Soru-Cevap Uygulamas\u0131 (TensorFlow.js)<\/title>\n <style>\n body {\n font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;\n margin: 20px;\n background-color: #f4f7f6;\n color: #333;\n line-height: 1.6;\n }\n .container {\n max-width: 800px;\n margin: 0 auto;\n background-color: #ffffff;\n padding: 30px;\n border-radius: 8px;\n box-shadow: 0 4px 12px rgba(0, 0, 0, 0.08);\n }\n h1, h2 {\n color: #2c3e50;\n text-align: center;\n margin-bottom: 25px;\n }\n label {\n display: block;\n margin-bottom: 8px;\n font-weight: bold;\n color: #34495e;\n }\n textarea, input[type=\"text\"] {\n width: calc(100% - 20px);\n padding: 12px;\n margin-bottom: 20px;\n border: 1px solid #ccc;\n border-radius: 5px;\n font-size: 16px;\n box-sizing: border-box;\n resize: vertical;\n }\n button {\n background-color: #3498db;\n color: white;\n padding: 12px 25px;\n border: none;\n border-radius: 5px;\n cursor: pointer;\n font-size: 18px;\n transition: background-color 0.3s ease;\n width: 100%;\n box-sizing: border-box;\n }\n button:hover {\n background-color: #2980b9;\n }\n button:disabled {\n background-color: #cccccc;\n cursor: not-allowed;\n }\n #status {\n text-align: center;\n margin-top: 20px;\n font-style: italic;\n color: #7f8c8d;\n }\n #answers {\n margin-top: 30px;\n padding: 20px;\n background-color: #ecf0f1;\n border-radius: 5px;\n border-left: 5px solid #3498db;\n min-height: 50px;\n }\n .answer-item {\n margin-bottom: 10px;\n padding: 10px;\n background-color: #ffffff;\n border-radius: 5px;\n box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);\n }\n .answer-item strong {\n color: #2c3e50;\n }\n .loading-spinner {\n border: 4px solid #f3f3f3;\n border-top: 4px solid #3498db;\n border-radius: 50%;\n width: 20px;\n height: 20px;\n animation: spin 1s linear infinite;\n display: inline-block;\n vertical-align: middle;\n margin-left: 10px;\n }\n @keyframes spin {\n 0% { transform: rotate(0deg); }\n 100% { transform: rotate(360deg); }\n }\n <\/style>\n<\/head>\n<body>\n <div class=\"container\">\n \n Model y\u00fckleniyor...<\/p>\n\n <label for=\"context\">Ba\u011flam Metni:<\/label>\n <textarea id=\"context\" rows=\"10\" placeholder=\"L\u00fctfen cevap aramak istedi\u011finiz metni buraya yap\u0131\u015ft\u0131r\u0131n.\"><\/textarea>\n\n <label for=\"question\">Sorunuz:<\/label>\n <input type=\"text\" id=\"question\" placeholder=\"Metinle ilgili bir soru sorun.\">\n\n <button id=\"askButton\" disabled>Cevap Bul<\/button>\n\n <h2>Cevaplar:<\/h2>\n <div id=\"answers\">\n Burada cevaplar g\u00f6r\u00fcnt\u00fclenecektir.<\/p>\n <\/div>\n <\/div>\n\n <script src=\"https:\/\/cdn.jsdelivr.net\/npm\/@tensorflow\/tfjs\"><\/script>\n <script src=\"https:\/\/cdn.jsdelivr.net\/npm\/@tensorflow-models\/qna\"><\/script>\n <script src=\"app.js\"><\/script> \n<\/body>\n<\/html><\/code><\/pre>\nYukar\u0131daki HTML dosyas\u0131nda, TensorFlow.js ve <code>@tensorflow-models\/qna<\/code> k\u00fct\u00fcphanelerini do\u011frudan CDN’den y\u00fckl\u00fcyoruz. Bu, basit uygulamalar i\u00e7in pratik bir yakla\u015f\u0131md\u0131r. Daha karma\u015f\u0131k uygulamalarda, bunlar\u0131 <code>npm<\/code> ile kurup bir bundler (Webpack, Rollup, Vite) kullanarak bir araya getirmek daha yayg\u0131nd\u0131r.<\/p>\n<h4>JavaScript Mant\u0131\u011f\u0131 (<code>app.js<\/code>)<\/h4>\n\u015eimdi <code>app.js<\/code> dosyas\u0131n\u0131 olu\u015ftural\u0131m ve soru-cevap mant\u0131\u011f\u0131n\u0131 ekleyelim.<\/p>\n<pre><code class=\"language-javascript\">\/\/ app.js\n\nlet model;\nconst statusElement = document.getElementById('status');\nconst contextInput = document.getElementById('context');\nconst questionInput = document.getElementById('question');\nconst askButton = document.getElementById('askButton');\nconst answersDiv = document.getElementById('answers');\n\n\/\/ Modeli y\u00fckleme fonksiyonu\nasync function loadQAModel() {\n statusElement.innerText = 'Soru-Cevap modeli y\u00fckleniyor...';\n askButton.disabled = true; \/\/ Model y\u00fcklenirken butonu devre d\u0131\u015f\u0131 b\u0131rak\n try {\n \/\/ En iyi performans\u0131 sa\u011flamak i\u00e7in WebGL veya WASM backend'ini se\u00e7in\n \/\/ Taray\u0131c\u0131 deste\u011fine g\u00f6re otomatik se\u00e7im veya manuel tercih yap\u0131labilir.\n \/\/ \u00d6rne\u011fin, 'webgl' GPU h\u0131zland\u0131rmas\u0131 sa\u011flar, 'wasm' CPU performans\u0131n\u0131 art\u0131r\u0131r.\n if (await tf.setBackend('webgl').then(() => true).catch(() => false)) {\n console.log('WebGL backend kullan\u0131l\u0131yor.');\n } else if (await tf.setBackend('wasm').then(() => true).catch(() => false)) {\n console.log('WASM backend kullan\u0131l\u0131yor.');\n } else {\n console.log('CPU backend kullan\u0131l\u0131yor (performans daha d\u00fc\u015f\u00fck olabilir).');\n }\n \n model = await qna.load();\n statusElement.innerText = 'Model y\u00fcklendi. Haz\u0131r!';\n askButton.disabled = false; \/\/ Model y\u00fcklendi\u011finde butonu etkinle\u015ftir\n console.log('Model ba\u015far\u0131yla y\u00fcklendi.');\n } catch (error) {\n statusElement.innerText = <code>Model y\u00fcklenemedi: ${error.message}<\/code>;\n console.error('Model y\u00fcklenirken bir hata olu\u015ftu:', error);\n }\n}\n\n\/\/ Cevap bulma fonksiyonu\nasync function findAnswers() {\n if (!model) {\n alert('Model hen\u00fcz y\u00fcklenmedi. L\u00fctfen bekleyin.');\n return;\n }\n\n const context = contextInput.value;\n const question = questionInput.value;\n\n if (!context || !question) {\n alert('L\u00fctfen hem ba\u011flam metnini hem de soruyu girin.');\n return;\n }\n\n answersDiv.innerHTML = '<\/span> Cevaplar aran\u0131yor...<\/p>';\n askButton.disabled = true; \/\/ \u0130\u015flem s\u0131ras\u0131nda butonu devre d\u0131\u015f\u0131 b\u0131rak\n\n try {\n \/\/ Modelden cevaplar\u0131 al\n \/\/ model.findAnswers(question, context) varsay\u0131lan olarak en iyi 5 cevab\u0131 d\u00f6nd\u00fcr\u00fcr.\n \/\/ Options nesnesi ile topk (ka\u00e7 cevap d\u00f6nece\u011fi) gibi parametreleri ayarlayabiliriz.\n const answers = await model.findAnswers(question, context, { topk: 3 });\n\n if (answers && answers.length > 0) {\n answersDiv.innerHTML = ''; \/\/ \u00d6nceki cevaplar\u0131 temizle\n answers.forEach((answer, index) => {\n const answerItem = document.createElement('div');\n answerItem.className = 'answer-item';\n answerItem.innerHTML = <code>\n Cevap ${index + 1}:<\/strong> ${answer.text}<\/p>\n G\u00fcven (Skor):<\/em> ${(answer.score * 100).toFixed(2)}%<\/p>\n <\/code>;\n answersDiv.appendChild(answerItem);\n });\n\n \/\/ Ba\u011flam metninde cevab\u0131 vurgulama (iste\u011fe ba\u011fl\u0131 ama g\u00fczel bir \u00f6zellik)\n highlightAnswerInContext(context, answers[0].text, answers[0].startIndex);\n\n } else {\n answersDiv.innerHTML = 'Bu soruya ba\u011flam i\u00e7inde bir cevap bulunamad\u0131.<\/p>';\n }\n } catch (error) {\n answersDiv.innerHTML = <code>Cevap bulunurken bir hata olu\u015ftu: ${error.message}<\/p><\/code>;\n console.error('Cevap bulunurken bir hata olu\u015ftu:', error);\n } finally {\n askButton.disabled = false; \/\/ \u0130\u015flem bitince butonu tekrar etkinle\u015ftir\n }\n}\n\n\/\/ Ba\u011flam metninde cevab\u0131 vurgulama fonksiyonu\nfunction highlightAnswerInContext(context, answerText, startIndex) {\n const endIndex = startIndex + answerText.length;\n const highlightedContext = \n context.substring(0, startIndex) +\n <code>${context.substring(startIndex, endIndex)}<\/mark><\/code> +\n context.substring(endIndex);\n \n \/\/ Ba\u011flam metni input alan\u0131n\u0131 sadece vurgulama i\u00e7in de\u011fi\u015ftiriyoruz.\n \/\/ Ancak textarea'n\u0131n HTML i\u00e7eri\u011fini do\u011frudan de\u011fi\u015ftiremeyiz.\n \/\/ Bunun yerine, vurgulanm\u0131\u015f metni ayr\u0131 bir div'de g\u00f6sterebiliriz.\n \/\/ \u015eu anki implementasyon sadece cevab\u0131 g\u00f6steriyor, vurgulama i\u00e7in daha karma\u015f\u0131k bir UI gerekir.\n \/\/ Bu k\u0131s\u0131m UI\/UX iyile\u015ftirmesi olarak d\u00fc\u015f\u00fcn\u00fclebilir.\n console.log(\"Cevap vurguland\u0131 (konsolda):\", highlightedContext);\n \/\/ Ger\u00e7ek bir uygulamada, ba\u011flam metnini bir div'e al\u0131p innerHTML ile vurgulanm\u0131\u015f metni g\u00f6sterebilirdik.\n \/\/ \u00d6rne\u011fin: document.getElementById('highlightedContext').innerHTML = highlightedContext;\n}\n\n\n\/\/ Olay dinleyicileri\naskButton.addEventListener('click', findAnswers);\nquestionInput.addEventListener('keypress', (e) => {\n if (e.key === 'Enter' && !askButton.disabled) {\n findAnswers();\n }\n});\n\n\/\/ Uygulama y\u00fcklendi\u011finde modeli ba\u015flat\nloadQAModel();<\/code><\/pre>\nBu JavaScript kodu: \n1. TensorFlow.js ve QnA modelini asenkron olarak y\u00fckler. \n2. Y\u00fckleme durumu ve hata mesajlar\u0131n\u0131 kullan\u0131c\u0131ya bildirir. \n3. “Cevap Bul” d\u00fc\u011fmesine t\u0131kland\u0131\u011f\u0131nda veya soru alan\u0131nda Enter tu\u015funa bas\u0131ld\u0131\u011f\u0131nda <code>findAnswers<\/code> fonksiyonunu tetikler. \n4. <code>findAnswers<\/code> fonksiyonu, ba\u011flam ve soru metinlerini alarak <code>model.findAnswers()<\/code> metodunu \u00e7a\u011f\u0131r\u0131r. \n5. Modelden gelen cevaplar\u0131 (metin ve g\u00fcven skoru ile birlikte) <code>answersDiv<\/code> elementinde g\u00f6r\u00fcnt\u00fcler. \n6. \u0130\u015flem s\u0131ras\u0131nda UI’da geri bildirim (y\u00fckleme spinner’\u0131, d\u00fc\u011fme devre d\u0131\u015f\u0131 b\u0131rakma) sa\u011flar. \n7. <code>highlightAnswerInContext<\/code> fonksiyonu, cevab\u0131n ba\u011flam metni i\u00e7inde nerede oldu\u011funu g\u00f6stermek i\u00e7in basit bir vurgulama mant\u0131\u011f\u0131 sunar (\u015fu an i\u00e7in konsolda g\u00f6steriliyor, ger\u00e7ek UI entegrasyonu i\u00e7in ek \u00e7al\u0131\u015fma gerekebilir).<\/p>\n<h4>Kullan\u0131c\u0131 Aray\u00fcz\u00fc Tasar\u0131m\u0131 (UI\/UX)<\/h4>\nYukar\u0131daki CSS, temel bir d\u00fczen ve stil sa\u011flar. \u0130yi bir kullan\u0131c\u0131 deneyimi i\u00e7in \u015funlar\u0131 g\u00f6z \u00f6n\u00fcnde bulundurmal\u0131s\u0131n\u0131z: \n* Duyarl\u0131l\u0131k (Responsiveness):<\/strong> Uygulaman\u0131z\u0131n farkl\u0131 ekran boyutlar\u0131nda (mobil, tablet, masa\u00fcst\u00fc) iyi g\u00f6r\u00fcnmesini sa\u011flay\u0131n. \n* Geri Bildirim:<\/strong> Modelin y\u00fcklendi\u011fini, cevaplar\u0131n arand\u0131\u011f\u0131n\u0131 ve herhangi bir hata olu\u015ftu\u011funu kullan\u0131c\u0131ya a\u00e7\u0131k\u00e7a bildirin. Y\u00fckleme spinner’lar\u0131 veya durum mesajlar\u0131 bu konuda yard\u0131mc\u0131 olur. \n* A\u00e7\u0131kl\u0131k:<\/strong> Ba\u011flam ve soru alanlar\u0131n\u0131n ne i\u00e7in oldu\u011funu net bir \u015fekilde belirtin. Cevaplar\u0131 anla\u015f\u0131l\u0131r bir formatta sunun (\u00f6rne\u011fin, g\u00fcven skorlar\u0131yla birlikte). \n* Giri\u015f Do\u011frulamas\u0131:<\/strong> Bo\u015f ba\u011flam veya soru giri\u015fi gibi durumlar\u0131 ele al\u0131n.<\/p>\n<h3>Da\u011f\u0131t\u0131m Stratejileri<\/h3>\nWeb uygulaman\u0131z haz\u0131r oldu\u011funda, onu bir uygulama platformuna da\u011f\u0131tman\u0131n zaman\u0131 gelmi\u015ftir. TensorFlow.js modelleri istemci taraf\u0131nda \u00e7al\u0131\u015ft\u0131\u011f\u0131 i\u00e7in, \u00e7o\u011fu durumda basit bir statik web sitesi da\u011f\u0131t\u0131m\u0131 yeterli olacakt\u0131r.<\/p>\n<h4>Statik Web Sitesi Da\u011f\u0131t\u0131m\u0131<\/h4>\nBu, en yayg\u0131n ve en basit da\u011f\u0131t\u0131m y\u00f6ntemidir. Uygulaman\u0131z\u0131n t\u00fcm HTML, CSS ve JavaScript dosyalar\u0131 bir web sunucusunda bar\u0131nd\u0131r\u0131l\u0131r ve kullan\u0131c\u0131 taray\u0131c\u0131s\u0131 taraf\u0131ndan do\u011frudan eri\u015filir.<\/p>\nPop\u00fcler Platformlar:<\/strong><\/p>\n* Vercel:<\/strong> <code>git push<\/code> ile kolayca da\u011f\u0131t\u0131m, otomatik SSL, CDN, sunucusuz fonksiyon deste\u011fi. Next.js, React gibi framework’lerle iyi entegrasyon. \n* Netlify:<\/strong> Vercel’e benzer \u00f6zellikler, \u00fccretsiz katman, kolay Git entegrasyonu, sunucusuz fonksiyonlar. \n* GitHub Pages:<\/strong> GitHub depolar\u0131ndan do\u011frudan statik web sitesi yay\u0131nlama imkan\u0131. En basit se\u00e7eneklerden biri. \n* AWS S3 + CloudFront:<\/strong> AWS kullan\u0131c\u0131lar\u0131 i\u00e7in g\u00fc\u00e7l\u00fc ve \u00f6l\u00e7eklenebilir bir \u00e7\u00f6z\u00fcm. S3 statik dosyalar\u0131 bar\u0131nd\u0131r\u0131rken, CloudFront bir CDN g\u00f6revi g\u00f6r\u00fcr. \n* DigitalOcean App Platform:<\/strong> Hem statik siteleri hem de tam stack uygulamalar\u0131 destekleyen, y\u00f6netilen bir platform.<\/p>\nGenel Da\u011f\u0131t\u0131m Ad\u0131mlar\u0131 (\u00d6rnek: Vercel\/Netlify):<\/strong><\/p>\n1. Git Deposunda Bar\u0131nd\u0131rma:<\/strong> Uygulaman\u0131z\u0131n kodunu bir Git deposuna (\u00f6rn. GitHub) y\u00fckleyin. \n2. Platforma Ba\u011flanma:<\/strong> Se\u00e7ti\u011finiz uygulama platformunun web aray\u00fcz\u00fcne gidin ve GitHub hesab\u0131n\u0131z\u0131 ba\u011flay\u0131n. \n3. Depoyu Se\u00e7me:<\/strong> Da\u011f\u0131tmak istedi\u011finiz depoyu se\u00e7in. \n4. Yap\u0131land\u0131rma:<\/strong> \n * Build Command (Derleme Komutu):<\/strong> E\u011fer uygulaman\u0131z bir build ad\u0131m\u0131 gerektiriyorsa (\u00f6rne\u011fin, React i\u00e7in <code>npm run build<\/code>), bu komutu belirtin. Bizim basit HTML\/JS \u00f6rne\u011fimiz i\u00e7in bu ad\u0131ma gerek yoktur veya bo\u015f b\u0131rak\u0131labilir. \n * Output Directory (\u00c7\u0131kt\u0131 Dizini):<\/strong> Derlenmi\u015f dosyalar\u0131n bulundu\u011fu dizini belirtin (\u00f6rne\u011fin, <code>build<\/code>, <code>dist<\/code>, <code>public<\/code>). Bizim \u00f6rne\u011fimizde, t\u00fcm dosyalar k\u00f6k dizinde oldu\u011fu i\u00e7in bu da bo\u015f b\u0131rak\u0131labilir veya <code>.<\/code> olarak ayarlanabilir. \n5. Da\u011f\u0131t\u0131m:<\/strong> Platform, kodunuzu otomatik olarak \u00e7ekip derleyecek (e\u011fer varsa) ve bir URL \u00fczerinden yay\u0131nlayacakt\u0131r. \n6. \u00d6zel Alan Ad\u0131 (Custom Domain):<\/strong> \u0130ste\u011fe ba\u011fl\u0131 olarak, uygulaman\u0131za kendi alan ad\u0131n\u0131z\u0131 (\u00f6rne\u011fin <code>sorucevap.com<\/code>) ba\u011flayabilirsiniz. Platformlar genellikle \u00fccretsiz SSL sertifikas\u0131 sa\u011flar.<\/p>\n<h4>Sunucusuz Fonksiyonlarla Da\u011f\u0131t\u0131m (\u0130ste\u011fe Ba\u011fl\u0131 ve Geli\u015fmi\u015f)<\/h4>\n\u00c7o\u011fu TensorFlow.js istemci taraf\u0131 uygulamas\u0131 i\u00e7in sunucusuz fonksiyonlara gerek yoktur. Ancak, a\u015fa\u011f\u0131daki senaryolarda faydal\u0131 olabilirler: \n* B\u00fcy\u00fck Modeller:<\/strong> E\u011fer modeliniz taray\u0131c\u0131da \u00e7al\u0131\u015famayacak kadar b\u00fcy\u00fckse, sunucusuz bir fonksiyonda modelin tahminlerini yap\u0131p sadece sonucu istemciye g\u00f6nderebilirsiniz. Bu durumda, TensorFlow.js yerine Python tabanl\u0131 TensorFlow veya PyTorch gibi bir k\u00fct\u00fcphane sunucu taraf\u0131nda \u00e7al\u0131\u015f\u0131r. \n* D\u0131\u015f Veri Kaynaklar\u0131:<\/strong> Uygulaman\u0131z\u0131n ba\u011flam metnini bir veritaban\u0131ndan, harici bir API’den veya depolama hizmetinden \u00e7ekmesi gerekiyorsa, sunucusuz fonksiyonlar bu i\u015flemleri g\u00fcvenli bir \u015fekilde ger\u00e7ekle\u015ftirebilir. \n* Kullan\u0131c\u0131 Kimlik Do\u011frulama\/Yetkilendirme:<\/strong> Kullan\u0131c\u0131 oturumlar\u0131n\u0131 y\u00f6netmek veya belirli \u00f6zelliklere eri\u015fimi k\u0131s\u0131tlamak i\u00e7in sunucusuz fonksiyonlar kullan\u0131labilir.<\/p>\nBu senaryolarda, uygulaman\u0131z\u0131n frontend’i statik olarak bar\u0131nd\u0131r\u0131l\u0131rken, JavaScript kodunuz sunucusuz fonksiyonlar\u0131 \u00e7a\u011f\u0131rarak backend i\u015flemlerini tetikler. Vercel ve Netlify gibi platformlar, bu t\u00fcr hibrit da\u011f\u0131t\u0131mlar\u0131 kolayca destekler.<\/p>\n<h3>Performans ve Optimizasyon<\/h3>\nTensorFlow.js modellerini taray\u0131c\u0131da \u00e7al\u0131\u015ft\u0131rmak, performans optimizasyonlar\u0131n\u0131 kritik hale getirir.<\/p>\n* Model Boyutu ve Y\u00fckleme S\u00fcresi:<\/strong> \n * K\u00fc\u00e7\u00fck Modeller Kullan\u0131n:<\/strong> MobileBERT gibi daha k\u00fc\u00e7\u00fck, mobil uyumlu modeller, daha h\u0131zl\u0131 y\u00fckleme ve daha d\u00fc\u015f\u00fck bellek t\u00fcketimi sunar. <code>@tensorflow-models\/qna<\/code> paketi zaten bu t\u00fcr modelleri varsay\u0131lan olarak kullan\u0131r. \n * Model A\u011f\u0131rl\u0131klar\u0131n\u0131 \u00d6nbelle\u011fe Al\u0131n:<\/strong> Taray\u0131c\u0131 \u00f6nbellekleme mekanizmalar\u0131n\u0131 kullanarak model a\u011f\u0131rl\u0131klar\u0131n\u0131n tekrar tekrar indirilmesini \u00f6nleyin. CDN’ler genellikle bunu otomatik olarak yapar. \n * Tembel Y\u00fckleme (Lazy Loading):<\/strong> Modelin hemen uygulama ba\u015flang\u0131c\u0131nda de\u011fil, ihtiya\u00e7 duyuldu\u011funda (\u00f6rne\u011fin, kullan\u0131c\u0131 soru sorma b\u00f6l\u00fcm\u00fcne geldi\u011finde) y\u00fcklenmesini sa\u011flay\u0131n.<\/p>\n* Backend Se\u00e7imi:<\/strong> \n * <code>tf.setBackend('webgl')<\/code>: GPU h\u0131zland\u0131rmas\u0131 sa\u011flar ve \u00f6zellikle b\u00fcy\u00fck modeller veya yo\u011fun hesaplama gerektiren i\u015flemler i\u00e7in performans\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde art\u0131rabilir. Ancak, her taray\u0131c\u0131 veya cihaz WebGL’i desteklemeyebilir. \n * <code>tf.setBackend('wasm')<\/code>: WebAssembly (WASM) tabanl\u0131 CPU h\u0131zland\u0131rmas\u0131 sunar. WebGL’in desteklenmedi\u011fi durumlarda veya CPU tabanl\u0131 i\u015flemler i\u00e7in iyi bir alternatiftir. \n * <code>tf.setBackend('cpu')<\/code>: En temel backend’dir ve her zaman \u00e7al\u0131\u015f\u0131r, ancak en yava\u015ft\u0131r. \n * Otomatik Se\u00e7im ve Geri D\u00f6n\u00fc\u015f:<\/strong> Uygulaman\u0131z\u0131n mevcut cihaz ve taray\u0131c\u0131ya g\u00f6re en uygun backend’i otomatik olarak se\u00e7mesini ve birincil backend ba\u015far\u0131s\u0131z olursa di\u011ferine geri d\u00f6nmesini sa\u011flay\u0131n. Yukar\u0131daki <code>loadQAModel<\/code> fonksiyonunda basit bir \u00f6rnek verilmi\u015ftir.<\/p>\n* Bellek Kullan\u0131m\u0131:<\/strong> \n * Tensorleri \u0130mha Edin:<\/strong> TensorFlow.js, bellekte tensorler (veri yap\u0131lar\u0131) olu\u015fturur. Bu tensorler i\u015finiz bitti\u011finde manuel olarak <code>tf.dispose()<\/code> veya <code>tf.tidy()<\/code> fonksiyonlar\u0131n\u0131 kullanarak serbest b\u0131rak\u0131lmal\u0131d\u0131r. Bu, \u00f6zellikle modelden birden \u00e7ok tahmin yap\u0131ld\u0131\u011f\u0131nda bellek s\u0131z\u0131nt\u0131lar\u0131n\u0131 \u00f6nler. \n * <code>tf.tidy()<\/code>: Bir fonksiyon i\u00e7inde olu\u015fturulan t\u00fcm tensorleri otomatik olarak imha eden kullan\u0131\u015fl\u0131 bir ara\u00e7t\u0131r.<\/p>\n<pre><code class=\"language-javascript\">async function findAnswers() {\n \/\/ ...\n const answers = tf.tidy(() => {\n return model.findAnswers(question, context, { topk: 3 });\n });\n \/\/ ...\n }<\/code><\/pre>\n* Kullan\u0131c\u0131 Deneyimi:<\/strong> \n * \u0130lerleme G\u00f6stergeleri:<\/strong> Model y\u00fcklenirken veya cevap aran\u0131rken kullan\u0131c\u0131ya g\u00f6rsel geri bildirim sa\u011flay\u0131n (spinner, ilerleme \u00e7ubu\u011fu). \n * Giri\u015f Do\u011frulama ve Debounce:<\/strong> Kullan\u0131c\u0131 \u00e7ok h\u0131zl\u0131 yazarken gereksiz hesaplamalar\u0131 \u00f6nlemek i\u00e7in <code>debounce<\/code> fonksiyonlar\u0131 kullanabilirsiniz.<\/p>\n<h3>G\u00fcvenlik ve Gizlilik<\/h3>\nTensorFlow.js’in istemci taraf\u0131nda \u00e7al\u0131\u015fmas\u0131, g\u00fcvenlik ve gizlilik a\u00e7\u0131s\u0131ndan \u00f6nemli avantajlar sunar:<\/p>\n* Veri Gizlili\u011fi:<\/strong> Kullan\u0131c\u0131 verileri (ba\u011flam metni, sorular) kullan\u0131c\u0131n\u0131n cihaz\u0131ndan ayr\u0131lmaz. Bu, \u00f6zellikle hassas verilerle \u00e7al\u0131\u015f\u0131rken kritik bir gizlilik avantaj\u0131d\u0131r. Sunucu taraf\u0131na veri g\u00f6nderilmedi\u011fi i\u00e7in, veri ihlali riski azal\u0131r. \n* Model G\u00fcvenli\u011fi:<\/strong> Model a\u011f\u0131rl\u0131klar\u0131 ve mimarisi, genellikle uygulaman\u0131zla birlikte herkese a\u00e7\u0131k olarak sunulur. Bu, modelin nas\u0131l \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 merak edenler i\u00e7in \u015feffafl\u0131k sa\u011flar. Ancak, modelin \u00e7al\u0131nmas\u0131 veya tersine m\u00fchendislikle incelenmesi riski de vard\u0131r. Ticari s\u0131r niteli\u011findeki modeller i\u00e7in bu bir dezavantaj olabilir. \n* Ba\u011flant\u0131 G\u00fcvenli\u011fi (HTTPS):<\/strong> Uygulaman\u0131z\u0131 da\u011f\u0131tt\u0131\u011f\u0131n\u0131z platformun (Vercel, Netlify vb.) otomatik olarak sa\u011flad\u0131\u011f\u0131 HTTPS sertifikalar\u0131n\u0131 kullan\u0131n. Bu, kullan\u0131c\u0131n\u0131n taray\u0131c\u0131s\u0131 ile sunucu aras\u0131ndaki t\u00fcm ileti\u015fimin \u015fifrelenmesini sa\u011flar ve k\u00f6t\u00fc niyetli m\u00fcdahaleleri \u00f6nler.<\/p>\n<h3>Gelecek ve \u0130leri Ad\u0131mlar<\/h3>\nBu temel soru-cevap uygulamas\u0131n\u0131 kurduktan sonra, projeyi daha da geli\u015ftirmek i\u00e7in bir\u00e7ok yol vard\u0131r:<\/p>\n* \u00d6zel Model E\u011fitimi:<\/strong> Kendi \u00f6zel veri k\u00fcmeniz \u00fczerinde bir soru-cevap modeli e\u011fitebilir ve bunu TensorFlow.js format\u0131na d\u00f6n\u00fc\u015ft\u00fcrerek uygulaman\u0131zda kullanabilirsiniz. Bu, daha ni\u015f veya sekt\u00f6re \u00f6zel uygulamalar i\u00e7in idealdir. \n* Daha Karma\u015f\u0131k NLP G\u00f6revleri:<\/strong> Metin \u00f6zetleme, duygu analizi, metin s\u0131n\u0131fland\u0131rma gibi di\u011fer NLP g\u00f6revlerini de TensorFlow.js ile entegre edebilirsiniz. \n* Vurgulama ve Etkile\u015fim:<\/strong> Cevaplar\u0131n ba\u011flam metni i\u00e7inde g\u00f6rsel olarak vurgulanmas\u0131, kullan\u0131c\u0131 deneyimini \u00f6nemli \u00f6l\u00e7\u00fcde iyile\u015ftirir. Metin se\u00e7imi olaylar\u0131n\u0131 dinleyerek ba\u011flam\u0131 dinamik olarak de\u011fi\u015ftirebilirsiniz. \n* Daha \u0130yi Kullan\u0131c\u0131 Aray\u00fcz\u00fc:<\/strong> Bir frontend framework’\u00fc (React, Vue, Angular) kullanarak daha dinamik, y\u00f6netilebilir ve \u00f6l\u00e7eklenebilir bir kullan\u0131c\u0131 aray\u00fcz\u00fc olu\u015fturabilirsiniz. \n* Geri Bildirim Mekanizmalar\u0131:<\/strong> Kullan\u0131c\u0131lardan gelen geri bildirimleri toplayarak (\u00f6rne\u011fin, “Bu cevap yard\u0131mc\u0131 oldu mu?”) modelin performans\u0131n\u0131 izleyebilir ve gelecekteki geli\u015ftirmeler i\u00e7in veri toplayabilirsiniz. \n* \u00c7evrimd\u0131\u015f\u0131 Destek:<\/strong> Service Worker’lar kullanarak uygulaman\u0131z\u0131n ve model a\u011f\u0131rl\u0131klar\u0131n\u0131n \u00e7evrimd\u0131\u015f\u0131 \u00f6nbelle\u011fe al\u0131nmas\u0131n\u0131 sa\u011flayabilir, b\u00f6ylece kullan\u0131c\u0131lar internet ba\u011flant\u0131s\u0131 olmasa bile uygulamay\u0131 kullanabilirler.<\/p>\n<h3>Sonu\u00e7<\/h3>\n\u00d6nceden e\u011fitilmi\u015f bir soru-cevap TensorFlow.js modelini bir uygulama platformuna da\u011f\u0131tmak, modern web geli\u015ftirmenin en heyecan verici y\u00f6nlerinden biridir. Bu rehberde, model haz\u0131rl\u0131\u011f\u0131ndan uygulama geli\u015ftirmeye ve nihai da\u011f\u0131t\u0131ma kadar t\u00fcm s\u00fcreci ele ald\u0131k. TensorFlow.js’in g\u00fcc\u00fc ve modern uygulama platformlar\u0131n\u0131n da\u011f\u0131t\u0131m kolayl\u0131\u011f\u0131 sayesinde, yapay zeka destekli web uygulamalar\u0131n\u0131 geli\u015ftirmek ve yay\u0131nlamak hi\u00e7 bu kadar eri\u015filebilir olmam\u0131\u015ft\u0131.<\/p>\n\u0130ster bir prototip olu\u015fturuyor olun ister tam te\u015fekk\u00fcll\u00fc bir \u00fcr\u00fcn geli\u015ftiriyor olun, istemci taraf\u0131nda \u00e7al\u0131\u015fan makine \u00f6\u011frenimi modelleri, performans, gizlilik ve kullan\u0131c\u0131 deneyimi a\u00e7\u0131s\u0131ndan \u00f6nemli avantajlar sunar. Bu teknolojileri kullanarak, kullan\u0131c\u0131lar\u0131n do\u011frudan taray\u0131c\u0131lar\u0131nda ak\u0131ll\u0131 etkile\u015fimler ger\u00e7ekle\u015ftirebildi\u011fi, yenilik\u00e7i ve g\u00fc\u00e7l\u00fc web uygulamalar\u0131 yaratabilirsiniz. Uygulaman\u0131z\u0131 da\u011f\u0131tt\u0131ktan sonra, s\u00fcrekli iyile\u015ftirme ve optimizasyon ile kullan\u0131c\u0131lar\u0131n\u0131za en iyi deneyimi sunmaya devam edebilirsiniz. Bu alanda denemeler yapmaktan ve s\u0131n\u0131rlar\u0131 zorlamaktan \u00e7ekinmeyin!<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow. js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi TensorFlow.","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-41100","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>\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow.js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi - 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\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow.js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi\" \/>\n<meta property=\"og:description\" content=\"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow. js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi TensorFlow.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/\" \/>\n<meta property=\"og:site_name\" content=\"Kodlar\u0131n Gizemli D\u00fcnyas\u0131\" \/>\n<meta property=\"article:published_time\" content=\"2026-03-03T10:40:52+00:00\" \/>\n<meta name=\"author\" content=\"Fatih Soysal\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Yazan:\" \/>\n\t<meta name=\"twitter:data1\" content=\"Fatih Soysal\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tahmini okuma s\u00fcresi\" \/>\n\t<meta name=\"twitter:data2\" content=\"19 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/\"},\"author\":{\"name\":\"Fatih Soysal\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"headline\":\"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow.js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi\",\"datePublished\":\"2026-03-03T10:40:52+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/\"},\"wordCount\":3092,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/#respond\"]}],\"copyrightYear\":\"2026\",\"copyrightHolder\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/\",\"name\":\"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow.js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi - Kodlar\u0131n Gizemli D\u00fcnyas\u0131\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\"},\"datePublished\":\"2026-03-03T10:40:52+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/#breadcrumb\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Anasayfa\",\"item\":\"https:\/\/fatihsoysal.com\/blog\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow.js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/\",\"name\":\"Fatihsoysal.com\",\"description\":\"Blog - Yaz\u0131l\u0131m D\u00fcnyas\u0131 Tecr\u00fcbelerim\",\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/fatihsoysal.com\/blog\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"tr\"},{\"@type\":[\"Person\",\"Organization\"],\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\",\"name\":\"Fatih Soysal\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"tr\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2024\/04\/cropped-replicate-prediction-3kgg1hgjn5rgp0cf0p5tr0jw7w-1.png\",\"contentUrl\":\"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2024\/04\/cropped-replicate-prediction-3kgg1hgjn5rgp0cf0p5tr0jw7w-1.png\",\"width\":512,\"height\":512,\"caption\":\"Fatih Soysal\"},\"logo\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/image\/\"},\"description\":\"Kullan\u0131m ve kodlama m\u00fckemmeliyetini odak alan uygulamalar olu\u015fturma deneyimine sahip, profesyonel olarak 15+ y\u0131l \u00fczeri deneyime sahip bir yaz\u0131l\u0131m m\u00fchendisi.\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/author\/fatihsoysal\/\"}]}<\/script>\n","yoast_head_json":{"title":"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow.js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi - Kodlar\u0131n Gizemli D\u00fcnyas\u0131","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/","og_locale":"tr_TR","og_type":"article","og_title":"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow.js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi","og_description":"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow. js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi TensorFlow.","og_url":"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/","og_site_name":"Kodlar\u0131n Gizemli D\u00fcnyas\u0131","article_published_time":"2026-03-03T10:40:52+00:00","author":"Fatih Soysal","twitter_card":"summary_large_image","twitter_misc":{"Yazan:":"Fatih Soysal","Tahmini okuma s\u00fcresi":"19 dakika"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/#article","isPartOf":{"@id":"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/"},"author":{"name":"Fatih Soysal","@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1"},"headline":"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow.js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi","datePublished":"2026-03-03T10:40:52+00:00","mainEntityOfPage":{"@id":"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/"},"wordCount":3092,"commentCount":0,"publisher":{"@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1"},"inLanguage":"tr","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/#respond"]}],"copyrightYear":"2026","copyrightHolder":{"@id":"https:\/\/fatihsoysal.com\/blog\/#organization"}},{"@type":"WebPage","@id":"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/","url":"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/","name":"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow.js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi - Kodlar\u0131n Gizemli D\u00fcnyas\u0131","isPartOf":{"@id":"https:\/\/fatihsoysal.com\/blog\/#website"},"datePublished":"2026-03-03T10:40:52+00:00","breadcrumb":{"@id":"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/#breadcrumb"},"inLanguage":"tr","potentialAction":[{"@type":"ReadAction","target":["https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Anasayfa","item":"https:\/\/fatihsoysal.com\/blog\/"},{"@type":"ListItem","position":2,"name":"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow.js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi"}]},{"@type":"WebSite","@id":"https:\/\/fatihsoysal.com\/blog\/#website","url":"https:\/\/fatihsoysal.com\/blog\/","name":"Fatihsoysal.com","description":"Blog - Yaz\u0131l\u0131m D\u00fcnyas\u0131 Tecr\u00fcbelerim","publisher":{"@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/fatihsoysal.com\/blog\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"tr"},{"@type":["Person","Organization"],"@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1","name":"Fatih Soysal","image":{"@type":"ImageObject","inLanguage":"tr","@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/image\/","url":"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2024\/04\/cropped-replicate-prediction-3kgg1hgjn5rgp0cf0p5tr0jw7w-1.png","contentUrl":"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2024\/04\/cropped-replicate-prediction-3kgg1hgjn5rgp0cf0p5tr0jw7w-1.png","width":512,"height":512,"caption":"Fatih Soysal"},"logo":{"@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/image\/"},"description":"Kullan\u0131m ve kodlama m\u00fckemmeliyetini odak alan uygulamalar olu\u015fturma deneyimine sahip, profesyonel olarak 15+ y\u0131l \u00fczeri deneyime sahip bir yaz\u0131l\u0131m m\u00fchendisi.","url":"https:\/\/fatihsoysal.com\/blog\/author\/fatihsoysal\/"}]}},"yoast_meta":{"yoast_wpseo_title":"","yoast_wpseo_metadesc":"","yoast_wpseo_canonical":""},"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/posts\/41100","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/comments?post=41100"}],"version-history":[{"count":1,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/posts\/41100\/revisions"}],"predecessor-version":[{"id":41101,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/posts\/41100\/revisions\/41101"}],"wp:attachment":[{"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/media?parent=41100"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/categories?post=41100"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/tags?post=41100"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}

{"id":41100,"date":"2026-03-03T13:40:52","date_gmt":"2026-03-03T10:40:52","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/?p=41100"},"modified":"2026-03-03T13:40:52","modified_gmt":"2026-03-03T10:40:52","slug":"onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/","title":{"rendered":"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow.js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi"},"content":{"rendered":"

<\/p>\n

\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow.js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi<\/h2>\n
TensorFlow.js, makine \u00f6\u011frenimi modellerini do\u011frudan web taray\u0131c\u0131s\u0131nda veya Node.js ortam\u0131nda \u00e7al\u0131\u015ft\u0131rmam\u0131z\u0131 sa\u011flayan g\u00fc\u00e7l\u00fc bir k\u00fct\u00fcphanedir. Bu yetenek, geli\u015ftiricilere yapay zeka destekli uygulamalar\u0131 istemci taraf\u0131nda, yani kullan\u0131c\u0131lar\u0131n cihazlar\u0131nda \u00e7al\u0131\u015ft\u0131rma imkan\u0131 sunar. \u00d6zellikle soru-cevap (Question and Answer – Q&A) modelleri gibi do\u011fal dil i\u015fleme (NLP) g\u00f6revleri i\u00e7in TensorFlow.js kullanmak, gizlilik, performans ve eri\u015filebilirlik a\u00e7\u0131s\u0131ndan \u00f6nemli avantajlar sa\u011flar. Bu makalede, \u00f6nceden e\u011fitilmi\u015f bir soru-cevap TensorFlow.js modelini bir uygulama platformuna nas\u0131l da\u011f\u0131taca\u011f\u0131m\u0131z\u0131 ad\u0131m ad\u0131m inceleyece\u011fiz. Amac\u0131m\u0131z, kullan\u0131c\u0131lar\u0131n belirli bir metin ba\u011flam\u0131nda sorular sorarak an\u0131nda cevaplar alabilece\u011fi interaktif bir web uygulamas\u0131 olu\u015fturmak ve bunu kolayca eri\u015filebilir k\u0131lmakt\u0131r.<\/p>\n

Giri\u015f: Yapay Zeka Destekli Web Uygulamalar\u0131n\u0131n Y\u00fckseli\u015fi<\/h3>\n
G\u00fcn\u00fcm\u00fcz dijital d\u00fcnyas\u0131nda, yapay zeka (YZ) yeteneklerini web uygulamalar\u0131na entegre etmek, kullan\u0131c\u0131 deneyimini zenginle\u015ftirmenin ve yeni nesil etkile\u015fimli ara\u00e7lar yaratman\u0131n anahtar\u0131 haline gelmi\u015ftir. Soru-cevap sistemleri, \u00f6zellikle b\u00fcy\u00fck metin belgelerinden veya web sayfalar\u0131ndan bilgi \u00e7\u0131karma ihtiyac\u0131 duyulan alanlarda paha bi\u00e7ilmezdir. M\u00fc\u015fteri hizmetleri botlar\u0131ndan e\u011fitim platformlar\u0131na, bilgi tabanlar\u0131ndan i\u00e7erik arama motorlar\u0131na kadar geni\u015f bir yelpazede uygulama alan\u0131 bulurlar.<\/p>\n
TensorFlow.js, bu t\u00fcr modelleri sunucu taraf\u0131nda karma\u015f\u0131k altyap\u0131lar kurmaya gerek kalmadan do\u011frudan taray\u0131c\u0131da \u00e7al\u0131\u015ft\u0131rma esnekli\u011fi sunar. Bu, \u00f6zellikle veri gizlili\u011finin \u00f6nemli oldu\u011fu veya d\u00fc\u015f\u00fck gecikme s\u00fcresinin kritik oldu\u011fu senaryolarda b\u00fcy\u00fck bir avantajd\u0131r. Uygulama platformlar\u0131 ise (Vercel, Netlify, Heroku, DigitalOcean App Platform vb.), bu web uygulamalar\u0131n\u0131 kolayca da\u011f\u0131tmak, bar\u0131nd\u0131rmak ve \u00f6l\u00e7eklendirmek i\u00e7in modern ve verimli \u00e7\u00f6z\u00fcmler sunar. Bu makale, bu iki g\u00fc\u00e7l\u00fc teknolojiyi birle\u015ftirerek, \u00f6nceden e\u011fitilmi\u015f bir soru-cevap modelini nas\u0131l al\u0131p canl\u0131 bir web uygulamas\u0131 olarak d\u00fcnyaya sunaca\u011f\u0131n\u0131z\u0131 detayland\u0131racakt\u0131r.<\/p>\n

Temel Kavramlar<\/h3>\n
Bir soru-cevap modelini uygulama platformuna da\u011f\u0131tmadan \u00f6nce, ilgili temel kavramlar\u0131 anlamak \u00f6nemlidir.<\/p>\n

TensorFlow.js Nedir?<\/h4>\n
TensorFlow.js, Google taraf\u0131ndan geli\u015ftirilen, a\u00e7\u0131k kaynakl\u0131 bir makine \u00f6\u011frenimi k\u00fct\u00fcphanesidir. JavaScript tabanl\u0131 olmas\u0131 sayesinde web taray\u0131c\u0131lar\u0131nda (istemci taraf\u0131nda) ve Node.js sunucular\u0131nda (sunucu taraf\u0131nda) \u00e7al\u0131\u015fabilir. Python tabanl\u0131 TensorFlow’un t\u00fcm \u00f6zelliklerini JavaScript ekosistemine ta\u015f\u0131r.<\/p>\n
* Taray\u0131c\u0131da \u00c7al\u0131\u015fma:<\/strong> En b\u00fcy\u00fck avantajlar\u0131ndan biri, modelin kullan\u0131c\u0131n\u0131n cihaz\u0131nda \u00e7al\u0131\u015fmas\u0131d\u0131r. Bu, sunucu y\u00fck\u00fcn\u00fc azalt\u0131r, veri gizlili\u011fini art\u0131r\u0131r (veri sunucuya g\u00f6nderilmez) ve internet ba\u011flant\u0131s\u0131 zay\u0131f olsa bile belirli bir \u00f6l\u00e7\u00fcde \u00e7al\u0131\u015fabilirlik sa\u011flar.
\n* Model D\u00f6n\u00fc\u015ft\u00fcrme:<\/strong> Python’da e\u011fitilmi\u015f Keras veya TensorFlow modelleri, tensorflowjs_converter<\/code> arac\u0131 kullan\u0131larak TensorFlow.js format\u0131na d\u00f6n\u00fc\u015ft\u00fcr\u00fclebilir. Bu format, bir model.json<\/code> dosyas\u0131 (modelin mimarisi ve topolojisi) ve bir veya daha fazla ikili a\u011f\u0131rl\u0131k dosyas\u0131ndan olu\u015fur. \n* \u00d6nceden E\u011fitilmi\u015f Modeller:<\/strong> TensorFlow.js ekosistemi, g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma, nesne alg\u0131lama, poz tahmini ve do\u011fal dil i\u015fleme gibi \u00e7e\u015fitli g\u00f6revler i\u00e7in \u00f6nceden e\u011fitilmi\u015f modeller sunar. Soru-cevap modelleri de bu kategoriye girer.<\/p>\n

Soru-Cevap Modelleri<\/h4>\nSoru-cevap modelleri, belirli bir metin ba\u011flam\u0131 (context) verildi\u011finde, bu ba\u011flam i\u00e7indeki bir soruya en uygun cevab\u0131 bulmay\u0131 ama\u00e7layan do\u011fal dil i\u015fleme modelleridir. Genellikle, modelin \u00e7\u0131kt\u0131s\u0131 ba\u011flam metni i\u00e7indeki bir “cevap aral\u0131\u011f\u0131” (answer span) \u015feklinde olur.<\/p>\n * \u00c7al\u0131\u015fma Prensibi:<\/strong> Model, hem soru hem de ba\u011flam metnini al\u0131r, bu iki metin aras\u0131ndaki ili\u015fkileri analiz eder ve ba\u011flam i\u00e7inde soruyu en iyi yan\u0131tlayan metin par\u00e7as\u0131n\u0131n ba\u015flang\u0131\u00e7 ve biti\u015f indekslerini tahmin eder. \n* Yayg\u0131n Modeller:<\/strong> Bu t\u00fcr modeller genellikle SQuAD (Stanford Question Answering Dataset) gibi b\u00fcy\u00fck veri k\u00fcmeleri \u00fczerinde e\u011fitilir. BERT, RoBERTa, ALBERT, MobileBERT gibi transformat\u00f6r tabanl\u0131 mimariler bu alanda yayg\u0131n olarak kullan\u0131l\u0131r. TensorFlow.js i\u00e7in \u00f6zel olarak optimize edilmi\u015f MobileBERT gibi daha hafif modeller, taray\u0131c\u0131da daha iyi performans sunar. \n* @tensorflow-models\/qna<\/code> Paketi:<\/strong> TensorFlow.js ekibi, soru-cevap g\u00f6revleri i\u00e7in \u00f6zel olarak optimize edilmi\u015f ve kullan\u0131ma haz\u0131r modelleri i\u00e7eren @tensorflow-models\/qna<\/code> paketini sa\u011flar. Bu paket, model y\u00fckleme ve tahmin yapma s\u00fcre\u00e7lerini olduk\u00e7a basitle\u015ftirir.<\/p>\n Uygulama Platformlar\u0131<\/h4>\nUygulama platformlar\u0131, web uygulamalar\u0131n\u0131 ve API’leri da\u011f\u0131tmak, bar\u0131nd\u0131rmak ve y\u00f6netmek i\u00e7in tasarlanm\u0131\u015f hizmetlerdir. Geli\u015ftiricilerin altyap\u0131 y\u00f6netimi yerine uygulama geli\u015ftirmeye odaklanmas\u0131n\u0131 sa\u011flarlar.<\/p>\n * Statik Site Bar\u0131nd\u0131rma:<\/strong> \u00c7o\u011fu TensorFlow.js uygulamas\u0131, tamamen istemci taraf\u0131nda \u00e7al\u0131\u015ft\u0131\u011f\u0131 i\u00e7in statik web sitesi olarak da\u011f\u0131t\u0131labilir. Bu, HTML, CSS ve JavaScript dosyalar\u0131n\u0131n bir CDN (\u0130\u00e7erik Da\u011f\u0131t\u0131m A\u011f\u0131) \u00fczerinden sunulmas\u0131 anlam\u0131na gelir. Vercel, Netlify, GitHub Pages, AWS S3 + CloudFront bu konuda pop\u00fcler se\u00e7eneklerdir. \n* Sunucusuz (Serverless) Fonksiyonlar:<\/strong> Baz\u0131 uygulama platformlar\u0131 (Vercel, Netlify, DigitalOcean App Platform) statik sitelerin yan\u0131 s\u0131ra sunucusuz fonksiyonlar\u0131 da destekler. E\u011fer model \u00e7ok b\u00fcy\u00fckse ve taray\u0131c\u0131da \u00e7al\u0131\u015fmas\u0131 zor ise veya ek sunucu taraf\u0131 i\u015flem gerekiyorsa (\u00f6rne\u011fin, bir veritaban\u0131ndan ba\u011flam \u00e7ekmek), bu fonksiyonlar kullan\u0131labilir. Ancak, bu makalede ana odak noktam\u0131z tamamen istemci taraf\u0131nda \u00e7al\u0131\u015fan bir model olacakt\u0131r. \n* Otomatik Da\u011f\u0131t\u0131m:<\/strong> \u00c7o\u011fu modern uygulama platformu, Git depolar\u0131yla (GitHub, GitLab, Bitbucket) entegrasyon sunar. Bu sayede, kodunuzu push etti\u011finizde uygulama otomatik olarak derlenir ve da\u011f\u0131t\u0131l\u0131r.<\/p>\n Model Haz\u0131rl\u0131\u011f\u0131<\/h3>\nUygulamam\u0131z\u0131 geli\u015ftirmeye ba\u015flamadan \u00f6nce, kullanaca\u011f\u0131m\u0131z soru-cevap modelini haz\u0131rlamam\u0131z gerekir.<\/p>\n \u00d6nceden E\u011fitilmi\u015f Model Bulma ve Y\u00fckleme<\/h4>\nEn kolay yol, TensorFlow.js ekibinin sa\u011flad\u0131\u011f\u0131 @tensorflow-models\/qna<\/code> paketini kullanmakt\u0131r. Bu paket, \u00f6nceden e\u011fitilmi\u015f MobileBERT gibi modelleri i\u00e7erir ve bunlar\u0131 do\u011frudan web uygulaman\u0131za entegre etmenizi sa\u011flar.<\/p>\n 1. Ba\u011f\u0131ml\u0131l\u0131klar\u0131 Kurma:<\/strong> \u0130lk olarak, projenize npm<\/code> veya yarn<\/code> ile gerekli TensorFlow.js paketlerini eklemeniz gerekir:<\/p>\n npm install @tensorflow\/tfjs @tensorflow-models\/qna\n # veya\n yarn add @tensorflow\/tfjs @tensorflow-models\/qna<\/code><\/pre>\n2. Modeli Y\u00fckleme:<\/strong> JavaScript kodunuzda modeli y\u00fcklemek olduk\u00e7a basittir:<\/p>\n import * as tf from '@tensorflow\/tfjs';\n import * as qna from '@tensorflow-models\/qna';\n\n let model;\n\n async function loadQAModel() {\n console.log('Soru-Cevap modeli y\u00fckleniyor...');\n try {\n \/\/ WebGL backend'i kullanmak performans\u0131 art\u0131rabilir\n await tf.setBackend('webgl');\n model = await qna.load();\n console.log('Model ba\u015far\u0131yla y\u00fcklendi.');\n \/\/ UI'da y\u00fckleme durumunu g\u00fcncelleyin\n document.getElementById('status').innerText = 'Model y\u00fcklendi. Haz\u0131r!';\n } catch (error) {\n console.error('Model y\u00fcklenirken bir hata olu\u015ftu:', error);\n document.getElementById('status').innerText = 'Model y\u00fcklenemedi: ' + error.message;\n }\n }\n\n loadQAModel();<\/code><\/pre>\n qna.load()<\/code> fonksiyonu, varsay\u0131lan olarak optimize edilmi\u015f MobileBERT modelini y\u00fckler. Bu model, taray\u0131c\u0131 ortam\u0131nda iyi performans g\u00f6stermek \u00fczere tasarlanm\u0131\u015ft\u0131r.<\/p>\n Model D\u00f6n\u00fc\u015ft\u00fcrme (\u0130ste\u011fe Ba\u011fl\u0131)<\/h4>\nE\u011fer kendi \u00f6zel soru-cevap modelinizi Python’da e\u011fittiyseniz (\u00f6rne\u011fin Keras veya TensorFlow ile) ve bunu TensorFlow.js’de kullanmak istiyorsan\u0131z, modeli d\u00f6n\u00fc\u015ft\u00fcrmeniz gerekir.<\/p>\n 1. TensorFlow.js Converter Kurulumu:<\/strong><\/p>\n pip install tensorflowjs<\/code><\/pre>\n2. Modeli D\u00f6n\u00fc\u015ft\u00fcrme:<\/strong> Modelinizi SavedModel format\u0131nda kaydettikten sonra (\u00f6rne\u011fin my_qa_model<\/code> klas\u00f6r\u00fcnde), a\u015fa\u011f\u0131daki komutu kullanarak d\u00f6n\u00fc\u015ft\u00fcrebilirsiniz:<\/p>\n tensorflowjs_converter --input_format=tf_saved_model \\\n --output_format=tfjs_graph_model \\\n --output_node_names='output_node_name' \\ # Modelinizin \u00e7\u0131k\u0131\u015f d\u00fc\u011f\u00fcm\u00fcn\u00fcn ad\u0131n\u0131 buraya girin\n my_qa_model \\\n .\/tfjs_model_output<\/code><\/pre>\n Bu komut, tfjs_model_output<\/code> klas\u00f6r\u00fcne model.json<\/code> ve a\u011f\u0131rl\u0131k dosyalar\u0131n\u0131 (group1-shard1ofX.bin<\/code>) kaydedecektir. Daha sonra bu dosyalar\u0131 uygulaman\u0131z\u0131n public klas\u00f6r\u00fcne koyarak kendi modelinizi y\u00fckleyebilirsiniz:<\/p>\n model = await tf.loadGraphModel('.\/tfjs_model_output\/model.json');<\/code><\/pre>\n Ancak, bu makalede @tensorflow-models\/qna<\/code> paketinin sundu\u011fu haz\u0131r modeli kullanmaya odaklanaca\u011f\u0131z, \u00e7\u00fcnk\u00fc bu, ba\u015flang\u0131\u00e7 i\u00e7in en basit ve en yayg\u0131n yakla\u015f\u0131md\u0131r.<\/p>\n Uygulama Geli\u015ftirme<\/h3>\nModelimiz haz\u0131r oldu\u011funa g\u00f6re, kullan\u0131c\u0131 aray\u00fcz\u00fcn\u00fc (UI) ve temel JavaScript mant\u0131\u011f\u0131n\u0131 olu\u015fturabiliriz.<\/p>\n Frontend Yap\u0131s\u0131 (HTML, CSS)<\/h4>\nBasit bir HTML dosyas\u0131 ile ba\u015flayal\u0131m. Bu dosya, ba\u011flam metni i\u00e7in bir textarea, soru i\u00e7in bir input alan\u0131 ve cevaplar\u0131n g\u00f6sterilece\u011fi bir b\u00f6l\u00fcm i\u00e7erecektir.<\/p>\n \n\n\n \n \n Soru-Cevap Uygulamas\u0131 (TensorFlow.js)<\/title>\n <style>\n body {\n font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;\n margin: 20px;\n background-color: #f4f7f6;\n color: #333;\n line-height: 1.6;\n }\n .container {\n max-width: 800px;\n margin: 0 auto;\n background-color: #ffffff;\n padding: 30px;\n border-radius: 8px;\n box-shadow: 0 4px 12px rgba(0, 0, 0, 0.08);\n }\n h1, h2 {\n color: #2c3e50;\n text-align: center;\n margin-bottom: 25px;\n }\n label {\n display: block;\n margin-bottom: 8px;\n font-weight: bold;\n color: #34495e;\n }\n textarea, input[type=\"text\"] {\n width: calc(100% - 20px);\n padding: 12px;\n margin-bottom: 20px;\n border: 1px solid #ccc;\n border-radius: 5px;\n font-size: 16px;\n box-sizing: border-box;\n resize: vertical;\n }\n button {\n background-color: #3498db;\n color: white;\n padding: 12px 25px;\n border: none;\n border-radius: 5px;\n cursor: pointer;\n font-size: 18px;\n transition: background-color 0.3s ease;\n width: 100%;\n box-sizing: border-box;\n }\n button:hover {\n background-color: #2980b9;\n }\n button:disabled {\n background-color: #cccccc;\n cursor: not-allowed;\n }\n #status {\n text-align: center;\n margin-top: 20px;\n font-style: italic;\n color: #7f8c8d;\n }\n #answers {\n margin-top: 30px;\n padding: 20px;\n background-color: #ecf0f1;\n border-radius: 5px;\n border-left: 5px solid #3498db;\n min-height: 50px;\n }\n .answer-item {\n margin-bottom: 10px;\n padding: 10px;\n background-color: #ffffff;\n border-radius: 5px;\n box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);\n }\n .answer-item strong {\n color: #2c3e50;\n }\n .loading-spinner {\n border: 4px solid #f3f3f3;\n border-top: 4px solid #3498db;\n border-radius: 50%;\n width: 20px;\n height: 20px;\n animation: spin 1s linear infinite;\n display: inline-block;\n vertical-align: middle;\n margin-left: 10px;\n }\n @keyframes spin {\n 0% { transform: rotate(0deg); }\n 100% { transform: rotate(360deg); }\n }\n <\/style>\n<\/head>\n<body>\n <div class=\"container\">\n \n Model y\u00fckleniyor...<\/p>\n\n <label for=\"context\">Ba\u011flam Metni:<\/label>\n <textarea id=\"context\" rows=\"10\" placeholder=\"L\u00fctfen cevap aramak istedi\u011finiz metni buraya yap\u0131\u015ft\u0131r\u0131n.\"><\/textarea>\n\n <label for=\"question\">Sorunuz:<\/label>\n <input type=\"text\" id=\"question\" placeholder=\"Metinle ilgili bir soru sorun.\">\n\n <button id=\"askButton\" disabled>Cevap Bul<\/button>\n\n <h2>Cevaplar:<\/h2>\n <div id=\"answers\">\n Burada cevaplar g\u00f6r\u00fcnt\u00fclenecektir.<\/p>\n <\/div>\n <\/div>\n\n <script src=\"https:\/\/cdn.jsdelivr.net\/npm\/@tensorflow\/tfjs\"><\/script>\n <script src=\"https:\/\/cdn.jsdelivr.net\/npm\/@tensorflow-models\/qna\"><\/script>\n <script src=\"app.js\"><\/script> \n<\/body>\n<\/html><\/code><\/pre>\nYukar\u0131daki HTML dosyas\u0131nda, TensorFlow.js ve <code>@tensorflow-models\/qna<\/code> k\u00fct\u00fcphanelerini do\u011frudan CDN’den y\u00fckl\u00fcyoruz. Bu, basit uygulamalar i\u00e7in pratik bir yakla\u015f\u0131md\u0131r. Daha karma\u015f\u0131k uygulamalarda, bunlar\u0131 <code>npm<\/code> ile kurup bir bundler (Webpack, Rollup, Vite) kullanarak bir araya getirmek daha yayg\u0131nd\u0131r.<\/p>\n<h4>JavaScript Mant\u0131\u011f\u0131 (<code>app.js<\/code>)<\/h4>\n\u015eimdi <code>app.js<\/code> dosyas\u0131n\u0131 olu\u015ftural\u0131m ve soru-cevap mant\u0131\u011f\u0131n\u0131 ekleyelim.<\/p>\n<pre><code class=\"language-javascript\">\/\/ app.js\n\nlet model;\nconst statusElement = document.getElementById('status');\nconst contextInput = document.getElementById('context');\nconst questionInput = document.getElementById('question');\nconst askButton = document.getElementById('askButton');\nconst answersDiv = document.getElementById('answers');\n\n\/\/ Modeli y\u00fckleme fonksiyonu\nasync function loadQAModel() {\n statusElement.innerText = 'Soru-Cevap modeli y\u00fckleniyor...';\n askButton.disabled = true; \/\/ Model y\u00fcklenirken butonu devre d\u0131\u015f\u0131 b\u0131rak\n try {\n \/\/ En iyi performans\u0131 sa\u011flamak i\u00e7in WebGL veya WASM backend'ini se\u00e7in\n \/\/ Taray\u0131c\u0131 deste\u011fine g\u00f6re otomatik se\u00e7im veya manuel tercih yap\u0131labilir.\n \/\/ \u00d6rne\u011fin, 'webgl' GPU h\u0131zland\u0131rmas\u0131 sa\u011flar, 'wasm' CPU performans\u0131n\u0131 art\u0131r\u0131r.\n if (await tf.setBackend('webgl').then(() => true).catch(() => false)) {\n console.log('WebGL backend kullan\u0131l\u0131yor.');\n } else if (await tf.setBackend('wasm').then(() => true).catch(() => false)) {\n console.log('WASM backend kullan\u0131l\u0131yor.');\n } else {\n console.log('CPU backend kullan\u0131l\u0131yor (performans daha d\u00fc\u015f\u00fck olabilir).');\n }\n \n model = await qna.load();\n statusElement.innerText = 'Model y\u00fcklendi. Haz\u0131r!';\n askButton.disabled = false; \/\/ Model y\u00fcklendi\u011finde butonu etkinle\u015ftir\n console.log('Model ba\u015far\u0131yla y\u00fcklendi.');\n } catch (error) {\n statusElement.innerText = <code>Model y\u00fcklenemedi: ${error.message}<\/code>;\n console.error('Model y\u00fcklenirken bir hata olu\u015ftu:', error);\n }\n}\n\n\/\/ Cevap bulma fonksiyonu\nasync function findAnswers() {\n if (!model) {\n alert('Model hen\u00fcz y\u00fcklenmedi. L\u00fctfen bekleyin.');\n return;\n }\n\n const context = contextInput.value;\n const question = questionInput.value;\n\n if (!context || !question) {\n alert('L\u00fctfen hem ba\u011flam metnini hem de soruyu girin.');\n return;\n }\n\n answersDiv.innerHTML = '<\/span> Cevaplar aran\u0131yor...<\/p>';\n askButton.disabled = true; \/\/ \u0130\u015flem s\u0131ras\u0131nda butonu devre d\u0131\u015f\u0131 b\u0131rak\n\n try {\n \/\/ Modelden cevaplar\u0131 al\n \/\/ model.findAnswers(question, context) varsay\u0131lan olarak en iyi 5 cevab\u0131 d\u00f6nd\u00fcr\u00fcr.\n \/\/ Options nesnesi ile topk (ka\u00e7 cevap d\u00f6nece\u011fi) gibi parametreleri ayarlayabiliriz.\n const answers = await model.findAnswers(question, context, { topk: 3 });\n\n if (answers && answers.length > 0) {\n answersDiv.innerHTML = ''; \/\/ \u00d6nceki cevaplar\u0131 temizle\n answers.forEach((answer, index) => {\n const answerItem = document.createElement('div');\n answerItem.className = 'answer-item';\n answerItem.innerHTML = <code>\n Cevap ${index + 1}:<\/strong> ${answer.text}<\/p>\n G\u00fcven (Skor):<\/em> ${(answer.score * 100).toFixed(2)}%<\/p>\n <\/code>;\n answersDiv.appendChild(answerItem);\n });\n\n \/\/ Ba\u011flam metninde cevab\u0131 vurgulama (iste\u011fe ba\u011fl\u0131 ama g\u00fczel bir \u00f6zellik)\n highlightAnswerInContext(context, answers[0].text, answers[0].startIndex);\n\n } else {\n answersDiv.innerHTML = 'Bu soruya ba\u011flam i\u00e7inde bir cevap bulunamad\u0131.<\/p>';\n }\n } catch (error) {\n answersDiv.innerHTML = <code>Cevap bulunurken bir hata olu\u015ftu: ${error.message}<\/p><\/code>;\n console.error('Cevap bulunurken bir hata olu\u015ftu:', error);\n } finally {\n askButton.disabled = false; \/\/ \u0130\u015flem bitince butonu tekrar etkinle\u015ftir\n }\n}\n\n\/\/ Ba\u011flam metninde cevab\u0131 vurgulama fonksiyonu\nfunction highlightAnswerInContext(context, answerText, startIndex) {\n const endIndex = startIndex + answerText.length;\n const highlightedContext = \n context.substring(0, startIndex) +\n <code>${context.substring(startIndex, endIndex)}<\/mark><\/code> +\n context.substring(endIndex);\n \n \/\/ Ba\u011flam metni input alan\u0131n\u0131 sadece vurgulama i\u00e7in de\u011fi\u015ftiriyoruz.\n \/\/ Ancak textarea'n\u0131n HTML i\u00e7eri\u011fini do\u011frudan de\u011fi\u015ftiremeyiz.\n \/\/ Bunun yerine, vurgulanm\u0131\u015f metni ayr\u0131 bir div'de g\u00f6sterebiliriz.\n \/\/ \u015eu anki implementasyon sadece cevab\u0131 g\u00f6steriyor, vurgulama i\u00e7in daha karma\u015f\u0131k bir UI gerekir.\n \/\/ Bu k\u0131s\u0131m UI\/UX iyile\u015ftirmesi olarak d\u00fc\u015f\u00fcn\u00fclebilir.\n console.log(\"Cevap vurguland\u0131 (konsolda):\", highlightedContext);\n \/\/ Ger\u00e7ek bir uygulamada, ba\u011flam metnini bir div'e al\u0131p innerHTML ile vurgulanm\u0131\u015f metni g\u00f6sterebilirdik.\n \/\/ \u00d6rne\u011fin: document.getElementById('highlightedContext').innerHTML = highlightedContext;\n}\n\n\n\/\/ Olay dinleyicileri\naskButton.addEventListener('click', findAnswers);\nquestionInput.addEventListener('keypress', (e) => {\n if (e.key === 'Enter' && !askButton.disabled) {\n findAnswers();\n }\n});\n\n\/\/ Uygulama y\u00fcklendi\u011finde modeli ba\u015flat\nloadQAModel();<\/code><\/pre>\nBu JavaScript kodu: \n1. TensorFlow.js ve QnA modelini asenkron olarak y\u00fckler. \n2. Y\u00fckleme durumu ve hata mesajlar\u0131n\u0131 kullan\u0131c\u0131ya bildirir. \n3. “Cevap Bul” d\u00fc\u011fmesine t\u0131kland\u0131\u011f\u0131nda veya soru alan\u0131nda Enter tu\u015funa bas\u0131ld\u0131\u011f\u0131nda <code>findAnswers<\/code> fonksiyonunu tetikler. \n4. <code>findAnswers<\/code> fonksiyonu, ba\u011flam ve soru metinlerini alarak <code>model.findAnswers()<\/code> metodunu \u00e7a\u011f\u0131r\u0131r. \n5. Modelden gelen cevaplar\u0131 (metin ve g\u00fcven skoru ile birlikte) <code>answersDiv<\/code> elementinde g\u00f6r\u00fcnt\u00fcler. \n6. \u0130\u015flem s\u0131ras\u0131nda UI’da geri bildirim (y\u00fckleme spinner’\u0131, d\u00fc\u011fme devre d\u0131\u015f\u0131 b\u0131rakma) sa\u011flar. \n7. <code>highlightAnswerInContext<\/code> fonksiyonu, cevab\u0131n ba\u011flam metni i\u00e7inde nerede oldu\u011funu g\u00f6stermek i\u00e7in basit bir vurgulama mant\u0131\u011f\u0131 sunar (\u015fu an i\u00e7in konsolda g\u00f6steriliyor, ger\u00e7ek UI entegrasyonu i\u00e7in ek \u00e7al\u0131\u015fma gerekebilir).<\/p>\n<h4>Kullan\u0131c\u0131 Aray\u00fcz\u00fc Tasar\u0131m\u0131 (UI\/UX)<\/h4>\nYukar\u0131daki CSS, temel bir d\u00fczen ve stil sa\u011flar. \u0130yi bir kullan\u0131c\u0131 deneyimi i\u00e7in \u015funlar\u0131 g\u00f6z \u00f6n\u00fcnde bulundurmal\u0131s\u0131n\u0131z: \n* Duyarl\u0131l\u0131k (Responsiveness):<\/strong> Uygulaman\u0131z\u0131n farkl\u0131 ekran boyutlar\u0131nda (mobil, tablet, masa\u00fcst\u00fc) iyi g\u00f6r\u00fcnmesini sa\u011flay\u0131n. \n* Geri Bildirim:<\/strong> Modelin y\u00fcklendi\u011fini, cevaplar\u0131n arand\u0131\u011f\u0131n\u0131 ve herhangi bir hata olu\u015ftu\u011funu kullan\u0131c\u0131ya a\u00e7\u0131k\u00e7a bildirin. Y\u00fckleme spinner’lar\u0131 veya durum mesajlar\u0131 bu konuda yard\u0131mc\u0131 olur. \n* A\u00e7\u0131kl\u0131k:<\/strong> Ba\u011flam ve soru alanlar\u0131n\u0131n ne i\u00e7in oldu\u011funu net bir \u015fekilde belirtin. Cevaplar\u0131 anla\u015f\u0131l\u0131r bir formatta sunun (\u00f6rne\u011fin, g\u00fcven skorlar\u0131yla birlikte). \n* Giri\u015f Do\u011frulamas\u0131:<\/strong> Bo\u015f ba\u011flam veya soru giri\u015fi gibi durumlar\u0131 ele al\u0131n.<\/p>\n<h3>Da\u011f\u0131t\u0131m Stratejileri<\/h3>\nWeb uygulaman\u0131z haz\u0131r oldu\u011funda, onu bir uygulama platformuna da\u011f\u0131tman\u0131n zaman\u0131 gelmi\u015ftir. TensorFlow.js modelleri istemci taraf\u0131nda \u00e7al\u0131\u015ft\u0131\u011f\u0131 i\u00e7in, \u00e7o\u011fu durumda basit bir statik web sitesi da\u011f\u0131t\u0131m\u0131 yeterli olacakt\u0131r.<\/p>\n<h4>Statik Web Sitesi Da\u011f\u0131t\u0131m\u0131<\/h4>\nBu, en yayg\u0131n ve en basit da\u011f\u0131t\u0131m y\u00f6ntemidir. Uygulaman\u0131z\u0131n t\u00fcm HTML, CSS ve JavaScript dosyalar\u0131 bir web sunucusunda bar\u0131nd\u0131r\u0131l\u0131r ve kullan\u0131c\u0131 taray\u0131c\u0131s\u0131 taraf\u0131ndan do\u011frudan eri\u015filir.<\/p>\nPop\u00fcler Platformlar:<\/strong><\/p>\n* Vercel:<\/strong> <code>git push<\/code> ile kolayca da\u011f\u0131t\u0131m, otomatik SSL, CDN, sunucusuz fonksiyon deste\u011fi. Next.js, React gibi framework’lerle iyi entegrasyon. \n* Netlify:<\/strong> Vercel’e benzer \u00f6zellikler, \u00fccretsiz katman, kolay Git entegrasyonu, sunucusuz fonksiyonlar. \n* GitHub Pages:<\/strong> GitHub depolar\u0131ndan do\u011frudan statik web sitesi yay\u0131nlama imkan\u0131. En basit se\u00e7eneklerden biri. \n* AWS S3 + CloudFront:<\/strong> AWS kullan\u0131c\u0131lar\u0131 i\u00e7in g\u00fc\u00e7l\u00fc ve \u00f6l\u00e7eklenebilir bir \u00e7\u00f6z\u00fcm. S3 statik dosyalar\u0131 bar\u0131nd\u0131r\u0131rken, CloudFront bir CDN g\u00f6revi g\u00f6r\u00fcr. \n* DigitalOcean App Platform:<\/strong> Hem statik siteleri hem de tam stack uygulamalar\u0131 destekleyen, y\u00f6netilen bir platform.<\/p>\nGenel Da\u011f\u0131t\u0131m Ad\u0131mlar\u0131 (\u00d6rnek: Vercel\/Netlify):<\/strong><\/p>\n1. Git Deposunda Bar\u0131nd\u0131rma:<\/strong> Uygulaman\u0131z\u0131n kodunu bir Git deposuna (\u00f6rn. GitHub) y\u00fckleyin. \n2. Platforma Ba\u011flanma:<\/strong> Se\u00e7ti\u011finiz uygulama platformunun web aray\u00fcz\u00fcne gidin ve GitHub hesab\u0131n\u0131z\u0131 ba\u011flay\u0131n. \n3. Depoyu Se\u00e7me:<\/strong> Da\u011f\u0131tmak istedi\u011finiz depoyu se\u00e7in. \n4. Yap\u0131land\u0131rma:<\/strong> \n * Build Command (Derleme Komutu):<\/strong> E\u011fer uygulaman\u0131z bir build ad\u0131m\u0131 gerektiriyorsa (\u00f6rne\u011fin, React i\u00e7in <code>npm run build<\/code>), bu komutu belirtin. Bizim basit HTML\/JS \u00f6rne\u011fimiz i\u00e7in bu ad\u0131ma gerek yoktur veya bo\u015f b\u0131rak\u0131labilir. \n * Output Directory (\u00c7\u0131kt\u0131 Dizini):<\/strong> Derlenmi\u015f dosyalar\u0131n bulundu\u011fu dizini belirtin (\u00f6rne\u011fin, <code>build<\/code>, <code>dist<\/code>, <code>public<\/code>). Bizim \u00f6rne\u011fimizde, t\u00fcm dosyalar k\u00f6k dizinde oldu\u011fu i\u00e7in bu da bo\u015f b\u0131rak\u0131labilir veya <code>.<\/code> olarak ayarlanabilir. \n5. Da\u011f\u0131t\u0131m:<\/strong> Platform, kodunuzu otomatik olarak \u00e7ekip derleyecek (e\u011fer varsa) ve bir URL \u00fczerinden yay\u0131nlayacakt\u0131r. \n6. \u00d6zel Alan Ad\u0131 (Custom Domain):<\/strong> \u0130ste\u011fe ba\u011fl\u0131 olarak, uygulaman\u0131za kendi alan ad\u0131n\u0131z\u0131 (\u00f6rne\u011fin <code>sorucevap.com<\/code>) ba\u011flayabilirsiniz. Platformlar genellikle \u00fccretsiz SSL sertifikas\u0131 sa\u011flar.<\/p>\n<h4>Sunucusuz Fonksiyonlarla Da\u011f\u0131t\u0131m (\u0130ste\u011fe Ba\u011fl\u0131 ve Geli\u015fmi\u015f)<\/h4>\n\u00c7o\u011fu TensorFlow.js istemci taraf\u0131 uygulamas\u0131 i\u00e7in sunucusuz fonksiyonlara gerek yoktur. Ancak, a\u015fa\u011f\u0131daki senaryolarda faydal\u0131 olabilirler: \n* B\u00fcy\u00fck Modeller:<\/strong> E\u011fer modeliniz taray\u0131c\u0131da \u00e7al\u0131\u015famayacak kadar b\u00fcy\u00fckse, sunucusuz bir fonksiyonda modelin tahminlerini yap\u0131p sadece sonucu istemciye g\u00f6nderebilirsiniz. Bu durumda, TensorFlow.js yerine Python tabanl\u0131 TensorFlow veya PyTorch gibi bir k\u00fct\u00fcphane sunucu taraf\u0131nda \u00e7al\u0131\u015f\u0131r. \n* D\u0131\u015f Veri Kaynaklar\u0131:<\/strong> Uygulaman\u0131z\u0131n ba\u011flam metnini bir veritaban\u0131ndan, harici bir API’den veya depolama hizmetinden \u00e7ekmesi gerekiyorsa, sunucusuz fonksiyonlar bu i\u015flemleri g\u00fcvenli bir \u015fekilde ger\u00e7ekle\u015ftirebilir. \n* Kullan\u0131c\u0131 Kimlik Do\u011frulama\/Yetkilendirme:<\/strong> Kullan\u0131c\u0131 oturumlar\u0131n\u0131 y\u00f6netmek veya belirli \u00f6zelliklere eri\u015fimi k\u0131s\u0131tlamak i\u00e7in sunucusuz fonksiyonlar kullan\u0131labilir.<\/p>\nBu senaryolarda, uygulaman\u0131z\u0131n frontend’i statik olarak bar\u0131nd\u0131r\u0131l\u0131rken, JavaScript kodunuz sunucusuz fonksiyonlar\u0131 \u00e7a\u011f\u0131rarak backend i\u015flemlerini tetikler. Vercel ve Netlify gibi platformlar, bu t\u00fcr hibrit da\u011f\u0131t\u0131mlar\u0131 kolayca destekler.<\/p>\n<h3>Performans ve Optimizasyon<\/h3>\nTensorFlow.js modellerini taray\u0131c\u0131da \u00e7al\u0131\u015ft\u0131rmak, performans optimizasyonlar\u0131n\u0131 kritik hale getirir.<\/p>\n* Model Boyutu ve Y\u00fckleme S\u00fcresi:<\/strong> \n * K\u00fc\u00e7\u00fck Modeller Kullan\u0131n:<\/strong> MobileBERT gibi daha k\u00fc\u00e7\u00fck, mobil uyumlu modeller, daha h\u0131zl\u0131 y\u00fckleme ve daha d\u00fc\u015f\u00fck bellek t\u00fcketimi sunar. <code>@tensorflow-models\/qna<\/code> paketi zaten bu t\u00fcr modelleri varsay\u0131lan olarak kullan\u0131r. \n * Model A\u011f\u0131rl\u0131klar\u0131n\u0131 \u00d6nbelle\u011fe Al\u0131n:<\/strong> Taray\u0131c\u0131 \u00f6nbellekleme mekanizmalar\u0131n\u0131 kullanarak model a\u011f\u0131rl\u0131klar\u0131n\u0131n tekrar tekrar indirilmesini \u00f6nleyin. CDN’ler genellikle bunu otomatik olarak yapar. \n * Tembel Y\u00fckleme (Lazy Loading):<\/strong> Modelin hemen uygulama ba\u015flang\u0131c\u0131nda de\u011fil, ihtiya\u00e7 duyuldu\u011funda (\u00f6rne\u011fin, kullan\u0131c\u0131 soru sorma b\u00f6l\u00fcm\u00fcne geldi\u011finde) y\u00fcklenmesini sa\u011flay\u0131n.<\/p>\n* Backend Se\u00e7imi:<\/strong> \n * <code>tf.setBackend('webgl')<\/code>: GPU h\u0131zland\u0131rmas\u0131 sa\u011flar ve \u00f6zellikle b\u00fcy\u00fck modeller veya yo\u011fun hesaplama gerektiren i\u015flemler i\u00e7in performans\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde art\u0131rabilir. Ancak, her taray\u0131c\u0131 veya cihaz WebGL’i desteklemeyebilir. \n * <code>tf.setBackend('wasm')<\/code>: WebAssembly (WASM) tabanl\u0131 CPU h\u0131zland\u0131rmas\u0131 sunar. WebGL’in desteklenmedi\u011fi durumlarda veya CPU tabanl\u0131 i\u015flemler i\u00e7in iyi bir alternatiftir. \n * <code>tf.setBackend('cpu')<\/code>: En temel backend’dir ve her zaman \u00e7al\u0131\u015f\u0131r, ancak en yava\u015ft\u0131r. \n * Otomatik Se\u00e7im ve Geri D\u00f6n\u00fc\u015f:<\/strong> Uygulaman\u0131z\u0131n mevcut cihaz ve taray\u0131c\u0131ya g\u00f6re en uygun backend’i otomatik olarak se\u00e7mesini ve birincil backend ba\u015far\u0131s\u0131z olursa di\u011ferine geri d\u00f6nmesini sa\u011flay\u0131n. Yukar\u0131daki <code>loadQAModel<\/code> fonksiyonunda basit bir \u00f6rnek verilmi\u015ftir.<\/p>\n* Bellek Kullan\u0131m\u0131:<\/strong> \n * Tensorleri \u0130mha Edin:<\/strong> TensorFlow.js, bellekte tensorler (veri yap\u0131lar\u0131) olu\u015fturur. Bu tensorler i\u015finiz bitti\u011finde manuel olarak <code>tf.dispose()<\/code> veya <code>tf.tidy()<\/code> fonksiyonlar\u0131n\u0131 kullanarak serbest b\u0131rak\u0131lmal\u0131d\u0131r. Bu, \u00f6zellikle modelden birden \u00e7ok tahmin yap\u0131ld\u0131\u011f\u0131nda bellek s\u0131z\u0131nt\u0131lar\u0131n\u0131 \u00f6nler. \n * <code>tf.tidy()<\/code>: Bir fonksiyon i\u00e7inde olu\u015fturulan t\u00fcm tensorleri otomatik olarak imha eden kullan\u0131\u015fl\u0131 bir ara\u00e7t\u0131r.<\/p>\n<pre><code class=\"language-javascript\">async function findAnswers() {\n \/\/ ...\n const answers = tf.tidy(() => {\n return model.findAnswers(question, context, { topk: 3 });\n });\n \/\/ ...\n }<\/code><\/pre>\n* Kullan\u0131c\u0131 Deneyimi:<\/strong> \n * \u0130lerleme G\u00f6stergeleri:<\/strong> Model y\u00fcklenirken veya cevap aran\u0131rken kullan\u0131c\u0131ya g\u00f6rsel geri bildirim sa\u011flay\u0131n (spinner, ilerleme \u00e7ubu\u011fu). \n * Giri\u015f Do\u011frulama ve Debounce:<\/strong> Kullan\u0131c\u0131 \u00e7ok h\u0131zl\u0131 yazarken gereksiz hesaplamalar\u0131 \u00f6nlemek i\u00e7in <code>debounce<\/code> fonksiyonlar\u0131 kullanabilirsiniz.<\/p>\n<h3>G\u00fcvenlik ve Gizlilik<\/h3>\nTensorFlow.js’in istemci taraf\u0131nda \u00e7al\u0131\u015fmas\u0131, g\u00fcvenlik ve gizlilik a\u00e7\u0131s\u0131ndan \u00f6nemli avantajlar sunar:<\/p>\n* Veri Gizlili\u011fi:<\/strong> Kullan\u0131c\u0131 verileri (ba\u011flam metni, sorular) kullan\u0131c\u0131n\u0131n cihaz\u0131ndan ayr\u0131lmaz. Bu, \u00f6zellikle hassas verilerle \u00e7al\u0131\u015f\u0131rken kritik bir gizlilik avantaj\u0131d\u0131r. Sunucu taraf\u0131na veri g\u00f6nderilmedi\u011fi i\u00e7in, veri ihlali riski azal\u0131r. \n* Model G\u00fcvenli\u011fi:<\/strong> Model a\u011f\u0131rl\u0131klar\u0131 ve mimarisi, genellikle uygulaman\u0131zla birlikte herkese a\u00e7\u0131k olarak sunulur. Bu, modelin nas\u0131l \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 merak edenler i\u00e7in \u015feffafl\u0131k sa\u011flar. Ancak, modelin \u00e7al\u0131nmas\u0131 veya tersine m\u00fchendislikle incelenmesi riski de vard\u0131r. Ticari s\u0131r niteli\u011findeki modeller i\u00e7in bu bir dezavantaj olabilir. \n* Ba\u011flant\u0131 G\u00fcvenli\u011fi (HTTPS):<\/strong> Uygulaman\u0131z\u0131 da\u011f\u0131tt\u0131\u011f\u0131n\u0131z platformun (Vercel, Netlify vb.) otomatik olarak sa\u011flad\u0131\u011f\u0131 HTTPS sertifikalar\u0131n\u0131 kullan\u0131n. Bu, kullan\u0131c\u0131n\u0131n taray\u0131c\u0131s\u0131 ile sunucu aras\u0131ndaki t\u00fcm ileti\u015fimin \u015fifrelenmesini sa\u011flar ve k\u00f6t\u00fc niyetli m\u00fcdahaleleri \u00f6nler.<\/p>\n<h3>Gelecek ve \u0130leri Ad\u0131mlar<\/h3>\nBu temel soru-cevap uygulamas\u0131n\u0131 kurduktan sonra, projeyi daha da geli\u015ftirmek i\u00e7in bir\u00e7ok yol vard\u0131r:<\/p>\n* \u00d6zel Model E\u011fitimi:<\/strong> Kendi \u00f6zel veri k\u00fcmeniz \u00fczerinde bir soru-cevap modeli e\u011fitebilir ve bunu TensorFlow.js format\u0131na d\u00f6n\u00fc\u015ft\u00fcrerek uygulaman\u0131zda kullanabilirsiniz. Bu, daha ni\u015f veya sekt\u00f6re \u00f6zel uygulamalar i\u00e7in idealdir. \n* Daha Karma\u015f\u0131k NLP G\u00f6revleri:<\/strong> Metin \u00f6zetleme, duygu analizi, metin s\u0131n\u0131fland\u0131rma gibi di\u011fer NLP g\u00f6revlerini de TensorFlow.js ile entegre edebilirsiniz. \n* Vurgulama ve Etkile\u015fim:<\/strong> Cevaplar\u0131n ba\u011flam metni i\u00e7inde g\u00f6rsel olarak vurgulanmas\u0131, kullan\u0131c\u0131 deneyimini \u00f6nemli \u00f6l\u00e7\u00fcde iyile\u015ftirir. Metin se\u00e7imi olaylar\u0131n\u0131 dinleyerek ba\u011flam\u0131 dinamik olarak de\u011fi\u015ftirebilirsiniz. \n* Daha \u0130yi Kullan\u0131c\u0131 Aray\u00fcz\u00fc:<\/strong> Bir frontend framework’\u00fc (React, Vue, Angular) kullanarak daha dinamik, y\u00f6netilebilir ve \u00f6l\u00e7eklenebilir bir kullan\u0131c\u0131 aray\u00fcz\u00fc olu\u015fturabilirsiniz. \n* Geri Bildirim Mekanizmalar\u0131:<\/strong> Kullan\u0131c\u0131lardan gelen geri bildirimleri toplayarak (\u00f6rne\u011fin, “Bu cevap yard\u0131mc\u0131 oldu mu?”) modelin performans\u0131n\u0131 izleyebilir ve gelecekteki geli\u015ftirmeler i\u00e7in veri toplayabilirsiniz. \n* \u00c7evrimd\u0131\u015f\u0131 Destek:<\/strong> Service Worker’lar kullanarak uygulaman\u0131z\u0131n ve model a\u011f\u0131rl\u0131klar\u0131n\u0131n \u00e7evrimd\u0131\u015f\u0131 \u00f6nbelle\u011fe al\u0131nmas\u0131n\u0131 sa\u011flayabilir, b\u00f6ylece kullan\u0131c\u0131lar internet ba\u011flant\u0131s\u0131 olmasa bile uygulamay\u0131 kullanabilirler.<\/p>\n<h3>Sonu\u00e7<\/h3>\n\u00d6nceden e\u011fitilmi\u015f bir soru-cevap TensorFlow.js modelini bir uygulama platformuna da\u011f\u0131tmak, modern web geli\u015ftirmenin en heyecan verici y\u00f6nlerinden biridir. Bu rehberde, model haz\u0131rl\u0131\u011f\u0131ndan uygulama geli\u015ftirmeye ve nihai da\u011f\u0131t\u0131ma kadar t\u00fcm s\u00fcreci ele ald\u0131k. TensorFlow.js’in g\u00fcc\u00fc ve modern uygulama platformlar\u0131n\u0131n da\u011f\u0131t\u0131m kolayl\u0131\u011f\u0131 sayesinde, yapay zeka destekli web uygulamalar\u0131n\u0131 geli\u015ftirmek ve yay\u0131nlamak hi\u00e7 bu kadar eri\u015filebilir olmam\u0131\u015ft\u0131.<\/p>\n\u0130ster bir prototip olu\u015fturuyor olun ister tam te\u015fekk\u00fcll\u00fc bir \u00fcr\u00fcn geli\u015ftiriyor olun, istemci taraf\u0131nda \u00e7al\u0131\u015fan makine \u00f6\u011frenimi modelleri, performans, gizlilik ve kullan\u0131c\u0131 deneyimi a\u00e7\u0131s\u0131ndan \u00f6nemli avantajlar sunar. Bu teknolojileri kullanarak, kullan\u0131c\u0131lar\u0131n do\u011frudan taray\u0131c\u0131lar\u0131nda ak\u0131ll\u0131 etkile\u015fimler ger\u00e7ekle\u015ftirebildi\u011fi, yenilik\u00e7i ve g\u00fc\u00e7l\u00fc web uygulamalar\u0131 yaratabilirsiniz. Uygulaman\u0131z\u0131 da\u011f\u0131tt\u0131ktan sonra, s\u00fcrekli iyile\u015ftirme ve optimizasyon ile kullan\u0131c\u0131lar\u0131n\u0131za en iyi deneyimi sunmaya devam edebilirsiniz. Bu alanda denemeler yapmaktan ve s\u0131n\u0131rlar\u0131 zorlamaktan \u00e7ekinmeyin!<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow. js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi TensorFlow.","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-41100","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>\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow.js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi - 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\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow.js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi\" \/>\n<meta property=\"og:description\" content=\"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow. js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi TensorFlow.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/\" \/>\n<meta property=\"og:site_name\" content=\"Kodlar\u0131n Gizemli D\u00fcnyas\u0131\" \/>\n<meta property=\"article:published_time\" content=\"2026-03-03T10:40:52+00:00\" \/>\n<meta name=\"author\" content=\"Fatih Soysal\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Yazan:\" \/>\n\t<meta name=\"twitter:data1\" content=\"Fatih Soysal\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tahmini okuma s\u00fcresi\" \/>\n\t<meta name=\"twitter:data2\" content=\"19 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/\"},\"author\":{\"name\":\"Fatih Soysal\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"headline\":\"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow.js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi\",\"datePublished\":\"2026-03-03T10:40:52+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/\"},\"wordCount\":3092,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/#respond\"]}],\"copyrightYear\":\"2026\",\"copyrightHolder\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/\",\"name\":\"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow.js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi - Kodlar\u0131n Gizemli D\u00fcnyas\u0131\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\"},\"datePublished\":\"2026-03-03T10:40:52+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/#breadcrumb\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Anasayfa\",\"item\":\"https:\/\/fatihsoysal.com\/blog\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow.js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/\",\"name\":\"Fatihsoysal.com\",\"description\":\"Blog - Yaz\u0131l\u0131m D\u00fcnyas\u0131 Tecr\u00fcbelerim\",\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/fatihsoysal.com\/blog\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"tr\"},{\"@type\":[\"Person\",\"Organization\"],\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\",\"name\":\"Fatih Soysal\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"tr\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2024\/04\/cropped-replicate-prediction-3kgg1hgjn5rgp0cf0p5tr0jw7w-1.png\",\"contentUrl\":\"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2024\/04\/cropped-replicate-prediction-3kgg1hgjn5rgp0cf0p5tr0jw7w-1.png\",\"width\":512,\"height\":512,\"caption\":\"Fatih Soysal\"},\"logo\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/image\/\"},\"description\":\"Kullan\u0131m ve kodlama m\u00fckemmeliyetini odak alan uygulamalar olu\u015fturma deneyimine sahip, profesyonel olarak 15+ y\u0131l \u00fczeri deneyime sahip bir yaz\u0131l\u0131m m\u00fchendisi.\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/author\/fatihsoysal\/\"}]}<\/script>\n","yoast_head_json":{"title":"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow.js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi - Kodlar\u0131n Gizemli D\u00fcnyas\u0131","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/","og_locale":"tr_TR","og_type":"article","og_title":"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow.js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi","og_description":"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow. js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi TensorFlow.","og_url":"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/","og_site_name":"Kodlar\u0131n Gizemli D\u00fcnyas\u0131","article_published_time":"2026-03-03T10:40:52+00:00","author":"Fatih Soysal","twitter_card":"summary_large_image","twitter_misc":{"Yazan:":"Fatih Soysal","Tahmini okuma s\u00fcresi":"19 dakika"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/#article","isPartOf":{"@id":"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/"},"author":{"name":"Fatih Soysal","@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1"},"headline":"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow.js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi","datePublished":"2026-03-03T10:40:52+00:00","mainEntityOfPage":{"@id":"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/"},"wordCount":3092,"commentCount":0,"publisher":{"@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1"},"inLanguage":"tr","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/#respond"]}],"copyrightYear":"2026","copyrightHolder":{"@id":"https:\/\/fatihsoysal.com\/blog\/#organization"}},{"@type":"WebPage","@id":"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/","url":"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/","name":"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow.js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi - Kodlar\u0131n Gizemli D\u00fcnyas\u0131","isPartOf":{"@id":"https:\/\/fatihsoysal.com\/blog\/#website"},"datePublished":"2026-03-03T10:40:52+00:00","breadcrumb":{"@id":"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/#breadcrumb"},"inLanguage":"tr","potentialAction":[{"@type":"ReadAction","target":["https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/fatihsoysal.com\/blog\/onceden-egitilmis-bir-soru-cevap-tensorflow-js-modelini-uygulama-platformuna-dagitma-rehberi\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Anasayfa","item":"https:\/\/fatihsoysal.com\/blog\/"},{"@type":"ListItem","position":2,"name":"\u00d6nceden E\u011fitilmi\u015f Bir Soru-Cevap TensorFlow.js Modelini Uygulama Platformuna Da\u011f\u0131tma Rehberi"}]},{"@type":"WebSite","@id":"https:\/\/fatihsoysal.com\/blog\/#website","url":"https:\/\/fatihsoysal.com\/blog\/","name":"Fatihsoysal.com","description":"Blog - Yaz\u0131l\u0131m D\u00fcnyas\u0131 Tecr\u00fcbelerim","publisher":{"@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/fatihsoysal.com\/blog\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"tr"},{"@type":["Person","Organization"],"@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1","name":"Fatih Soysal","image":{"@type":"ImageObject","inLanguage":"tr","@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/image\/","url":"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2024\/04\/cropped-replicate-prediction-3kgg1hgjn5rgp0cf0p5tr0jw7w-1.png","contentUrl":"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2024\/04\/cropped-replicate-prediction-3kgg1hgjn5rgp0cf0p5tr0jw7w-1.png","width":512,"height":512,"caption":"Fatih Soysal"},"logo":{"@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/image\/"},"description":"Kullan\u0131m ve kodlama m\u00fckemmeliyetini odak alan uygulamalar olu\u015fturma deneyimine sahip, profesyonel olarak 15+ y\u0131l \u00fczeri deneyime sahip bir yaz\u0131l\u0131m m\u00fchendisi.","url":"https:\/\/fatihsoysal.com\/blog\/author\/fatihsoysal\/"}]}},"yoast_meta":{"yoast_wpseo_title":"","yoast_wpseo_metadesc":"","yoast_wpseo_canonical":""},"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/posts\/41100","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/comments?post=41100"}],"version-history":[{"count":1,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/posts\/41100\/revisions"}],"predecessor-version":[{"id":41101,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/posts\/41100\/revisions\/41101"}],"wp:attachment":[{"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/media?parent=41100"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/categories?post=41100"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/tags?post=41100"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}

Temel Kavramlar<\/h3>\nBir soru-cevap modelini uygulama platformuna da\u011f\u0131tmadan \u00f6nce, ilgili temel kavramlar\u0131 anlamak \u00f6nemlidir.<\/p>\n

Temel Kavramlar<\/h3>\n
Bir soru-cevap modelini uygulama platformuna da\u011f\u0131tmadan \u00f6nce, ilgili temel kavramlar\u0131 anlamak \u00f6nemlidir.<\/p>\n