{"id":32404,"date":"2025-10-21T09:03:23","date_gmt":"2025-10-21T06:03:23","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/acik-kaynak-dusuk-kodlu-yapay-zeka-araclari-github-yildizlarina-gore-en-iyileri\/"},"modified":"2025-10-21T09:03:23","modified_gmt":"2025-10-21T06:03:23","slug":"acik-kaynak-dusuk-kodlu-yapay-zeka-araclari-github-yildizlarina-gore-en-iyileri","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/acik-kaynak-dusuk-kodlu-yapay-zeka-araclari-github-yildizlarina-gore-en-iyileri\/","title":{"rendered":"A\u00e7\u0131k Kaynak D\u00fc\u015f\u00fck Kodlu Yapay Zeka Ara\u00e7lar\u0131: GitHub Y\u0131ld\u0131zlar\u0131na G\u00f6re En \u0130yileri"},"content":{"rendered":"<p><body><\/p>\n<style>\n  body {\n    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;\n    line-height: 1.7;\n    color: #333;\n    margin: 0;\n    padding: 25px;\n    background-color: #f9f9f9;\n  }\n  h2, h3 {\n    color: #2c3e50;\n    margin-top: 1.5em;\n    margin-bottom: 0.8em;\n  }\n  h2 {\n    border-bottom: 2px solid #3498db;\n    padding-bottom: 10px;\n  }\n  h3 {\n    color: #34495e;\n  }\n  p {\n    margin-bottom: 1em;\n  }\n  ul, ol {\n    margin-bottom: 1em;\n    padding-left: 25px;\n  }\n  li {\n    margin-bottom: 0.5em;\n  }\n  pre {\n    background-color: #e8f4f8;\n    padding: 15px;\n    border-radius: 8px;\n    overflow-x: auto;\n    margin-bottom: 1.5em;\n    font-size: 0.9em;\n    border: 1px solid #d4edf6;\n  }\n  code {\n    font-family: 'Fira Code', 'Cascadia Code', 'Consolas', monospace;\n    color: #c0392b; \/* Example for inline code *\/\n    background-color: #fde6e6;\n    padding: 2px 4px;\n    border-radius: 4px;\n  }\n  pre code {\n    color: #2c3e50;\n    background-color: transparent;\n    padding: 0;\n    border-radius: 0;\n  }\n  table {\n    width: 100%;\n    border-collapse: collapse;\n    margin-bottom: 1.5em;\n    background-color: #fff;\n    box-shadow: 0 2px 5px rgba(0,0,0,0.1);\n  }\n  th, td {\n    border: 1px solid #dee2e6;\n    padding: 12px 15px;\n    text-align: left;\n  }\n  th {\n    background-color: #f2f2f2;\n    font-weight: bold;\n    color: #495057;\n  }\n  tr:nth-child(even) {\n    background-color: #f8f8f8;\n  }\n  .expert-tip {\n    background-color: #e0f7fa;\n    border-left: 6px solid #00bcd4;\n    padding: 20px;\n    margin: 30px 0;\n    border-radius: 8px;\n    font-style: italic;\n    color: #006064;\n    box-shadow: 0 2px 4px rgba(0,0,0,0.08);\n  }\n  .expert-tip strong {\n    color: #00838f;\n  }<\/p>\n<p>  \/* Mobil uyumluluk i\u00e7in medya sorgular\u0131 *\/\n  @media (max-width: 768px) {\n    body {\n      padding: 15px;\n    }\n    h2 {\n      font-size: 1.6em;\n    }\n    h3 {\n      font-size: 1.3em;\n    }\n    table, thead, tbody, th, td, tr {\n      display: block;\n    }\n    thead tr {\n      position: absolute;\n      top: -9999px;\n      left: -9999px;\n    }\n    tr {\n      border: 1px solid #ccc;\n      margin-bottom: 15px;\n      display: block;\n      background-color: #fff;\n      box-shadow: 0 2px 5px rgba(0,0,0,0.1);\n    }\n    td {\n      border: none;\n      border-bottom: 1px solid #eee;\n      position: relative;\n      padding-left: 50%;\n      text-align: right;\n      min-height: 40px; \/* Ensure enough height for content *\/\n      display: flex;\n      align-items: center;\n      justify-content: flex-end;\n    }\n    td:last-child {\n      border-bottom: none;\n    }\n    td:before {\n      position: absolute;\n      top: 0;\n      left: 0;\n      width: 45%;\n      padding-left: 10px;\n      white-space: nowrap;\n      text-align: left;\n      font-weight: bold;\n      color: #555;\n      height: 100%;\n      display: flex;\n      align-items: center;\n      background-color: #f2f2f2;\n      border-right: 1px solid #eee;\n    }\n    \/* \u00d6rnek tablo ba\u015fl\u0131klar\u0131 i\u00e7in i\u00e7erik *\/\n    td:nth-of-type(1):before { content: \"Ba\u015fl\u0131k:\"; }\n    td:nth-of-type(2):before { content: \"A\u00e7\u0131klama:\"; }\n    td:nth-of-type(3):before { content: \"Durum:\"; }<\/p>\n<p>    .expert-tip {\n      padding: 15px;\n      margin: 20px 0;\n    }\n  }\n<\/style>\n<p>Yapay zeka (YZ) teknolojileri, g\u00fcn\u00fcm\u00fcz i\u015f d\u00fcnyas\u0131nda ve g\u00fcnl\u00fck ya\u015fant\u0131m\u0131zda \u00e7\u0131\u011f\u0131r a\u00e7an d\u00f6n\u00fc\u015f\u00fcmler yarat\u0131yor. Ancak bu teknolojileri geli\u015ftirmek genellikle derinlemesine kodlama bilgisi gerektiriyor. Peki ya bu karma\u015f\u0131k d\u00fcnyay\u0131, kod yazma y\u00fck\u00fcn\u00fc hafifleterek ke\u015ffetmek m\u00fcmk\u00fcn olsayd\u0131? \u0130\u015fte &#8220;d\u00fc\u015f\u00fck kodlu&#8221; ve &#8220;kodsuz&#8221; yapay zeka ara\u00e7lar\u0131 tam da bu noktada devreye giriyor.<\/p>\n<p>D\u00fc\u015f\u00fck kodlu (low-code) ve kodsuz (no-code) yapay zeka platformlar\u0131, makine \u00f6\u011frenimi modelleri olu\u015fturmay\u0131, e\u011fitmeyi ve da\u011f\u0131tmay\u0131, minimal veya hi\u00e7 kodlama bilgisi olmadan ger\u00e7ekle\u015ftirmeye olanak tan\u0131yan ara\u00e7lard\u0131r. Bu platformlar genellikle g\u00f6rsel aray\u00fczler, s\u00fcr\u00fckle-b\u0131rak fonksiyonlar\u0131 ve \u00f6nceden tan\u0131mlanm\u0131\u015f \u015fablonlar sunarak teknik olmayan kullan\u0131c\u0131lar\u0131n bile karma\u015f\u0131k YZ projelerine giri\u015f yapmas\u0131n\u0131 sa\u011flar. Geleneksel YZ geli\u015ftirme s\u00fcre\u00e7leri, Python veya R gibi dillerde uzmanl\u0131k, \u00e7e\u015fitli k\u00fct\u00fcphaneler (TensorFlow, PyTorch, Scikit-learn) hakk\u0131nda derin bilgi ve veri bilimi metodolojilerine hakimiyet gerektirir. Ancak d\u00fc\u015f\u00fck kodlu yakla\u015f\u0131mlar, bu teknik engelleri ortadan kald\u0131rarak YZ&#8217;yi daha geni\u015f kitlelere ula\u015ft\u0131r\u0131r.<\/p>\n<p>Bu yakla\u015f\u0131m\u0131n \u00f6nemi birka\u00e7 temel fakt\u00f6re dayan\u0131r. \u00d6ncelikle, geli\u015ftirme s\u00fcrecini h\u0131zland\u0131r\u0131r. \u0130\u015fletmeler, fikirleri \u00e7ok daha k\u0131sa s\u00fcrede prototiplere d\u00f6n\u00fc\u015ft\u00fcrebilir ve pazar testlerini h\u0131zland\u0131rabilirler. \u0130kinci olarak, maliyetleri d\u00fc\u015f\u00fcr\u00fcr. Daha az uzmanl\u0131k gerektiren bir s\u00fcre\u00e7 oldu\u011fu i\u00e7in, YZ ekipleri daha k\u00fc\u00e7\u00fck olabilir veya mevcut i\u015f analistleri ve domain uzmanlar\u0131 YZ \u00e7\u00f6z\u00fcmleri geli\u015ftirmeye dahil olabilir. \u00dc\u00e7\u00fcnc\u00fc olarak, inovasyonu demokratikle\u015ftirir. Art\u0131k sadece b\u00fcy\u00fck teknoloji \u015firketleri veya y\u00fcksek b\u00fct\u00e7eli Ar-Ge departmanlar\u0131 de\u011fil, KOB\u0130&#8217;ler ve bireysel geli\u015ftiriciler de kendi YZ tabanl\u0131 \u00e7\u00f6z\u00fcmlerini \u00fcretebilirler. Bu da sekt\u00f6rler aras\u0131 yenilikleri te\u015fvik eder ve rekabeti art\u0131r\u0131r. Sonu\u00e7 olarak, d\u00fc\u015f\u00fck kodlu ve kodsuz YZ ara\u00e7lar\u0131, yapay zekay\u0131 bir l\u00fcks olmaktan \u00e7\u0131kar\u0131p, her b\u00fcy\u00fckl\u00fckteki organizasyon ve birey i\u00e7in eri\u015filebilir bir araca d\u00f6n\u00fc\u015ft\u00fcrmektedir.<\/p>\n<h3>A\u00e7\u0131k Kaynak Yakla\u015f\u0131m\u0131n\u0131n Avantajlar\u0131 Nelerdir?<\/h3>\n<p>D\u00fc\u015f\u00fck kodlu YZ d\u00fcnyas\u0131nda a\u00e7\u0131k kaynak \u00e7\u00f6z\u00fcmlerin \u00f6zel bir yeri vard\u0131r ve sunduklar\u0131 avantajlar g\u00f6z ard\u0131 edilemez. A\u00e7\u0131k kaynak ara\u00e7lar, topluluk taraf\u0131ndan geli\u015ftirilen, \u015feffaf, esnek ve genellikle \u00fccretsiz veya \u00e7ok uygun maliyetli platformlard\u0131r. Temelde, kod tabanlar\u0131 herkes taraf\u0131ndan incelenebilir, de\u011fi\u015ftirilebilir ve geli\u015ftirilebilir durumdad\u0131r. Bu durum, \u00f6zellikle g\u00fcvenlik ve \u015feffafl\u0131k arayan i\u015fletmeler i\u00e7in b\u00fcy\u00fck bir g\u00fcvence sa\u011flar. Bir kodun nas\u0131l \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 anlamak, potansiyel g\u00fcvenlik a\u00e7\u0131klar\u0131n\u0131 tespit etmek veya belirli bir i\u015f ihtiyac\u0131na g\u00f6re \u00f6zelle\u015ftirmeler yapmak, a\u00e7\u0131k kaynakl\u0131 yaz\u0131l\u0131mlarla \u00e7ok daha kolayd\u0131r.<\/p>\n<p>Ayr\u0131ca, a\u00e7\u0131k kaynak topluluklar\u0131, projelerin canl\u0131l\u0131\u011f\u0131n\u0131 ve geli\u015fimini s\u00fcrekli olarak destekler. Geli\u015ftiriciler, kar\u015f\u0131la\u015ft\u0131klar\u0131 sorunlara h\u0131zl\u0131 \u00e7\u00f6z\u00fcmler bulabilir, yeni \u00f6zellik taleplerinde bulunabilir ve hatta kendileri katk\u0131da bulunarak ara\u00e7lar\u0131n geli\u015fimine y\u00f6n verebilirler. Bu i\u015fbirlik\u00e7i ortam, ara\u00e7lar\u0131n h\u0131zla olgunla\u015fmas\u0131na, \u00e7e\u015fitli kullan\u0131m senaryolar\u0131na uyum sa\u011flamas\u0131na ve teknolojik yenilikleri b\u00fcnyesine katmas\u0131na olanak tan\u0131r. Lisans maliyetlerinin olmamas\u0131 da b\u00fct\u00e7e k\u0131s\u0131tlamas\u0131 olan k\u00fc\u00e7\u00fck ve orta \u00f6l\u00e7ekli i\u015fletmeler (KOB\u0130&#8217;ler) i\u00e7in YZ teknolojilerine eri\u015fim kap\u0131s\u0131n\u0131 aralar. Sonu\u00e7 olarak, a\u00e7\u0131k kaynak d\u00fc\u015f\u00fck kodlu YZ ara\u00e7lar\u0131; maliyet etkinli\u011fi, esneklik, \u015feffafl\u0131k, g\u00fc\u00e7l\u00fc topluluk deste\u011fi ve ba\u011f\u0131ms\u0131zl\u0131k gibi kritik avantajlar sunarak, YZ ekosisteminin demokratikle\u015fmesinde kilit bir rol oynamaktad\u0131r. Bu ara\u00e7lar, geli\u015ftiricilere ve i\u015fletmelere kendi YZ yolculuklar\u0131nda daha fazla kontrol ve g\u00fc\u00e7 sa\u011flar.<\/p>\n<aside class=\"expert-tip\">\n  <strong>Uzman \u0130pucu:<\/strong> A\u00e7\u0131k kaynakl\u0131 ara\u00e7lar\u0131 se\u00e7erken, sadece GitHub y\u0131ld\u0131z say\u0131s\u0131na de\u011fil, ayn\u0131 zamanda aktif geli\u015ftirici toplulu\u011funun b\u00fcy\u00fckl\u00fc\u011f\u00fcne, dok\u00fcmantasyonun kalitesine ve en son g\u00fcncelleme tarihlerine de dikkat edin. Canl\u0131 bir topluluk, uzun vadeli destek ve s\u00fcrd\u00fcr\u00fclebilirlik anlam\u0131na gelir.<br \/>\n<\/aside>\n<h2>GitHub Y\u0131ld\u0131zlar\u0131na G\u00f6re \u00d6ne \u00c7\u0131kan A\u00e7\u0131k Kaynak D\u00fc\u015f\u00fck Kodlu Yapay Zeka Ara\u00e7lar\u0131<\/h2>\n<p>A\u00e7\u0131k kaynak d\u00fcnyas\u0131nda bir projenin pop\u00fclaritesi ve g\u00fcvenilirli\u011fi genellikle GitHub \u00fczerindeki y\u0131ld\u0131z say\u0131s\u0131 ile \u00f6l\u00e7\u00fcl\u00fcr. Bu metrik, geli\u015ftirici toplulu\u011funun bir araca olan ilgisini ve katk\u0131 potansiyelini g\u00f6sterir. D\u00fc\u015f\u00fck kodlu yapay zeka alan\u0131nda da bir\u00e7ok g\u00fc\u00e7l\u00fc ve yenilik\u00e7i a\u00e7\u0131k kaynak ara\u00e7 bulunmaktad\u0131r. Bu b\u00f6l\u00fcmde, GitHub&#8217;daki etkile\u015fim ve pop\u00fclaritelerine g\u00f6re \u00f6ne \u00e7\u0131kan 11 a\u00e7\u0131k kaynak d\u00fc\u015f\u00fck kodlu YZ arac\u0131n\u0131 inceleyece\u011fiz. Bu ara\u00e7lar, farkl\u0131 ihtiya\u00e7lara y\u00f6nelik \u00e7\u00f6z\u00fcmler sunarak, veri analistlerinden i\u015f analistlerine kadar geni\u015f bir yelpazede kullan\u0131c\u0131lara hitap etmektedir.<\/p>\n<p>\u0130lk olarak, g\u00f6rsel i\u015f ak\u0131\u015f\u0131 tabanl\u0131 yakla\u015f\u0131mlar\u0131yla \u00f6ne \u00e7\u0131kan platformlar dikkat \u00e7ekiyor. <strong>KNIME Analytics Platform<\/strong>, veri bilimcilerin ve i\u015f analistlerinin, g\u00f6rsel bir aray\u00fczle veri entegrasyonundan analize, makine \u00f6\u011frenimi modelleri olu\u015fturmaktan raporlamaya kadar her ad\u0131m\u0131 y\u00f6netmesini sa\u011flayan kapsaml\u0131 bir ara\u00e7t\u0131r. S\u00fcr\u00fckle-b\u0131rak \u00f6zelli\u011fi sayesinde kullan\u0131c\u0131lar karma\u015f\u0131k veri i\u015fleme ve analiz boru hatlar\u0131n\u0131 kolayca in\u015fa edebilirler. Benzer \u015fekilde, <strong>Orange Data Mining<\/strong>, interaktif veri g\u00f6rselle\u015ftirme, istatistiksel analiz ve makine \u00f6\u011frenimi i\u00e7in tasarlanm\u0131\u015f a\u00e7\u0131k kaynakl\u0131 bir yaz\u0131l\u0131m paketidir. Kullan\u0131c\u0131 dostu aray\u00fcz\u00fc ve geni\u015f bile\u015fen k\u00fct\u00fcphanesi ile h\u0131zl\u0131 prototipleme ve ke\u015fifsel veri analizi i\u00e7in idealdir. Her iki ara\u00e7 da \u00f6zellikle e\u011fitim ve akademik \u00e7evrelerde pop\u00fcler olmakla birlikte, i\u015f d\u00fcnyas\u0131nda da karma\u015f\u0131k g\u00f6revleri basitle\u015ftirmeleriyle tercih edilmektedir.<\/p>\n<p>Bir di\u011fer \u00f6nemli kategori, deklaratif veya otomatikle\u015ftirilmi\u015f makine \u00f6\u011frenimi (AutoML) yakla\u015f\u0131mlar\u0131n\u0131 benimseyen ara\u00e7lard\u0131r. Uber taraf\u0131ndan geli\u015ftirilen <strong>Ludwig<\/strong>, kullan\u0131c\u0131lar\u0131n derin \u00f6\u011frenme modellerini sadece bir YAML dosyas\u0131yla tan\u0131mlamas\u0131na olanak tan\u0131yan d\u00fc\u015f\u00fck kodlu bir framework&#8217;t\u00fcr. Bu sayede, geleneksel kodlama yerine model mimarisi ve veri \u00f6znitelikleri \u00fczerine odaklanarak h\u0131zl\u0131ca prototip olu\u015fturulabilir. Amazon&#8217;un <strong>AutoGluon<\/strong>&#8216;u ise minimal kodla y\u00fcksek performansl\u0131 makine \u00f6\u011frenimi modelleri olu\u015fturmay\u0131 hedefleyen bir AutoML k\u00fct\u00fcphanesidir. Geli\u015ftiricilerin \u00e7ok az m\u00fcdahale ile g\u00fc\u00e7l\u00fc modeller e\u011fitebilmesine olanak tan\u0131r, bu da veri bilimcilerin verimlili\u011fini \u00f6nemli \u00f6l\u00e7\u00fcde art\u0131r\u0131r. Apple&#8217;\u0131n <strong>Turi Create<\/strong>&#8216;i de benzer \u015fekilde, \u00f6neri sistemleri, resim s\u0131n\u0131fland\u0131rma ve nesne alg\u0131lama gibi yayg\u0131n makine \u00f6\u011frenimi g\u00f6revlerini basitle\u015ftiren bir framework olarak \u00f6ne \u00e7\u0131kar. Genellikle Swift veya Python ile kullan\u0131lsa da, sundu\u011fu y\u00fcksek seviyeli API&#8217;ler sayesinde olduk\u00e7a d\u00fc\u015f\u00fck kodlu bir geli\u015ftirme deneyimi sunar.<\/p>\n<p>Veritaban\u0131 ve da\u011f\u0131t\u0131m entegrasyonlar\u0131na odaklanan ara\u00e7lar da giderek pop\u00fclerle\u015fiyor. <strong>MindsDB<\/strong>, mevcut veritaban\u0131n\u0131za makine \u00f6\u011frenimi yetenekleri ekleyen a\u00e7\u0131k kaynakl\u0131 bir ara\u00e7t\u0131r. SQL sorgular\u0131 kullanarak tahmin modelleri olu\u015fturabilir, e\u011fitebilir ve bunlardan tahminler alabilirsiniz. Bu, veritaban\u0131 uzmanlar\u0131n\u0131n ML modellerini kolayca entegre etmesini sa\u011flar. Yeni nesil ara\u00e7lardan <strong>SuperDuperDB<\/strong> de benzer bir mant\u0131kla \u00e7al\u0131\u015farak, YZ modellerini do\u011frudan veritabanlar\u0131na g\u00f6merek daha ak\u0131ll\u0131 ve reaktif veri sistemleri olu\u015fturulmas\u0131na yard\u0131mc\u0131 olur. Bu sayede YZ i\u015flevselli\u011fi, veri katman\u0131nda do\u011fal bir bile\u015fen haline gelir. YZ modellerini h\u0131zl\u0131 bir \u015fekilde web aray\u00fcz\u00fcne ta\u015f\u0131mak isteyenler i\u00e7in <strong>Streamlit<\/strong> ve <strong>Gradio<\/strong> vazge\u00e7ilmezdir. Bu k\u00fct\u00fcphaneler, basit Python kodlar\u0131yla interaktif web uygulamalar\u0131 olu\u015fturarak makine \u00f6\u011frenimi modellerini demosunu yapmay\u0131 ve payla\u015fmay\u0131 son derece kolayla\u015ft\u0131r\u0131r. \u00d6zellikle YZ modellerini teknik olmayan payda\u015flara sunmak veya h\u0131zl\u0131 prototipleme yapmak i\u00e7in idealdirler.<\/p>\n<p>Son olarak, belirli YZ alanlar\u0131na odaklanm\u0131\u015f g\u00fc\u00e7l\u00fc platformlar da mevcuttur. <strong>DeepPavlov<\/strong>, \u00f6zellikle sohbet botlar\u0131 ve diyalog sistemleri olu\u015fturmak i\u00e7in tasarlanm\u0131\u015f a\u00e7\u0131k kaynakl\u0131 bir k\u00fct\u00fcphanedir. Yap\u0131land\u0131r\u0131labilir bile\u015fenleri sayesinde, do\u011fal dil anlama (NLU) ve do\u011fal dil \u00fcretimi (NLG) g\u00f6revlerini d\u00fc\u015f\u00fck kodla ger\u00e7ekle\u015ftirmeyi m\u00fcmk\u00fcn k\u0131lar. <strong>H2O-3<\/strong>, makine \u00f6\u011frenimi i\u00e7in da\u011f\u0131t\u0131k, in-memory bir platformdur ve geli\u015fmi\u015f algoritmalar\u0131 ve otomatikle\u015ftirilmi\u015f ML (AutoML) \u00f6zellikleriyle b\u00fcy\u00fck veri k\u00fcmeleri \u00fczerinde \u00f6l\u00e7eklenebilir model geli\u015ftirme imkan\u0131 sunar. D\u00fc\u015f\u00fck kodlu aray\u00fczleri sayesinde, YZ modellerini h\u0131zl\u0131ca olu\u015fturmak ve de\u011ferlendirmek m\u00fcmk\u00fcnd\u00fcr. T\u00fcm bu ara\u00e7lar, farkl\u0131 ihtiya\u00e7lara y\u00f6nelik \u00e7\u00f6z\u00fcmler sunarak a\u00e7\u0131k kaynak d\u00fc\u015f\u00fck kodlu YZ ekosistemini zenginle\u015ftirmektedir.<\/p>\n<h2>Bu Ara\u00e7lar\u0131 Kendi Projelerinizde Nas\u0131l Kullan\u0131rs\u0131n\u0131z?<\/h2>\n<p>D\u00fc\u015f\u00fck kodlu yapay zeka ara\u00e7lar\u0131n\u0131 projenize entegre etmek, do\u011fru ad\u0131mlar\u0131 izledi\u011finizde olduk\u00e7a basittir. Ba\u015flang\u0131\u00e7ta yapman\u0131z gereken en \u00f6nemli \u015fey, projenizin amac\u0131n\u0131 ve ula\u015fmak istedi\u011finiz hedefi net bir \u015fekilde belirlemektir. \u00d6rne\u011fin, m\u00fc\u015fteri kayb\u0131n\u0131 tahmin etmek mi istiyorsunuz, yoksa resimlerden belirli nesneleri mi tan\u0131mlamak? Amac\u0131n\u0131z netle\u015ftikten sonra, projenize en uygun d\u00fc\u015f\u00fck kodlu arac\u0131 se\u00e7meniz gerekecektir. G\u00f6rsel i\u015f ak\u0131\u015f\u0131na m\u0131 ihtiyac\u0131n\u0131z var (KNIME, Orange gibi), yoksa deklaratif bir yakla\u015f\u0131mla h\u0131zl\u0131ca model mi kurmak istiyorsunuz (Ludwig gibi)? Ya da sadece bir modelin aray\u00fcz\u00fcn\u00fc m\u00fc olu\u015fturacaks\u0131n\u0131z (Streamlit, Gradio)? Se\u00e7im, projenizin do\u011fas\u0131na ve teknik yeterlili\u011finize ba\u011fl\u0131 olacakt\u0131r. Ard\u0131ndan, ilgili arac\u0131n kurulum talimatlar\u0131n\u0131 takip ederek platformu kullan\u0131ma haz\u0131r hale getirebilirsiniz. \u00c7o\u011fu a\u00e7\u0131k kaynak ara\u00e7, detayl\u0131 dok\u00fcmantasyon ve kolay kurulum s\u00fcre\u00e7leri sunar.<\/p>\n<h3>Veri Haz\u0131rl\u0131\u011f\u0131 ve Model E\u011fitimi Ad\u0131mlar\u0131 Nelerdir?<\/h3>\n<p>Her yapay zeka projesinin temelini veriler olu\u015fturur. Bu nedenle, d\u00fc\u015f\u00fck kodlu bir YZ arac\u0131 kullan\u0131yor olsan\u0131z bile, veri haz\u0131rl\u0131\u011f\u0131 ad\u0131m\u0131 kritik \u00f6neme sahiptir. \u0130lk olarak, kullanaca\u011f\u0131n\u0131z veriyi toplaman\u0131z ve gerekli \u00f6n i\u015fleme ad\u0131mlar\u0131n\u0131 uygulaman\u0131z gerekir. Bu genellikle eksik de\u011ferleri doldurma, ayk\u0131r\u0131 de\u011ferleri temizleme, veri tiplerini d\u00f6n\u00fc\u015ft\u00fcrme ve \u00f6zellik m\u00fchendisli\u011fi gibi ad\u0131mlar\u0131 i\u00e7erir. KNIME ve Orange gibi g\u00f6rsel ara\u00e7lar, bu ad\u0131mlar\u0131 s\u00fcr\u00fckle-b\u0131rak bile\u015fenleriyle kolayca yapman\u0131z\u0131 sa\u011flar. Verileriniz haz\u0131r oldu\u011funda, model e\u011fitimine ge\u00e7ebilirsiniz. D\u00fc\u015f\u00fck kodlu ara\u00e7larda bu, genellikle bir model t\u00fcr\u00fc se\u00e7mek (\u00f6rne\u011fin, s\u0131n\u0131fland\u0131rma, regresyon), hiperparametreleri ayarlamak ve modeli e\u011fit d\u00fc\u011fmesine basmak kadar basittir. \u00d6rne\u011fin, AutoGluon gibi bir ara\u00e7ta sadece verinizi sa\u011flaman\u0131z yeterlidir; ara\u00e7 sizin i\u00e7in en iyi modelleri otomatik olarak bulup e\u011fitir. Model e\u011fitiminden sonra, modelin performans\u0131n\u0131 de\u011ferlendirmek i\u00e7in test verileri \u00fczerinde do\u011fruluk, kesinlik, geri \u00e7a\u011f\u0131rma gibi metrikleri analiz etmeniz gerekecektir. Bu metrikler, modelinizin ger\u00e7ek d\u00fcnya verileri \u00fczerinde ne kadar iyi performans g\u00f6sterdi\u011fini anlaman\u0131za yard\u0131mc\u0131 olur.<\/p>\n<h3>Ger\u00e7ek D\u00fcnya Senaryolar\u0131nda Uygulama \u00d6rnekleri Nelerdir?<\/h3>\n<p>D\u00fc\u015f\u00fck kodlu yapay zeka ara\u00e7lar\u0131, \u00e7e\u015fitli ger\u00e7ek d\u00fcnya problemlerine \u00e7\u00f6z\u00fcm sunmak i\u00e7in kullan\u0131labilir. \u00d6rne\u011fin, bir e-ticaret \u015firketi Orange Data Mining kullanarak m\u00fc\u015fteri davran\u0131\u015flar\u0131n\u0131 analiz edebilir ve hangi \u00fcr\u00fcnlerin birlikte sat\u0131n al\u0131nma olas\u0131l\u0131\u011f\u0131n\u0131n y\u00fcksek oldu\u011funu tahmin eden bir \u00f6neri sistemi geli\u015ftirebilir. Pazarlama departmanlar\u0131, MindsDB arac\u0131l\u0131\u011f\u0131yla SQL sorgular\u0131 kullanarak m\u00fc\u015fteri segmentasyonunu otomatikle\u015ftirebilir ve hedefli reklam kampanyalar\u0131 olu\u015fturabilir. Bir finans kurumu, KNIME kullanarak sahtekarl\u0131k tespit modelleri geli\u015ftirebilir; \u015f\u00fcpheli i\u015flemleri otomatik olarak belirleyip insan incelemesine y\u00f6nlendirebilir. M\u00fc\u015fteri hizmetleri departmanlar\u0131, DeepPavlov ile d\u00fc\u015f\u00fck kodlu bir sohbet botu olu\u015fturarak s\u0131k\u00e7a sorulan sorular\u0131 yan\u0131tlayabilir, b\u00f6ylece insan operat\u00f6rlerin y\u00fck\u00fcn\u00fc azaltabilir. \u00dcretim sekt\u00f6r\u00fcnde ise Turi Create, \u00fcr\u00fcn kalitesi kontrol\u00fc i\u00e7in otomatik g\u00f6rsel denetim sistemleri geli\u015ftirmek amac\u0131yla kullan\u0131labilir. \u00d6rne\u011fin, bir \u00fcr\u00fcn\u00fcn y\u00fczeyindeki kusurlar\u0131 tespit eden bir model h\u0131zl\u0131ca e\u011fitilebilir. Bu \u00f6rnekler, d\u00fc\u015f\u00fck kodlu yapay zeka ara\u00e7lar\u0131n\u0131n sekt\u00f6rler aras\u0131 nas\u0131l kolayca entegre edilerek de\u011ferli sonu\u00e7lar \u00fcretebilece\u011fini g\u00f6stermektedir. Bu ara\u00e7lar, karma\u015f\u0131k YZ projelerini daha eri\u015filebilir ve uygulanabilir hale getirir.<\/p>\n<pre><code>\n\/\/ MindsDB ile bir tahmin modeli olu\u015fturma (SQL benzeri sentaks)\nCREATE MODEL customer_churn_model\nFROM database_name (SELECT * FROM customers)\nPREDICT churn\nUSING ENGINE = 'lightwood';\n\n\/\/ Streamlit ile basit bir YZ aray\u00fcz\u00fc \u00f6rne\u011fi (Python)\nimport streamlit as st\nimport pandas as pd\nfrom sklearn.ensemble import RandomForestClassifier\n\nst.title(\"M\u00fc\u015fteri Kayb\u0131 Tahmin Uygulamas\u0131\")\nst.write(\"M\u00fc\u015fteri verilerini girerek kay\u0131p riskini \u00f6\u011frenin.\")\n\n# \u00d6rnek veri giri\u015fi\ncol1, col2 = st.columns(2)\nwith col1:\n    tenure = st.slider(\"M\u00fc\u015fteri S\u00fcresi (Ay)\", 1, 72, 36)\n    monthly_charges = st.number_input(\"Ayl\u0131k \u00dccret\", 0.0, 150.0, 50.0)\nwith col2:\n    total_charges = st.number_input(\"Toplam \u00dccret\", 0.0, 5000.0, 1000.0)\n    contract_type = st.selectbox(\"S\u00f6zle\u015fme Tipi\", [\"Month-to-month\", \"One year\", \"Two year\"])\n\n# Basit bir model e\u011fitimi (ger\u00e7ek uygulamada daha karma\u015f\u0131k olur)\ndata = {'tenure': [tenure], 'MonthlyCharges': [monthly_charges], \n        'TotalCharges': [total_charges], 'Contract': [contract_type]}\ninput_df = pd.DataFrame(data)\n\n# S\u00f6zle\u015fme tipini say\u0131sal hale d\u00f6n\u00fc\u015ft\u00fcrme (modelin anlayaca\u011f\u0131 format)\ncontract_mapping = {\"Month-to-month\": 0, \"One year\": 1, \"Two year\": 2}\ninput_df['Contract'] = input_df['Contract'].map(contract_mapping)\n\n# Dummy model (ger\u00e7ek bir model y\u00fcklenmeli)\nmodel = RandomForestClassifier(random_state=42)\n# \u00d6rnek e\u011fitim verisi (ger\u00e7ek bir e\u011fitim seti ile e\u011fitilmeli)\nX_train = pd.DataFrame([[10, 30.0, 300.0, 0], [60, 100.0, 4000.0, 2]], \n                       columns=['tenure', 'MonthlyCharges', 'TotalCharges', 'Contract'])\ny_train = [1, 0] # 1: churn, 0: no churn\nmodel.fit(X_train, y_train)\n\nif st.button(\"Tahmin Et\"):\n    prediction = model.predict(input_df)\n    prediction_proba = model.predict_proba(input_df)\n\n    if prediction[0] == 1:\n        st.error(f\"M\u00fc\u015fteri Kay\u0131p Riski Y\u00fcksek! (%{prediction_proba[0][1]*100:.2f})\")\n    else:\n        st.success(f\"M\u00fc\u015fterinin Kalma \u0130htimali Y\u00fcksek. (%{prediction_proba[0][0]*100:.2f})\")\n<\/pre>\n<p><\/code><\/p>\n<h2>\u0130leri D\u00fczey Uygulamalar ve D\u00fc\u015f\u00fcn\u00fclmesi Gerekenler<\/h2>\n<p>D\u00fc\u015f\u00fck kodlu yapay zeka ara\u00e7lar\u0131, temel modelleri h\u0131zl\u0131ca olu\u015fturman\u0131n \u00f6tesinde, daha ileri d\u00fczey uygulamalar i\u00e7in de zemin haz\u0131rlar. Deneyimli kullan\u0131c\u0131lar, bu platformlar\u0131 sadece bir ba\u015flang\u0131\u00e7 noktas\u0131 olarak g\u00f6rebilir ve mevcut yeteneklerini geni\u015fletmek i\u00e7in \u00e7e\u015fitli teknikler uygulayabilirler. \u00d6rne\u011fin, otomatikle\u015ftirilmi\u015f makine \u00f6\u011frenimi (AutoML) \u00f6zelliklerini kullanarak farkl\u0131 model mimarilerini ve hiperparametre kombinasyonlar\u0131n\u0131 otomatik olarak ke\u015ffedebilir, b\u00f6ylece en iyi performans\u0131 g\u00f6steren modeli daha az manuel \u00e7abayla bulabilirler. Ayr\u0131ca, platformun sundu\u011fu entegrasyon yeteneklerinden faydalanarak farkl\u0131 veri kaynaklar\u0131n\u0131 bir araya getirebilir ve daha zengin \u00f6zellik setleri olu\u015fturabilirler. D\u00fc\u015f\u00fck kodlu ara\u00e7lar genellikle API entegrasyonlar\u0131na izin verdi\u011fi i\u00e7in, modelleri mevcut i\u015f sistemlerine ba\u011flayarak ger\u00e7ek zamanl\u0131 tahminler veya otomasyonlar ger\u00e7ekle\u015ftirmek de m\u00fcmk\u00fcnd\u00fcr. Ancak, bu t\u00fcr ileri d\u00fczey kullan\u0131mlar i\u00e7in bile, YZ modellerinin etik etkilerini, veri gizlili\u011fi endi\u015felerini ve modelin a\u00e7\u0131klanabilirli\u011fini g\u00f6z \u00f6n\u00fcnde bulundurmak hayati \u00f6neme sahiptir. Modellerin \"kara kutu\" olmaktan \u00e7\u0131kar\u0131l\u0131p, kararlar\u0131n\u0131n nedenlerini a\u00e7\u0131klayabilir olmas\u0131, \u00f6zellikle hassas alanlarda g\u00fcven in\u015fa etmek i\u00e7in kritik bir ad\u0131md\u0131r.<\/p>\n<h3>Performans ve \u00d6l\u00e7eklenebilirlik \u0130pu\u00e7lar\u0131 Nelerdir?<\/h3>\n<p>D\u00fc\u015f\u00fck kodlu YZ modellerinin performans\u0131n\u0131 optimize etmek ve b\u00fcy\u00fck veri setleri veya y\u00fcksek trafikli uygulamalar i\u00e7in \u00f6l\u00e7eklenebilirli\u011fini sa\u011flamak, dikkatli planlama gerektirir. \u0130lk olarak, veri \u00f6n i\u015fleme ad\u0131mlar\u0131nda verimlili\u011fe odaklanmal\u0131s\u0131n\u0131z. B\u00fcy\u00fck veri setleri ile \u00e7al\u0131\u015f\u0131rken, veri temizli\u011fi ve d\u00f6n\u00fc\u015ft\u00fcrme i\u015flemlerinin optimize edilmesi, model e\u011fitim s\u00fcresini \u00f6nemli \u00f6l\u00e7\u00fcde k\u0131saltabilir. Performans iyile\u015ftirmesi i\u00e7in, d\u00fc\u015f\u00fck kodlu ara\u00e7lar\u0131n sundu\u011fu hiperparametre optimizasyonu \u00f6zelliklerini (\u00f6rne\u011fin, \u0131zgara arama, rastgele arama, Bayes optimizasyonu) aktif olarak kullanmal\u0131s\u0131n\u0131z. Bu sayede modelinizin en iyi parametrelerle \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flayabilirsiniz. \u00d6l\u00e7eklenebilirlik a\u00e7\u0131s\u0131ndan ise, se\u00e7ti\u011finiz a\u00e7\u0131k kaynak arac\u0131n da\u011f\u0131t\u0131k i\u015flem yeteneklerini destekleyip desteklemedi\u011fini kontrol edin. H2O-3 gibi platformlar, b\u00fcy\u00fck veri k\u00fcmeleri \u00fczerinde da\u011f\u0131t\u0131k makine \u00f6\u011frenimi i\u00e7in tasarlanm\u0131\u015ft\u0131r. Modellerinizi bir bulut ortam\u0131nda (AWS, Google Cloud, Azure) da\u011f\u0131tmay\u0131 d\u00fc\u015f\u00fcn\u00fcyorsan\u0131z, bu platformlar\u0131n d\u00fc\u015f\u00fck kodlu YZ ara\u00e7lar\u0131yla uyumlulu\u011funu ara\u015ft\u0131rman\u0131z faydal\u0131 olacakt\u0131r. Son olarak, model izleme ve yeniden e\u011fitim s\u00fcre\u00e7lerini otomatikle\u015ftirmek, zaman i\u00e7inde model performans\u0131n\u0131n d\u00fc\u015fmesini (model drift) engellemek i\u00e7in kritik bir ad\u0131md\u0131r. Modelinizin zamanla de\u011fi\u015fen veri da\u011f\u0131l\u0131mlar\u0131na uyum sa\u011flamas\u0131 i\u00e7in d\u00fczenli olarak yeniden e\u011fitilmesi gerekmektedir.<\/p>\n<h3>Topluluk G\u00fcc\u00fcnden Nas\u0131l Yararlan\u0131l\u0131r?<\/h3>\n<p>A\u00e7\u0131k kaynak d\u00fc\u015f\u00fck kodlu YZ ara\u00e7lar\u0131n\u0131n en b\u00fcy\u00fck avantajlar\u0131ndan biri, arkalar\u0131ndaki g\u00fc\u00e7l\u00fc ve aktif topluluktur. Bu topluluklardan en iyi \u015fekilde yararlanmak, \u00f6\u011frenme s\u00fcrecinizi h\u0131zland\u0131r\u0131r, kar\u015f\u0131la\u015ft\u0131\u011f\u0131n\u0131z sorunlara \u00e7\u00f6z\u00fcm bulman\u0131z\u0131 sa\u011flar ve en yeni geli\u015fmeleri takip etmenize yard\u0131mc\u0131 olur. Ba\u015flang\u0131\u00e7 olarak, arac\u0131n GitHub deposunu (repository) inceleyin; \"Issues\" b\u00f6l\u00fcm\u00fc genellikle s\u0131k\u00e7a sorulan sorulara ve olas\u0131 sorun giderme y\u00f6ntemlerine dair zengin bir kaynak sunar. Ayr\u0131ca, projenin Slack, Discord sunucular\u0131na veya forumlar\u0131na kat\u0131larak do\u011frudan di\u011fer kullan\u0131c\u0131larla ve geli\u015ftiricilerle etkile\u015fime ge\u00e7ebilirsiniz. Bu platformlar, teknik sorular sormak, deneyimlerinizi payla\u015fmak ve yeni fikirler edinmek i\u00e7in harika yerlerdir. Topluluk taraf\u0131ndan d\u00fczenlenen webinarlara, at\u00f6lye \u00e7al\u0131\u015fmalar\u0131na ve konferanslara kat\u0131lmak da bilginizi derinle\u015ftirmenin ve a\u011f\u0131n\u0131z\u0131 geni\u015fletmenin etkili yollar\u0131d\u0131r. E\u011fer bir konuda uzmanla\u015fmaya ba\u015flarsan\u0131z, kendiniz de toplulu\u011fa katk\u0131da bulunmay\u0131 d\u00fc\u015f\u00fcnebilirsiniz. \u00d6rne\u011fin, dok\u00fcmantasyonu geli\u015ftirerek, hata raporlar\u0131 g\u00f6ndererek veya hatta kod katk\u0131s\u0131nda bulunarak aktif bir \u00fcye olmak, hem kendi \u00f6\u011freniminizi peki\u015ftirir hem de projenin geli\u015fimine yard\u0131mc\u0131 olur. Unutmay\u0131n, a\u00e7\u0131k kaynak k\u00fclt\u00fcr\u00fc \"ver ve al\" prensibine dayan\u0131r; ne kadar katk\u0131da bulunursan\u0131z, o kadar \u00e7ok fayda sa\u011flars\u0131n\u0131z.<\/p>\n<h2>Sonu\u00e7: Gelece\u011fin Yapay Zeka Geli\u015ftirme Yakla\u015f\u0131m\u0131 ve S\u0131k\u00e7a Sorulan Sorular<\/h2>\n<p>D\u00fc\u015f\u00fck kodlu ve kodsuz yapay zeka ara\u00e7lar\u0131, yapay zeka geli\u015ftirme s\u00fcre\u00e7lerini demokratikle\u015ftirerek, teknik bilgiye sahip olmayan bireylerin ve KOB\u0130'lerin bile bu g\u00fc\u00e7l\u00fc teknolojiden faydalanmas\u0131n\u0131n \u00f6n\u00fcn\u00fc a\u00e7m\u0131\u015ft\u0131r. GitHub y\u0131ld\u0131zlar\u0131na g\u00f6re \u00f6ne \u00e7\u0131kan a\u00e7\u0131k kaynakl\u0131 \u00e7\u00f6z\u00fcmler, esneklik, \u015feffafl\u0131k, maliyet etkinli\u011fi ve g\u00fc\u00e7l\u00fc topluluk deste\u011fi gibi avantajlar\u0131yla YZ ekosisteminde kritik bir rol oynamaktad\u0131r. KNIME, Orange, Ludwig, AutoGluon, MindsDB, Streamlit gibi ara\u00e7lar, g\u00f6rsel i\u015f ak\u0131\u015flar\u0131ndan deklaratif modellere, veritaban\u0131 entegrasyonlar\u0131ndan kullan\u0131c\u0131 aray\u00fcz\u00fc geli\u015ftirmeye kadar geni\u015f bir yelpazede \u00e7\u00f6z\u00fcmler sunar. Bu ara\u00e7lar, veri haz\u0131rl\u0131\u011f\u0131ndan model da\u011f\u0131t\u0131m\u0131na kadar t\u00fcm YZ ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fc basitle\u015ftirirken, ayn\u0131 zamanda geli\u015ftiricilere karma\u015f\u0131k problemler \u00fczerinde \u00e7al\u0131\u015fma ve yenilik yapma \u00f6zg\u00fcrl\u00fc\u011f\u00fc tan\u0131r. Gelecekte, YZ'nin daha da yayg\u0131nla\u015fmas\u0131yla birlikte d\u00fc\u015f\u00fck kodlu yakla\u015f\u0131mlar\u0131n \u00f6nemi artacak, i\u015f analistleri ve sekt\u00f6r uzmanlar\u0131 gibi teknik olmayan rollerdeki ki\u015filerin YZ projelerine daha fazla dahil olmas\u0131 beklenmektedir. Bu d\u00f6n\u00fc\u015f\u00fcm, YZ'nin sadece teknoloji departmanlar\u0131n\u0131n de\u011fil, her i\u015f biriminin bir par\u00e7as\u0131 haline gelmesini sa\u011flayacakt\u0131r.<\/p>\n<p>Bu makale boyunca, a\u00e7\u0131k kaynak d\u00fc\u015f\u00fck kodlu YZ'nin temel kavramlar\u0131n\u0131, \u00f6ne \u00e7\u0131kan ara\u00e7lar\u0131n\u0131, pratik kullan\u0131m senaryolar\u0131n\u0131 ve ileri d\u00fczey ipu\u00e7lar\u0131n\u0131 ele ald\u0131k. Unutulmamal\u0131d\u0131r ki, en iyi ara\u00e7, projenizin \u00f6zel ihtiya\u00e7lar\u0131na ve ekibinizin yeteneklerine en uygun olan\u0131d\u0131r. Bu teknolojileri benimsemek, organizasyonlar\u0131n daha \u00e7evik olmas\u0131n\u0131, inovasyonu h\u0131zland\u0131rmas\u0131n\u0131 ve YZ'nin sundu\u011fu potansiyelden tam olarak yararlanmas\u0131n\u0131 sa\u011flayacakt\u0131r.<\/p>\n<h3>S\u0131k\u00e7a Sorulan Sorular<\/h3>\n<dl>\n<dt>D\u00fc\u015f\u00fck kodlu YZ ara\u00e7lar\u0131, geleneksel kodlama yakla\u015f\u0131mlar\u0131n\u0131n yerini tamamen alacak m\u0131?<\/dt>\n<dd>Hay\u0131r, d\u00fc\u015f\u00fck kodlu YZ ara\u00e7lar\u0131 geleneksel kodlama yakla\u015f\u0131mlar\u0131n\u0131n yerini tamamen almayacakt\u0131r. Aksine, birbirini tamamlay\u0131c\u0131 niteliktedirler. D\u00fc\u015f\u00fck kodlu ara\u00e7lar h\u0131zl\u0131 prototipleme ve tekrarlayan g\u00f6revler i\u00e7in idealdir, ancak benzersiz ve \u00e7ok \u00f6zel YZ \u00e7\u00f6z\u00fcmleri geli\u015ftirmek veya mevcut d\u00fc\u015f\u00fck kodlu ara\u00e7lar\u0131n s\u0131n\u0131rlar\u0131n\u0131 zorlamak i\u00e7in hala derinlemesine kodlama bilgisine ihtiya\u00e7 duyulacakt\u0131r. Hibrit yakla\u015f\u0131mlar gelecekte daha da yayg\u0131nla\u015facakt\u0131r.<\/dd>\n<dt>A\u00e7\u0131k kaynak d\u00fc\u015f\u00fck kodlu YZ ara\u00e7lar\u0131 ticari projelerde kullan\u0131labilir mi?<\/dt>\n<dd>Evet, \u00e7o\u011fu a\u00e7\u0131k kaynak d\u00fc\u015f\u00fck kodlu YZ arac\u0131, uygun lisans ko\u015fullar\u0131 (\u00f6rne\u011fin MIT, Apache 2.0, GPL) alt\u0131nda ticari projelerde kullan\u0131labilir. Ancak, her arac\u0131n lisans\u0131n\u0131 dikkatlice incelemeniz \u00f6nemlidir, \u00e7\u00fcnk\u00fc baz\u0131 lisanslar belirli k\u0131s\u0131tlamalar (\u00f6rne\u011fin, t\u00fcretilmi\u015f eserlerin de a\u00e7\u0131k kaynak olmas\u0131 zorunlulu\u011fu) i\u00e7erebilir. Genel olarak, a\u00e7\u0131k kaynak projeler, ticari kullan\u0131m i\u00e7in b\u00fcy\u00fck esneklik sunar.<\/dd>\n<dt>Bu ara\u00e7larla geli\u015ftirilen modellerin performans\u0131, uzmanlar taraf\u0131ndan kodlanm\u0131\u015f modellere g\u00f6re nas\u0131ld\u0131r?<\/dt>\n<dd>D\u00fc\u015f\u00fck kodlu YZ ara\u00e7lar\u0131, \u00e7o\u011fu durumda olduk\u00e7a y\u00fcksek performansl\u0131 modeller olu\u015fturabilir, \u00f6zellikle de AutoML \u00f6zelliklerine sahip olanlar. Bu ara\u00e7lar, karma\u015f\u0131k algoritmalar\u0131 ve en iyi uygulamalar\u0131 otomatik olarak uygulayarak, uzmanlar taraf\u0131ndan manuel olarak yap\u0131lan optimizasyonlara yak\u0131n sonu\u00e7lar verebilir. Ancak, \u00e7ok \u00f6zel ve ni\u015f problemlerde veya y\u00fcksek performans gerektiren kritik uygulamalarda, \u00f6zel olarak tasarlanm\u0131\u015f ve optimize edilmi\u015f kodlanm\u0131\u015f modeller k\u00fc\u00e7\u00fck bir avantaja sahip olabilir. Genel i\u015f ihtiya\u00e7lar\u0131 i\u00e7in d\u00fc\u015f\u00fck kodlu modeller fazlas\u0131yla yeterlidir.<\/dd>\n<dt>D\u00fc\u015f\u00fck kodlu YZ'nin gelece\u011fi neye benziyor?<\/dt>\n<dd>D\u00fc\u015f\u00fck kodlu YZ'nin gelece\u011fi olduk\u00e7a parlak. Yapay zeka teknolojileri daha karma\u015f\u0131k hale geldik\u00e7e, bu karma\u015f\u0131kl\u0131\u011f\u0131 soyutlayan ve daha eri\u015filebilir hale getiren ara\u00e7lara olan ihtiya\u00e7 artacakt\u0131r. Daha fazla otomasyon, daha ak\u0131ll\u0131 aray\u00fczler ve mevcut i\u015f sistemleriyle daha sorunsuz entegrasyonlar, d\u00fc\u015f\u00fck kodlu YZ platformlar\u0131n\u0131n gelecekteki geli\u015fim y\u00f6nlerini olu\u015fturacakt\u0131r. YZ'nin daha fazla i\u015f birimine yay\u0131lmas\u0131yla birlikte, d\u00fc\u015f\u00fck kodlu ara\u00e7lar inovasyonun temel itici g\u00fc\u00e7lerinden biri haline gelecektir.<\/dd>\n<\/dl>\n<p><\/body><\/p>\n","protected":false},"excerpt":{"rendered":"Yapay zeka (YZ) teknolojileri, g\u00fcn\u00fcm\u00fcz i\u015f d\u00fcnyas\u0131nda ve g\u00fcnl\u00fck ya\u015fant\u0131m\u0131zda \u00e7\u0131\u011f\u0131r a\u00e7an d\u00f6n\u00fc\u015f\u00fcmler yarat\u0131yor. Ancak bu teknolojileri geli\u015ftirmek&hellip;","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":[1401],"tags":[],"class_list":{"0":"post-32404","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-git","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.5 (Yoast SEO v25.3.1) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>A\u00e7\u0131k Kaynak D\u00fc\u015f\u00fck Kodlu Yapay Zeka Ara\u00e7lar\u0131: GitHub Y\u0131ld\u0131zlar\u0131na G\u00f6re En \u0130yileri<\/title>\n<meta name=\"description\" content=\"Yapay zeka (YZ) teknolojileri, g\u00fcn\u00fcm\u00fcz i\u015f d\u00fcnyas\u0131nda ve g\u00fcnl\u00fck ya\u015fant\u0131m\u0131zda \u00e7\u0131\u011f\u0131r a\u00e7an d\u00f6n\u00fc\u015f\u00fcmler yarat\u0131yor. Ancak bu teknolojileri geli\u015ftirmek genellikle derinlemesine kodlama bilgisi gerektiriyor. Peki ya bu karma\u015f\u0131k d\u00fcnyay\u0131, kod yazma y\u00fck\u00fcn\u00fc hafifleterek ke\u015ffetmek m\u00fcmk\u00fcn olsayd\u0131? \u0130\u015fte &quot;d\u00fc\u015f\u00fck kodlu&quot; ve &quot;kodsuz&quot; yapay zeka ara\u00e7lar\u0131 tam da bu noktada devreye giriyor.\" \/>\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\/acik-kaynak-dusuk-kodlu-yapay-zeka-araclari-github-yildizlarina-gore-en-iyileri\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"A\u00e7\u0131k Kaynak D\u00fc\u015f\u00fck Kodlu Yapay Zeka Ara\u00e7lar\u0131: GitHub Y\u0131ld\u0131zlar\u0131na G\u00f6re En \u0130yileri\" \/>\n<meta property=\"og:description\" content=\"Yapay zeka (YZ) teknolojileri, g\u00fcn\u00fcm\u00fcz i\u015f d\u00fcnyas\u0131nda ve g\u00fcnl\u00fck ya\u015fant\u0131m\u0131zda \u00e7\u0131\u011f\u0131r a\u00e7an d\u00f6n\u00fc\u015f\u00fcmler yarat\u0131yor. 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