Yapay zeka d\u00fcnyas\u0131, \u00f6zellikle \u00fcretken yapay zeka (GenAI) alan\u0131ndaki ba\u015f d\u00f6nd\u00fcr\u00fcc\u00fc geli\u015fmelerle birlikte, yaz\u0131l\u0131m m\u00fchendisli\u011fi kariyerlerinin \u00e7ehresini h\u0131zla de\u011fi\u015ftiriyor. Bir zamanlar f\u00fct\u00fcristik g\u00f6r\u00fcnen kavramlar, art\u0131k g\u00fcnl\u00fck yaz\u0131l\u0131m geli\u015ftirme s\u00fcre\u00e7lerinin ayr\u0131lmaz bir par\u00e7as\u0131 haline gelmekte. 2026 y\u0131l\u0131na do\u011fru ilerlerken, geleneksel yaz\u0131l\u0131m m\u00fchendislerinin GenAI m\u00fchendisli\u011fine ge\u00e7i\u015f yapmas\u0131 sadece bir se\u00e7enek olmaktan \u00e7\u0131k\u0131p, rekabet\u00e7i kalmak ve kariyerlerini ileriye ta\u015f\u0131mak i\u00e7in bir zorunluluk haline geliyor. Bu makale, bu heyecan verici d\u00f6n\u00fc\u015f\u00fcm i\u00e7in pratik bir yol haritas\u0131 sunmaktad\u0131r.<\/p>\n
\u00dcretken yapay zeka (Generative AI), metin, g\u00f6r\u00fcnt\u00fc, kod ve di\u011fer medya t\u00fcrlerini insan benzeri kalitede \u00fcretebilen modelleri kapsar. ChatGPT, DALL-E gibi ara\u00e7larla hayat\u0131m\u0131za giren bu teknoloji, yaz\u0131l\u0131m geli\u015ftirme, i\u00e7erik olu\u015fturma, ara\u015ft\u0131rma ve daha bir\u00e7ok alanda devrim yarat\u0131yor. Bu d\u00f6n\u00fc\u015f\u00fcm, yaz\u0131l\u0131m m\u00fchendisleri i\u00e7in hem zorluklar hem de e\u015fsiz f\u0131rsatlar sunmaktad\u0131r.<\/p>\n
GenAI, otomasyonun \u00f6tesine ge\u00e7erek yarat\u0131c\u0131l\u0131\u011f\u0131 ve problem \u00e7\u00f6zme yetene\u011fini makinelere ta\u015f\u0131yor. Bu, yeni \u00fcr\u00fcnler, hizmetler ve i\u015f modelleri i\u00e7in kap\u0131lar a\u00e7\u0131yor. Yaz\u0131l\u0131m m\u00fchendisleri, bu yeni paradigmada \u00f6nc\u00fc roller \u00fcstlenerek, GenAI destekli uygulamalar tasarlayabilir, geli\u015ftirebilir ve da\u011f\u0131tabilirler. Bu, sadece mevcut i\u015fleri optimize etmekle kalmay\u0131p, tamamen yeni deneyimler yaratma potansiyeli ta\u015f\u0131r.<\/p>\n
Geleneksel yaz\u0131l\u0131m m\u00fchendisli\u011fi rolleri, GenAI’nin y\u00fckseli\u015fiyle birlikte evriliyor. Art\u0131k sadece kod yazmak de\u011fil, ayn\u0131 zamanda yapay zeka modelleriyle etkile\u015fim kurmak, onlar\u0131n \u00e7\u0131kt\u0131lar\u0131n\u0131 yorumlamak ve bu modelleri mevcut sistemlere entegre etmek de \u00f6nem kazan\u0131yor. Bu, m\u00fchendislerin problem \u00e7\u00f6zme yakla\u015f\u0131mlar\u0131n\u0131 geni\u015fletmelerini ve yeni ara\u00e7 setlerine hakim olmalar\u0131n\u0131 gerektiriyor.<\/p>\n
Pazar analistleri, GenAI pazar\u0131n\u0131n \u00f6n\u00fcm\u00fczdeki y\u0131llarda katlanarak b\u00fcy\u00fcyece\u011fini \u00f6ng\u00f6r\u00fcyor. Bu b\u00fcy\u00fcme, GenAI yeteneklerine sahip m\u00fchendislere olan talebi de art\u0131racak. 2026’ya kadar, \u015firketlerin b\u00fcy\u00fck bir k\u0131sm\u0131 GenAI \u00e7\u00f6z\u00fcmlerini benimsemi\u015f olacak ve bu alanda uzmanla\u015fm\u0131\u015f profesyoneller, kariyerlerinde \u00f6nemli bir avantaja sahip olacaklar. Bu nedenle, bug\u00fcnden yat\u0131r\u0131m yapmak, gelece\u011fin i\u015f g\u00fcc\u00fcnde yerinizi sa\u011flamla\u015ft\u0131rmak anlam\u0131na geliyor.<\/p>\n
GenAI m\u00fchendisi olmak i\u00e7in sa\u011flam bir temel, genel yapay zeka ve makine \u00f6\u011frenimi prensiplerini anlamaktan ge\u00e7er. Bu, sadece API’leri kullanman\u0131n \u00f6tesinde, modellerin nas\u0131l \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 kavramay\u0131 gerektirir.<\/p>\n
Do\u011frusal cebir, kalk\u00fcl\u00fcs, olas\u0131l\u0131k ve istatistik, makine \u00f6\u011freniminin temelini olu\u015fturur. Bu konulara hakim olmak, algoritmalar\u0131n i\u00e7 i\u015fleyi\u015fini anlaman\u0131za, model performans\u0131n\u0131 de\u011ferlendirmenize ve olas\u0131 sorunlar\u0131 gidermenize yard\u0131mc\u0131 olur. Derinlemesine bir matematik\u00e7i olman\u0131za gerek yok, ancak temel kavramlar\u0131 anlamak kritik \u00f6neme sahiptir.<\/p>\n
Denetimli \u00f6\u011frenme (regresyon, s\u0131n\u0131fland\u0131rma), denetimsiz \u00f6\u011frenme (k\u00fcmeleme) ve takviyeli \u00f6\u011frenme gibi temel makine \u00f6\u011frenimi algoritmalar\u0131n\u0131 \u00f6\u011frenmek, GenAI modellerinin alt\u0131nda yatan prensipleri anlaman\u0131za yard\u0131mc\u0131 olur. Karar a\u011fa\u00e7lar\u0131, SVM’ler, lojistik regresyon gibi klasik algoritmalar\u0131 bilmek, daha karma\u015f\u0131k derin \u00f6\u011frenme modellerine ge\u00e7i\u015finizi kolayla\u015ft\u0131racakt\u0131r.<\/p>\n
Python, yapay zeka ve makine \u00f6\u011frenimi alan\u0131nda de facto standart programlama dilidir. NumPy (say\u0131sal i\u015flemler), Pandas (veri manip\u00fclasyonu) ve Scikit-learn (makine \u00f6\u011frenimi algoritmalar\u0131) gibi k\u00fct\u00fcphanelere hakim olmak, veri i\u015fleme, model e\u011fitimi ve de\u011ferlendirme i\u00e7in vazge\u00e7ilmezdir. Bu k\u00fct\u00fcphaneler, GenAI projelerinizde de s\u0131k\u00e7a kullanaca\u011f\u0131n\u0131z temel ara\u00e7lard\u0131r.<\/p>\n
\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import accuracy_score\n\n# \u00d6rnek veri seti olu\u015fturma\ndata = {'feature1': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n 'feature2': [10, 9, 8, 7, 6, 5, 4, 3, 2, 1],\n 'target': [0, 0, 0, 0, 1, 1, 1, 1, 1, 1]}\ndf = pd.DataFrame(data)\n\nX = df[['feature1', 'feature2']]\ny = df['target']\n\n# Veriyi e\u011fitim ve test setlerine ay\u0131rma\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n\n# Lojistik regresyon modeli olu\u015fturma ve e\u011fitme\nmodel = LogisticRegression()\nmodel.fit(X_train, y_train)\n\n# Tahmin yapma ve do\u011fruluk skorunu hesaplama\ny_pred = model.predict(X_test)\nprint(f\"Model Do\u011frulu\u011fu: {accuracy_score(y_test, y_pred):.2f}\")\n<\/pre>\n<\/code><\/p>\n
3. \u00dcretken Yapay Zeka (GenAI) \u00c7ekirdek Konular\u0131 ve Ara\u00e7lar\u0131<\/h3>\n
Temelleri att\u0131ktan sonra, GenAI'nin kalbine inmenin zaman\u0131 geldi. Bu b\u00f6l\u00fcm, GenAI'yi di\u011fer ML alanlar\u0131ndan ay\u0131ran temel kavramlara odaklanmaktad\u0131r.<\/p>\n
3.1. B\u00fcy\u00fck Dil Modelleri (LLM'ler) ve Mimarileri<\/h4>\n
Transformer mimarisi, BERT, GPT serisi gibi B\u00fcy\u00fck Dil Modelleri (LLM'ler), GenAI'nin temelini olu\u015fturur. Bu modellerin nas\u0131l e\u011fitildi\u011fini (\u00f6n e\u011fitim, ince ayar), mimarilerini (encoder-decoder, decoder-only) ve yeteneklerini (metin \u00fcretimi, \u00f6zetleme, \u00e7eviri) anlamak hayati \u00f6nem ta\u015f\u0131r. Hugging Face Transformers k\u00fct\u00fcphanesi, bu modellerle \u00e7al\u0131\u015fmak i\u00e7in pop\u00fcler bir ara\u00e7t\u0131r.<\/p>\n
3.2. Prompt Engineering ve \u0130nce Ayar (Fine-tuning)<\/h4>\n
GenAI modellerinden istenen \u00e7\u0131kt\u0131lar\u0131 almak, do\u011fru \"prompt\"lar\u0131 (istemleri) yazma sanat\u0131n\u0131 gerektirir. Prompt engineering, modelin davran\u0131\u015f\u0131n\u0131 y\u00f6nlendirmek i\u00e7in en etkili giri\u015fleri tasarlama s\u00fcrecidir. Ayr\u0131ca, genel bir modeli belirli bir g\u00f6reve veya veri setine uyarlamak i\u00e7in yap\u0131lan \"ince ayar\" (fine-tuning) teknikleri de GenAI m\u00fchendislerinin bilmesi gereken \u00f6nemli bir beceridir.<\/p>\n
\n# Basit bir prompt engineering \u00f6rne\u011fi (ger\u00e7ek bir LLM API'si gerektirir)\n# from openai import OpenAI\n# client = OpenAI()\n# response = client.chat.completions.create(\n# model=\"gpt-3.5-turbo\",\n# messages=[\n# {\"role\": \"system\", \"content\": \"Sen bir yapay zeka asistan\u0131s\u0131n ve teknik makaleler yazmada uzmans\u0131n.\"},\n# {\"role\": \"user\", \"content\": \"Yaz\u0131l\u0131m m\u00fchendisli\u011finden GenAI m\u00fchendisli\u011fine ge\u00e7i\u015fin faydalar\u0131n\u0131 3 maddede \u00f6zetle.\"}\n# ]\n# )\n# print(response.choices[0].message.content)\n<\/pre>\n<\/code><\/p>\n
3.3. G\u00f6r\u00fcnt\u00fc ve Di\u011fer Medya \u00dcretimi Modelleri (Diffusion, GANs)<\/h4>\n
Metin tabanl\u0131 modellerin yan\u0131 s\u0131ra, GenAI ayn\u0131 zamanda g\u00f6r\u00fcnt\u00fc, ses ve video gibi di\u011fer medya t\u00fcrlerini de \u00fcretebilir. Generative Adversarial Networks (GANs) ve Diffusion modelleri (Stable Diffusion, Midjourney gibi), bu alandaki \u00f6nde gelen teknolojilerdir. Bu modellerin temel prensiplerini ve kullan\u0131m alanlar\u0131n\u0131 anlamak, GenAI m\u00fchendisinin yetenek setini geni\u015fletir.<\/p>\n
4. Pratik Uygulamalar ve Proje Geli\u015ftirme<\/h3>\n
Teorik bilgiyi prati\u011fe d\u00f6kmek, GenAI m\u00fchendisi olma yolundaki en \u00f6nemli ad\u0131mlardan biridir. Kendi projelerinizi geli\u015ftirmek, \u00f6\u011frenme s\u00fcrecini h\u0131zland\u0131r\u0131r ve portf\u00f6y\u00fcn\u00fcz\u00fc zenginle\u015ftirir.<\/p>\n
4.1. GenAI API'leri ve \u00c7er\u00e7eveleriyle \u00c7al\u0131\u015fmak (OpenAI, Hugging Face, LangChain)<\/h4>\n
OpenAI API'si, Google Gemini API'si gibi platformlar, g\u00fc\u00e7l\u00fc GenAI modellerine kolay eri\u015fim sa\u011flar. Hugging Face, \u00f6nceden e\u011fitilmi\u015f modelleri ve ara\u00e7lar\u0131 sunan bir di\u011fer \u00f6nemli kaynakt\u0131r. LangChain gibi \u00e7er\u00e7eveler ise, LLM'leri harici veri kaynaklar\u0131 ve di\u011fer ara\u00e7larla entegre ederek karma\u015f\u0131k uygulamalar olu\u015fturmay\u0131 kolayla\u015ft\u0131r\u0131r. Bu ara\u00e7lar\u0131 etkin bir \u015fekilde kullanmay\u0131 \u00f6\u011frenmek, verimlili\u011finizi art\u0131racakt\u0131r.<\/p>\n
4.2. Kendi GenAI Uygulamalar\u0131n\u0131z\u0131 Geli\u015ftirmek<\/h4>\n
K\u00fc\u00e7\u00fck \u00f6l\u00e7ekli projelerle ba\u015flay\u0131n: bir metin \u00f6zetleyici, bir kod \u00fcretici, bir sohbet botu veya basit bir g\u00f6r\u00fcnt\u00fc olu\u015fturucu. Bu projeler, GenAI modellerini entegre etme, \u00e7\u0131kt\u0131lar\u0131 i\u015fleme ve kullan\u0131c\u0131 aray\u00fczleri olu\u015fturma becerilerinizi geli\u015ftirmenizi sa\u011flar. Projelerinizde, kullan\u0131c\u0131 geri bildirimlerini dikkate alarak iteratif bir yakla\u015f\u0131mla geli\u015ftirme yap\u0131n.<\/p>\n
4.3. MLOps ve GenAI \u00dcr\u00fcnle\u015ftirme S\u00fcre\u00e7leri<\/h4>\n
Bir GenAI modelini prototipten \u00fcretime ta\u015f\u0131mak, MLOps (Makine \u00d6\u011frenimi Operasyonlar\u0131) prensiplerini gerektirir. Model versiyonlama, da\u011f\u0131t\u0131m, izleme ve s\u00fcrekli entegrasyon\/s\u00fcrekli teslimat (CI\/CD) s\u00fcre\u00e7leri, GenAI uygulamalar\u0131n\u0131n g\u00fcvenilir ve \u00f6l\u00e7eklenebilir olmas\u0131n\u0131 sa\u011flar. Bulut platformlar\u0131 (AWS SageMaker, Google Cloud AI Platform, Azure ML) bu s\u00fcre\u00e7lerde \u00f6nemli rol oynar.<\/p>\n
\n# Basit bir MLOps konsepti: Model versiyonlama i\u00e7in bir \u00f6rnek\n# production_model_v1.py\n# development_model_v2.py\n# Bu dosyalar, farkl\u0131 model versiyonlar\u0131n\u0131 veya konfig\u00fcrasyonlar\u0131n\u0131 temsil edebilir.\n# Ger\u00e7ek bir senaryoda, MLflow veya DVC gibi ara\u00e7lar kullan\u0131l\u0131r.\n<\/pre>\n<\/code><\/p>\n
5. S\u00fcrekli \u00d6\u011frenme ve Gelece\u011fe Y\u00f6nelik Beceriler<\/h3>\n
GenAI alan\u0131 son derece dinamiktir. Bu alanda ba\u015far\u0131l\u0131 olmak i\u00e7in s\u00fcrekli \u00f6\u011frenme ve adaptasyon esast\u0131r.<\/p>\n
5.1. Ara\u015ft\u0131rma Makalelerini Takip Etmek ve Deneyler Yapmak<\/h4>\n
ArXiv, NeurIPS, ICML gibi platformlardaki yeni ara\u015ft\u0131rma makalelerini takip etmek, alan\u0131n en son geli\u015fmelerinden haberdar olman\u0131z\u0131 sa\u011flar. Okudu\u011funuz yeni fikirleri k\u00fc\u00e7\u00fck \u00f6l\u00e7ekli deneylerle kendi ba\u015f\u0131n\u0131za uygulamaya \u00e7al\u0131\u015fmak, teorik bilginizi peki\u015ftirir ve problem \u00e7\u00f6zme yeteneklerinizi geli\u015ftirir.<\/p>\n
5.2. Etik AI ve Sorumlu Geli\u015ftirme<\/h4>\n
GenAI modelleri, \u00f6nyarg\u0131lar, yanl\u0131\u015f bilgi yayma ve gizlilik ihlalleri gibi \u00f6nemli etik sorunlar\u0131 beraberinde getirir. Bir GenAI m\u00fchendisi olarak, bu etik sorumluluklar\u0131n fark\u0131nda olmal\u0131 ve geli\u015ftirdi\u011finiz uygulamalar\u0131n adil, \u015feffaf ve sorumlu bir \u015fekilde kullan\u0131lmas\u0131n\u0131 sa\u011flamak i\u00e7in \u00e7aba g\u00f6stermelisiniz. G\u00fcvenli ve etik yapay zeka prensiplerini benimsemek, hem sizin hem de toplum i\u00e7in \u00f6nemlidir.<\/p>\n
5.3. Topluluklara Kat\u0131l\u0131m ve A\u011f Olu\u015fturma<\/h4>\n
GenAI topluluklar\u0131na (Kaggle, Discord sunucular\u0131, GitHub projeleri) kat\u0131lmak, di\u011fer m\u00fchendislerle etkile\u015fim kurmak, bilgi al\u0131\u015fveri\u015finde bulunmak ve yeni f\u0131rsatlar ke\u015ffetmek i\u00e7in harika bir yoldur. Konferanslara ve hackathon'lara kat\u0131lmak da a\u011f\u0131n\u0131z\u0131 geni\u015fletmenize ve sekt\u00f6rdeki trendleri yak\u0131ndan takip etmenize yard\u0131mc\u0131 olur.<\/p>\n
6. Kariyer Yolu ve Roller<\/h3>\n
GenAI m\u00fchendisi olmak, sadece teknik becerileri de\u011fil, ayn\u0131 zamanda kariyer stratejilerini de gerektirir.<\/p>\n
6.1. GenAI M\u00fchendisi Rol Tan\u0131m\u0131<\/h4>\n
Bir GenAI m\u00fchendisi, genellikle b\u00fcy\u00fck dil modelleri veya di\u011fer \u00fcretken modellerle \u00e7al\u0131\u015fan, bu modelleri mevcut \u00fcr\u00fcn ve hizmetlere entegre eden, prompt engineering tekniklerini uygulayan, modelleri ince ayarlayan ve bazen de s\u0131f\u0131rdan yeni modeller geli\u015ftiren ki\u015fidir. Rol, veri m\u00fchendisli\u011fi, ML m\u00fchendisli\u011fi ve yaz\u0131l\u0131m m\u00fchendisli\u011fi becerilerinin birle\u015fimini gerektirir.<\/p>\n
6.2. Mevcut \u0130\u015finizi GenAI Odakl\u0131 Hale Getirme<\/h4>\n
E\u011fer hali haz\u0131rda bir yaz\u0131l\u0131m m\u00fchendisiyseniz, GenAI becerilerinizi mevcut projelerinize entegre etme yollar\u0131n\u0131 aray\u0131n. \u00d6rne\u011fin, bir kod tamamlama arac\u0131 geli\u015ftirmek, m\u00fc\u015fteri hizmetleri i\u00e7in bir sohbet botu entegre etmek veya i\u00e7erik olu\u015fturma s\u00fcre\u00e7lerini otomatikle\u015ftirmek gibi projelerle ba\u015flayabilirsiniz. Bu, \u015firketiniz i\u00e7inde GenAI yeteneklerinizi kan\u0131tlaman\u0131n ve yeni bir rol \u00fcstlenmenin en iyi yollar\u0131ndan biridir.<\/p>\n
6.3. M\u00fclakatlara Haz\u0131rl\u0131k ve Portf\u00f6y Olu\u015fturma<\/h4>\n
GenAI m\u00fchendisi pozisyonlar\u0131 i\u00e7in m\u00fclakatlar, genellikle hem teorik GenAI bilgisi hem de pratik kodlama becerilerini test eder. Kendi geli\u015ftirdi\u011finiz GenAI projelerinden olu\u015fan g\u00fc\u00e7l\u00fc bir portf\u00f6y, yeteneklerinizi sergilemenin en etkili yoludur. GitHub profilinizi aktif tutun, blog yaz\u0131lar\u0131 yaz\u0131n ve projelerinizin detaylar\u0131n\u0131 a\u00e7\u0131klayan iyi belgelenmi\u015f README dosyalar\u0131 haz\u0131rlay\u0131n.<\/p>\n
Sonu\u00e7<\/h3>\n
Yaz\u0131l\u0131m m\u00fchendisli\u011finden GenAI m\u00fchendisli\u011fine ge\u00e7i\u015f, 2026 ve sonras\u0131 i\u00e7in kariyerinizi g\u00fc\u00e7lendirecek stratejik bir ad\u0131md\u0131r. Bu yolculuk, temel AI\/ML bilgisiyle ba\u015flay\u0131p, GenAI'nin \u00e7ekirdek konular\u0131na derinlemesine dalmay\u0131, pratik projelerle deneyim kazanmay\u0131 ve s\u00fcrekli \u00f6\u011frenmeyi gerektirir. Unutmay\u0131n ki bu alan s\u00fcrekli evriliyor; merakl\u0131 kalmak, deney yapmaktan \u00e7ekinmemek ve etik sorumluluklar\u0131 g\u00f6z \u00f6n\u00fcnde bulundurmak, sizi ba\u015far\u0131l\u0131 bir GenAI m\u00fchendisi yapacakt\u0131r. Bu yol haritas\u0131, bu heyecan verici ve \u00f6d\u00fcllendirici d\u00f6n\u00fc\u015f\u00fcmde size rehberlik etmek i\u00e7in tasarlanm\u0131\u015ft\u0131r. Gelece\u011fin teknolojisini \u015fekillendirmeye haz\u0131r olun!<\/p>\n
SSS (S\u0131k Sorulan Sorular)<\/h3>\n
Yapay zeka ge\u00e7mi\u015fim yok, ba\u015flayabilir miyim?<\/strong>
\nKesinlikle! Bir yaz\u0131l\u0131m m\u00fchendisi olarak zaten g\u00fc\u00e7l\u00fc bir problem \u00e7\u00f6zme ve kodlama temeline sahipsiniz. Yukar\u0131daki yol haritas\u0131n\u0131 takip ederek temel AI\/ML ve GenAI konular\u0131n\u0131 \u00f6\u011frenebilirsiniz.<\/p>\nHangi programlama dilini \u00f6\u011frenmeliyim?<\/strong>
\nPython, yapay zeka ve makine \u00f6\u011frenimi i\u00e7in end\u00fcstri standard\u0131d\u0131r. Di\u011fer dillerde de \u00e7al\u0131\u015fmalar olsa da, Python ile ba\u015flamak en do\u011fru ad\u0131md\u0131r.<\/p>\nBu d\u00f6n\u00fc\u015f\u00fcm ne kadar s\u00fcrer?<\/strong>
\nBu, ki\u015fisel \u00f6\u011frenme h\u0131z\u0131n\u0131za ve ay\u0131rabilece\u011finiz zamana ba\u011fl\u0131d\u0131r. Ancak, temelden ba\u015flay\u0131p pratik projelerle 6-12 ay i\u00e7inde \u00f6nemli bir ilerleme kaydedebilirsiniz.<\/p>\nGenAI M\u00fchendisi maa\u015flar\u0131 nas\u0131l?<\/strong>
\nGenAI m\u00fchendisleri, y\u00fcksek talep g\u00f6ren uzmanl\u0131k alanlar\u0131 nedeniyle genellikle sekt\u00f6rdeki en iyi maa\u015flar\u0131 al\u0131rlar. Deneyim ve konum gibi fakt\u00f6rlere ba\u011fl\u0131 olarak bu rakamlar de\u011fi\u015fiklik g\u00f6sterebilir.<\/p>\nHangi kaynaklar\u0131 takip etmeliyim?<\/strong>
\nCoursera, edX gibi platformlardaki kurslar, Hugging Face dok\u00fcmantasyonu, OpenAI API belgeleri, ArXiv gibi ara\u015ft\u0131rma makalesi siteleri ve Kaggle gibi yar\u0131\u015fma platformlar\u0131 harika ba\u015flang\u0131\u00e7 noktalar\u0131d\u0131r.<\/p>\n","protected":false},"excerpt":{"rendered":"\u00dcretken yapay zeka (Generative AI), metin, g\u00f6r\u00fcnt\u00fc, kod ve di\u011fer medya t\u00fcrlerini insan benzeri kalitede \u00fcretebilen modelleri kapsar. ChatGPT, DALL-E g…","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":[1342],"tags":[],"class_list":{"0":"post-37134","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-ai","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\nYaz\u0131l\u0131m M\u00fchendisli\u011finden GenAI M\u00fchendisli\u011fine: 2026 \u0130\u00e7in Pratik Bir Yol Haritas\u0131 - Kodlar\u0131n Gizemli D\u00fcnyas\u0131<\/title>\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\/yazilim-muhendisliginden-genai-muhendisligine-2026-icin-pratik-bir-yol-haritasi\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Yaz\u0131l\u0131m M\u00fchendisli\u011finden GenAI M\u00fchendisli\u011fine: 2026 \u0130\u00e7in Pratik Bir Yol Haritas\u0131\" \/>\n<meta property=\"og:description\" content=\"\u00dcretken yapay zeka (Generative AI), metin, g\u00f6r\u00fcnt\u00fc, kod ve di\u011fer medya t\u00fcrlerini insan benzeri kalitede \u00fcretebilen modelleri kapsar. 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