yak\u0131nsama zorla\u015fabilir veya ba\u015far\u0131s\u0131z olabilir.<\/p>\n<h2>Mini-Batch Gradyan \u0130ni\u015fi (MBGD)<\/h2>\n<p>Mini-Batch Gradyan \u0130ni\u015fi, BGD ve SGD’nin avantajlar\u0131n\u0131 birle\u015ftiren bir y\u00f6ntemdir. Her iterasyonda, rastgele se\u00e7ilen bir *veri alt k\u00fcmesi* (mini-batch) kullan\u0131larak gradyan hesaplan\u0131r. Bu yakla\u015f\u0131m, hem hesaplama maliyetini azalt\u0131r hem de g\u00fcr\u00fclt\u00fcy\u00fc azaltarak daha istikrarl\u0131 bir yak\u0131nsama sa\u011flar. Mini-batch boyutu, genellikle 32, 64, 128 veya 256 gibi g\u00fc\u00e7leri iki olan say\u0131larla belirlenir. Bu boyut, deneysel olarak belirlenebilir ve performans \u00fczerinde etkilidir. Uygun mini-batch boyutu se\u00e7imi, hesaplama s\u00fcresi ve yak\u0131nsama h\u0131z\u0131 aras\u0131nda bir denge kurmay\u0131 gerektirir.<\/p>\n<h2>Kar\u015f\u0131la\u015ft\u0131rma Tablosu<\/h2>\n<table>\n<tr>\n<th>Y\u00f6ntem<\/th>\n<th>Gradyan Hesaplama<\/th>\n<th>Hesaplama Maliyeti<\/th>\n<th>Yak\u0131nsama H\u0131z\u0131<\/th>\n<th>G\u00fcr\u00fclt\u00fc<\/th>\n<\/tr>\n<tr>\n<td>Batch GD<\/td>\n<td>T\u00fcm veriler<\/td>\n<td>Y\u00fcksek<\/td>\n<td>Yava\u015f<\/td>\n<td>D\u00fc\u015f\u00fck<\/td>\n<\/tr>\n<tr>\n<td>SGD<\/td>\n<td>Tek veri noktas\u0131<\/td>\n<td>D\u00fc\u015f\u00fck<\/td>\n<td>H\u0131zl\u0131 (ancak g\u00fcr\u00fclt\u00fcl\u00fc)<\/td>\n<td>Y\u00fcksek<\/td>\n<\/tr>\n<tr>\n<td>Mini-Batch GD<\/td>\n<td>Veri alt k\u00fcmesi<\/td>\n<td>Orta<\/td>\n<td>Orta (dengeli)<\/td>\n<td>Orta<\/td>\n<\/tr>\n<\/table>\n<p>Sonu\u00e7 olarak, hangi gradyan ini\u015f y\u00f6nteminin kullan\u0131laca\u011f\u0131, veri setinin b\u00fcy\u00fckl\u00fc\u011f\u00fc, hesaplama kaynaklar\u0131 ve istenen do\u011fruluk seviyesi gibi fakt\u00f6rlere ba\u011fl\u0131d\u0131r. K\u00fc\u00e7\u00fck veri setlerinde BGD tercih edilebilirken, b\u00fcy\u00fck veri setlerinde Mini-Batch GD genellikle en iyi performans\u0131 sa\u011flar. SGD ise h\u0131zl\u0131 bir prototip olu\u015fturma veya \u00e7ok b\u00fcy\u00fck veri setleri i\u00e7in uygun olabilir.<\/p>\n<p>Daha fazla bilgi i\u00e7in <a href=\"https:\/\/fatihsoysal.com\">fatihsoysal.com<\/a> sitesini ziyaret edebilirsiniz. Ayr\u0131ca, konuyla ilgili daha detayl\u0131 kaynaklar i\u00e7in <a href=\"https:\/\/dev.to\/hyperkai\/batch-mini-batch-stochastic-gradient-descent-5ep7\">buraya<\/a> bakabilirsiniz.<\/p>\n<h3>#Etiketler<\/h3>\n<p>#BatchGradyan\u0130ni\u015fi #MiniBatchGradyan\u0130ni\u015fi #StokastikGradyan\u0130ni\u015fi #Makine\u00d6\u011frenmesi #Derin\u00d6\u011frenme #Optimizasyon #Gradyan\u0130ni\u015fi #Algoritma<\/p>\n<p><\/body><br \/>\n<\/html><br \/>\n“`<\/p>\n","protected":false},"excerpt":{"rendered":"“`html Batch, Mini-Batch ve Stokastik Gradyan \u0130ni\u015fi Batch, Mini-Batch ve Stokastik Gradyan \u0130ni\u015fi: Makine \u00d6\u011frenmesinde Optimizasyon Y\u00f6ntemleri Makine…","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-4172","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\n<title>Batch, Mini-Batch ve Stokastik Gradyan \u0130ni\u015fi: Makine \u00d6\u011frenmesinde Optimizasyon Y\u00f6ntemleri<\/title>\n<meta name=\"description\" content=\"Makine \u00f6\u011frenmesinde, model e\u011fitiminin kalbi gradyan ini\u015f algoritmalar\u0131d\u0131r. Bu algoritmalar, modelin tahminlerinin ger\u00e7ek de\u011ferlere ne kadar yakla\u015ft\u0131\u011f\u0131n\u0131 \u00f6l\u00e7en hata fonksiyonunu minimize ederek \u00e7al\u0131\u015f\u0131r. Ancak, verilerin b\u00fcy\u00fckl\u00fc\u011f\u00fc ve karma\u015f\u0131kl\u0131\u011f\u0131, farkl\u0131 gradyan ini\u015f varyasyonlar\u0131n\u0131n kullan\u0131m\u0131n\u0131 gerektirir. Bu yaz\u0131da, Batch, Mini-Batch ve Stokastik Gradyan \u0130ni\u015fi'ni detayl\u0131 olarak ele alaca\u011f\u0131z ve aralar\u0131ndaki farklar\u0131 a\u00e7\u0131klayaca\u011f\u0131z.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fatihsoysal.com\/blog\/batch-mini-batch-ve-stokastik-gradyan-inisi-makine-ogrenmesinde-optimizasyon-yontemleri\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Batch, Mini-Batch ve Stokastik Gradyan \u0130ni\u015fi: Makine \u00d6\u011frenmesinde Optimizasyon Y\u00f6ntemleri\" \/>\n<meta property=\"og:description\" content=\"Makine \u00f6\u011frenmesinde, model e\u011fitiminin kalbi gradyan ini\u015f algoritmalar\u0131d\u0131r. 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