{"id":977,"date":"2024-04-09T02:50:19","date_gmt":"2024-04-08T23:50:19","guid":{"rendered":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/?p=977"},"modified":"2026-10-06T03:08:30","modified_gmt":"2026-10-06T00:08:30","slug":"yapay-zeka-egitimi-nedir","status":"publish","type":"post","link":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/genel\/yapay-zeka-egitimi-nedir\/","title":{"rendered":"Yapay Zeka E\u011fitimi Nedir?"},"content":{"rendered":"
\n

Yapay zek\u00e2 e\u011fitimi, bu alandaki kavramlar\u0131, ara\u00e7lar\u0131 ve uygulama y\u00f6ntemlerini \u00f6\u011frenme s\u00fcrecidir. Model e\u011fitimi ise veriden model parametreleri \u00f6\u011frenilen teknik i\u015flemi ifade eder. Bu rehber yapay zek\u00e2 alan\u0131na ad\u0131m atan geli\u015ftiriciler ve teknik ekipler i\u00e7in \u00f6\u011frenme yolunu ele al\u0131r: programlama, veri, modelleme ve sonu\u00e7 de\u011ferlendirmesi birlikte geli\u015fmelidir.<\/p>\n

\"Programlama
Ara\u00e7 kullanmak ile modelin sonucunu do\u011fru yorumlamak farkl\u0131 becerilerdir.
G\u00f6rseli b\u00fcy\u00fct \u2192<\/a><\/figcaption><\/figure>\n

Hedefinizi uygulama t\u00fcr\u00fcyle belirleyin<\/h2>\n

Tablo verisinden tahmin \u00fcretmek, g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rmak ve metinle \u00e7al\u0131\u015fan bir uygulama geli\u015ftirmek farkl\u0131 ba\u015flang\u0131\u00e7lar gerektirir. \u00d6nce \u00e7\u00f6zmek istedi\u011finiz soruyu ve eldeki veriyi tan\u0131mlay\u0131n. Her proje i\u00e7in b\u00fcy\u00fck bir model e\u011fitmek veya en karma\u015f\u0131k k\u00fct\u00fcphaneyi kullanmak gerekmez.<\/p>\n

\u00d6\u011frenme plan\u0131n\u0131 k\u00fc\u00e7\u00fck ve \u00f6l\u00e7\u00fclebilir \u00e7\u0131kt\u0131lara b\u00f6l\u00fcn: veriyi okumak, basit bir ba\u015flang\u0131\u00e7 modeli kurmak, hata \u00f6rneklerini a\u00e7\u0131klamak ve sonucu tekrar \u00fcretebilmek. Sertifika veya kurs bitirmek bu becerilerin tamam\u0131n\u0131n kazan\u0131ld\u0131\u011f\u0131n\u0131 tek ba\u015f\u0131na g\u00f6stermez.<\/p>\n

Programlama ve veri temeli olu\u015fturun<\/h2>\n

Python\u2019da de\u011fi\u015fken, fonksiyon, veri yap\u0131s\u0131, dosya i\u015flemi ve hata y\u00f6netimini \u00f6\u011frenin. Paket ortamlar\u0131n\u0131 ve s\u00fcr\u00fcmleri kaydetme al\u0131\u015fkanl\u0131\u011f\u0131 kazan\u0131n. Veri tablolar\u0131nda eksik de\u011fer, t\u00fcr d\u00f6n\u00fc\u015f\u00fcm\u00fc ve yinelenen kay\u0131t gibi sorunlar\u0131 fark edebilmek model se\u00e7imi kadar \u00f6nemlidir.<\/p>\n

Temel olas\u0131l\u0131k, istatistik ve do\u011frusal cebir kavramlar\u0131 sonu\u00e7lar\u0131 yorumlamay\u0131 kolayla\u015ft\u0131r\u0131r. Her matematik ayr\u0131nt\u0131s\u0131n\u0131 ba\u015ftan tamamlamak zorunda de\u011filsiniz; kullan\u0131lan y\u00f6ntemin varsay\u0131m\u0131n\u0131 ve de\u011ferlendirme \u00f6l\u00e7\u00fcs\u00fcn\u00fc anlayacak d\u00fczeyde ilerleyin.<\/p>\n

\u0130lk modeli de\u011ferlendirme plan\u0131yla kurun<\/h2>\n

Veriyi e\u011fitim, do\u011frulama ve test ama\u00e7lar\u0131 i\u00e7in uygun bi\u00e7imde ay\u0131r\u0131n. Zaman serisi veya ayn\u0131 ki\u015fiye ait birden fazla kay\u0131t gibi durumlarda rastgele ay\u0131rma yan\u0131lt\u0131c\u0131 olabilir. Test verisinden \u00f6\u011frenen \u00f6n i\u015fleme, veri s\u0131z\u0131nt\u0131s\u0131na ve gere\u011finden iyi g\u00f6r\u00fcnen sonuca yol a\u00e7abilir.<\/p>\n

\u00d6nce basit bir ba\u015flang\u0131\u00e7 y\u00f6ntemi belirleyin. S\u0131n\u0131f da\u011f\u0131l\u0131m\u0131 dengesizse do\u011fruluk y\u00fczdesi tek ba\u015f\u0131na yeterli olmayabilir; hatan\u0131n i\u015f etkisine uygun \u00f6l\u00e7\u00fcler se\u00e7in. Ba\u015far\u0131l\u0131 \u00f6rnekler kadar yanl\u0131\u015f sonu\u00e7lar\u0131 da inceleyin.<\/p>\n

Ara\u00e7lar\u0131 gereksinime g\u00f6re \u00f6\u011frenin<\/h2>\n\n\n\n\n\n\n\n\n
Ara\u00e7 \/ alan<\/th>\n\u00d6\u011frenme amac\u0131<\/th>\n<\/tr>\n<\/thead>\n
Python ve veri ara\u00e7lar\u0131<\/td>\nVeriyi haz\u0131rlama, d\u00f6n\u00fc\u015ft\u00fcrme ve tekrar edilebilir kod.<\/td>\n<\/tr>\n
scikit-learn<\/td>\nKlasik makine \u00f6\u011frenmesi, \u00f6n i\u015fleme ve de\u011ferlendirme ak\u0131\u015flar\u0131.<\/td>\n<\/tr>\n
PyTorch<\/td>\nDerin \u00f6\u011frenme modelleri, tens\u00f6rler ve e\u011fitim d\u00f6ng\u00fcs\u00fc.<\/td>\n<\/tr>\n
Deney kayd\u0131<\/td>\nVeri s\u00fcr\u00fcm\u00fc, parametre, ortam ve sonucu ili\u015fkilendirme.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

GPU baz\u0131 i\u015flerde yararl\u0131 olabilir; k\u00fc\u00e7\u00fck tablo veri projeleri i\u00e7in zorunlu de\u011fildir. Gerekli bellek ve i\u015flem s\u00fcresini \u00f6l\u00e7meden donan\u0131m se\u00e7meyin. Bulut kayna\u011f\u0131 kullan\u0131yorsan\u0131z deney bitti\u011finde veri ve \u00fccretlendirme kapsam\u0131n\u0131 kontrol edin.<\/p>\n

Sorumlu bir proje teslim edin<\/h2>\n

Verinin kullan\u0131m hakk\u0131n\u0131, ki\u015fisel veri kapsam\u0131n\u0131 ve \u00f6rneklemin s\u0131n\u0131rlar\u0131n\u0131 de\u011ferlendirin. Sonucun kimler i\u00e7in ge\u00e7erli oldu\u011funu ve hangi hata t\u00fcrlerinin olu\u015ftu\u011funu a\u00e7\u0131klay\u0131n. \u00dcretim uygulamas\u0131nda veri da\u011f\u0131l\u0131m\u0131 de\u011fi\u015febilir; bir test sonucu kal\u0131c\u0131 performans garantisi de\u011fildir.<\/p>\n

Portf\u00f6y projenizde problem tan\u0131m\u0131, veri kayna\u011f\u0131, kar\u015f\u0131la\u015ft\u0131rma y\u00f6ntemi, sonu\u00e7 ve s\u0131n\u0131rlara yer verin. FibaCloud \u00fczerinde \u00e7al\u0131\u015ft\u0131raca\u011f\u0131n\u0131z deney i\u00e7in kaynak ve i\u015fletim ihtiyac\u0131n\u0131 ayr\u0131 planlay\u0131n; belirli bir e\u011fitim platformunun hizmete haz\u0131r dahil oldu\u011funu varsaymay\u0131n.<\/p>\n

S\u0131k sorulan sorular<\/h2>\n
\n
Yapay zek\u00e2 \u00f6\u011frenmek i\u00e7in GPU \u015fart m\u0131?<\/strong><\/dt>\n
\n

Hay\u0131r. Bir\u00e7ok ba\u015flang\u0131\u00e7 projesi CPU ile yap\u0131labilir. GPU ihtiyac\u0131 model, veri boyutu ve hedef s\u00fcreye ba\u011fl\u0131d\u0131r.<\/p>\n<\/dd>\n

Y\u00fcksek test do\u011frulu\u011fu modelin her yerde iyi \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 g\u00f6sterir mi?<\/strong><\/dt>\n
\n

Hay\u0131r. Veri ay\u0131rma y\u00f6ntemi, \u00f6rneklem ve ger\u00e7ek kullan\u0131m ko\u015fullar\u0131 de\u011ferlendirilmelidir; veri s\u0131z\u0131nt\u0131s\u0131 sonucu yan\u0131ltabilir.<\/p>\n<\/dd>\n<\/dl>\n

Sonraki ad\u0131m<\/h2>\n

\u00d6\u011frendiklerinizi \u00fcretime ta\u015f\u0131mak i\u00e7in bulutta AI ve makine \u00f6\u011frenmesi i\u015fletimi<\/a> ve veri analiti\u011fi hatt\u0131<\/a> rehberlerini inceleyin.<\/p>\n

Kaynaklar<\/h2>\n
    \n
  1. The Python Tutorial \u2014 Python 3.14.8 documentation<\/a><\/li>\n
  2. scikit-learn \u2014 Common pitfalls and recommended practices<\/a><\/li>\n
  3. PyTorch \u2014 Tutorials<\/a><\/li>\n
  4. NIST AI 100-1 \u2014 AI Risk Management Framework 1.0<\/a><\/li>\n<\/ol>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

    Yapay zek\u00e2 e\u011fitimi, bu alandaki kavramlar\u0131, ara\u00e7lar\u0131 ve uygulama y\u00f6ntemlerini \u00f6\u011frenme s\u00fcrecidir. Model e\u011fitimi ise veriden model parametreleri \u00f6\u011frenilen teknik i\u015flemi ifade eder. Bu rehber yapay zek\u00e2 alan\u0131na ad\u0131m atan geli\u015ftiriciler ve teknik ekipler i\u00e7in \u00f6\u011frenme yolunu ele al\u0131r: programlama, veri, modelleme ve sonu\u00e7 de\u011ferlendirmesi birlikte geli\u015fmelidir. Ara\u00e7 kullanmak ile modelin sonucunu do\u011fru yorumlamak farkl\u0131 […]<\/p>\n","protected":false},"author":1,"featured_media":1385,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1,25],"tags":[181,182,180],"class_list":["post-977","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-genel","category-buluta-genel-bakis","tag-chatgpt","tag-learning","tag-yapay-zeka"],"_links":{"self":[{"href":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/wp-json\/wp\/v2\/posts\/977","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/wp-json\/wp\/v2\/comments?post=977"}],"version-history":[{"count":1,"href":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/wp-json\/wp\/v2\/posts\/977\/revisions"}],"predecessor-version":[{"id":979,"href":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/wp-json\/wp\/v2\/posts\/977\/revisions\/979"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/wp-json\/wp\/v2\/media\/1385"}],"wp:attachment":[{"href":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/wp-json\/wp\/v2\/media?parent=977"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/wp-json\/wp\/v2\/categories?post=977"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/wp-json\/wp\/v2\/tags?post=977"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}