{"id":30991,"date":"2025-10-04T11:31:44","date_gmt":"2025-10-04T08:31:44","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/makineler-nasil-ogrenir-yapay-sinir-aglarinin-temellerini-anlamak\/"},"modified":"2025-10-04T11:31:44","modified_gmt":"2025-10-04T08:31:44","slug":"makineler-nasil-ogrenir-yapay-sinir-aglarinin-temellerini-anlamak","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/makineler-nasil-ogrenir-yapay-sinir-aglarinin-temellerini-anlamak\/","title":{"rendered":"Makineler Nas\u0131l \u00d6\u011frenir: Yapay Sinir A\u011flar\u0131n\u0131n Temellerini Anlamak"},"content":{"rendered":"<style>\n  \/* Mobil uyumluluk i\u00e7in temel stil \u00f6rnekleri *\/\n  body {\n    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;\n    line-height: 1.6;\n    color: #333;\n    margin: 0 auto;\n    max-width: 960px;\n    padding: 20px;\n  }\n  h2 {\n    color: #0056b3;\n    border-bottom: 2px solid #eee;\n    padding-bottom: 10px;\n    margin-top: 40px;\n  }\n  h3 {\n    color: #007bff;\n    margin-top: 30px;\n  }\n  p {\n    margin-bottom: 15px;\n  }\n  ul, ol {\n    margin-bottom: 15px;\n    padding-left: 20px;\n  }\n  li {\n    margin-bottom: 8px;\n  }\n  code {\n    background-color: #f4f4f4;\n    padding: 2px 4px;\n    border-radius: 4px;\n  }\n  pre {\n    background-color: #272c34;\n    color: #f8f8f2;\n    padding: 15px;\n    border-radius: 8px;\n    overflow-x: auto;\n    font-size: 0.9em;\n    margin-bottom: 20px;\n  }\n  table {\n    width: 100%;\n    border-collapse: collapse;\n    margin-bottom: 20px;\n  }\n  th, td {\n    border: 1px solid #ddd;\n    padding: 10px;\n    text-align: left;\n  }\n  th {\n    background-color: #f2f2f2;\n    color: #333;\n  }\n  .expert-tip {\n    background-color: #e6f7ff;\n    border-left: 5px solid #007bff;\n    padding: 15px;\n    margin: 20px 0;\n    border-radius: 5px;\n    font-style: italic;\n    color: #0056b3;\n  }\n  .faq-section {\n    background-color: #f9f9f9;\n    border-top: 1px solid #eee;\n    padding-top: 20px;\n    margin-top: 40px;\n  }\n  .faq-question {\n    font-weight: bold;\n    color: #333;\n    margin-bottom: 5px;\n  }\n  .faq-answer {\n    margin-bottom: 15px;\n    color: #555;\n  }<\/p>\n<p>  \/* Mobil cihazlar i\u00e7in medya sorgusu *\/\n  @media (max-width: 768px) {\n    body {\n      padding: 15px;\n      font-size: 0.95em;\n    }\n    h2 {\n      font-size: 1.6em;\n    }\n    h3 {\n      font-size: 1.3em;\n    }\n    pre {\n      font-size: 0.8em;\n      padding: 10px;\n    }\n    table, th, td {\n      font-size: 0.9em;\n      display: block; \/* K\u00fc\u00e7\u00fck ekranlarda tablo s\u00fctunlar\u0131n\u0131 alt alta y\u0131\u011f *\/\n      width: 100%;\n      box-sizing: border-box;\n    }\n    th {\n        display: none; \/* Ba\u015fl\u0131klar\u0131 mobil g\u00f6r\u00fcn\u00fcmde gizle *\/\n    }\n    td:before {\n        content: attr(data-label); \/* data-label kullanarak mobil ba\u015fl\u0131k ekle *\/\n        font-weight: bold;\n        display: inline-block;\n        width: 120px; \/* Label i\u00e7in yer ay\u0131r *\/\n    }\n  }<\/p>\n<p>  \/* Ek medya sorgusu \u00f6rne\u011fi: \u00c7ok k\u00fc\u00e7\u00fck ekranlar i\u00e7in *\/\n  @media (max-width: 480px) {\n    body {\n      padding: 10px;\n      font-size: 0.9em;\n    }\n    h2 {\n      font-size: 1.4em;\n    }\n    h3 {\n      font-size: 1.2em;\n    }\n    .expert-tip {\n        padding: 10px;\n        font-size: 0.9em;\n    }\n  }\n<\/style>\n<p>G\u00fcn\u00fcm\u00fcz d\u00fcnyas\u0131nda ak\u0131ll\u0131 asistanlar, otonom ara\u00e7lar ve ki\u015fiselle\u015ftirilmi\u015f \u00f6neri sistemleri gibi teknolojiler hayat\u0131m\u0131z\u0131n vazge\u00e7ilmez bir par\u00e7as\u0131 haline gelmi\u015ftir. Peki, bu makineler nas\u0131l &#8220;d\u00fc\u015f\u00fcn\u00fcyor&#8221; veya daha do\u011fru ifadeyle &#8220;\u00f6\u011freniyor&#8221;? Bu makine \u00f6\u011frenimi mucizesinin arkas\u0131nda yatan temel mekanizma genellikle yapay sinir a\u011flar\u0131d\u0131r. Bu yaz\u0131da, makinelerin nas\u0131l \u00f6\u011frendi\u011fini, yapay sinir a\u011flar\u0131n\u0131n temel kavramlar\u0131n\u0131 ve ger\u00e7ek d\u00fcnyadaki uygulamalar\u0131n\u0131 ba\u015flang\u0131\u00e7 seviyesinden ileri d\u00fczeye do\u011fru ke\u015ffedece\u011fiz. Haz\u0131rsan\u0131z, bu heyecan verici yolculu\u011fa ba\u015flayal\u0131m!<\/p>\n<p>Etraf\u0131m\u0131zdaki d\u00fcnyay\u0131 g\u00f6zlemledi\u011fimizde, yapay zek\u00e2n\u0131n etkilerini her ge\u00e7en g\u00fcn daha fazla g\u00f6r\u00fcyoruz. Ak\u0131ll\u0131 telefonlar\u0131m\u0131zdaki y\u00fcz tan\u0131ma \u00f6zelliklerinden, sosyal medya ak\u0131\u015flar\u0131m\u0131z\u0131 ki\u015fiselle\u015ftiren algoritmalara, hatta karma\u015f\u0131k bilimsel ara\u015ft\u0131rmalarda veri analizine kadar bir\u00e7ok alanda makineler, insan zek\u00e2s\u0131n\u0131 taklit etme veya a\u015fma kapasitesine ula\u015fm\u0131\u015f durumda. Bu devrimin kalbinde ise makine \u00f6\u011frenimi yat\u0131yor. Makine \u00f6\u011frenimi, sistemlere a\u00e7\u0131k\u00e7a programlanmadan verilerden \u00f6\u011frenme yetene\u011fi kazand\u0131ran bir yapay zek\u00e2 alt dal\u0131d\u0131r.<\/p>\n<p>Geleneksel yaz\u0131l\u0131m geli\u015ftirme yakla\u015f\u0131mlar\u0131nda, bir g\u00f6revi yerine getirecek her kural\u0131 ve ko\u015fulu bizim tan\u0131mlamam\u0131z gerekir. \u00d6rne\u011fin, bir spam e-postas\u0131n\u0131 tespit etmek i\u00e7in &#8220;e\u011fer e-posta ba\u015fl\u0131\u011f\u0131 &#8216;kazand\u0131n\u0131z&#8217; i\u00e7eriyorsa VE g\u00f6nderici adresi \u015f\u00fcpheliyse, o zaman spamd\u0131r&#8221; gibi kesin kurallar yazard\u0131k. Ancak bu yakla\u015f\u0131m, veriler karma\u015f\u0131kla\u015ft\u0131k\u00e7a veya kurallar s\u00fcrekli de\u011fi\u015ftik\u00e7e s\u00fcrd\u00fcr\u00fclemez hale gelir. \u0130\u015fte bu noktada makine \u00f6\u011frenimi devreye girer. Makine \u00f6\u011frenimi algoritmalar\u0131, b\u00fcy\u00fck veri k\u00fcmelerindeki desenleri ve ili\u015fkileri kendi ba\u015flar\u0131na ke\u015ffederek, gelecekteki veriler hakk\u0131nda tahminler yapmay\u0131 veya kararlar almay\u0131 \u00f6\u011frenirler. Bu da onlara dinamik ve s\u00fcrekli de\u011fi\u015fen ortamlarda adaptasyon yetene\u011fi kazand\u0131r\u0131r.<\/p>\n<p>Peki, bu kadar karma\u015f\u0131k g\u00f6revlerin \u00fcstesinden nas\u0131l geliyorlar? Genellikle bu sistemlerin temelinde yapay sinir a\u011flar\u0131 ad\u0131 verilen \u00f6zel bir makine \u00f6\u011frenimi modeli bulunur. Yapay sinir a\u011flar\u0131, insan beynindeki n\u00f6ronlar\u0131n \u00e7al\u0131\u015fma prensibinden esinlenerek tasarlanm\u0131\u015ft\u0131r ve veriler aras\u0131ndaki gizli, do\u011frusal olmayan ili\u015fkileri \u00f6\u011frenme konusunda ola\u011fan\u00fcst\u00fc bir yetene\u011fe sahiptir. Bu a\u011flar, genellikle bir giri\u015f katman\u0131, bir veya daha fazla gizli katman ve bir \u00e7\u0131k\u0131\u015f katman\u0131ndan olu\u015fur. Her katmandaki &#8220;n\u00f6ronlar&#8221; (i\u015flem birimleri) birbirleriyle ba\u011flant\u0131l\u0131d\u0131r ve bir n\u00f6ronun \u00e7\u0131kt\u0131s\u0131, bir sonraki n\u00f6ronun girdisi haline gelir. Bu karma\u015f\u0131k yap\u0131 sayesinde, a\u011flar milyarlarca veri noktas\u0131n\u0131 i\u015fleyerek, dil \u00e7evirisi, g\u00f6r\u00fcnt\u00fc analizi, ses tan\u0131ma ve hatta t\u0131bbi te\u015fhis gibi alanlarda devrim niteli\u011finde ba\u015far\u0131lar elde edebilmektedir. Bu nedenle, makine \u00f6\u011frenimi ve \u00f6zellikle yapay sinir a\u011flar\u0131, g\u00fcn\u00fcm\u00fcz\u00fcn ve gelece\u011fin teknolojik inovasyonlar\u0131n\u0131n temel direklerinden biri olarak kabul edilmektedir.<\/p>\n<h2>Yapay Sinir A\u011flar\u0131n\u0131n Kalbinde Ne Var: Temel Kavramlara Yolculuk<\/h2>\n<p>Yapay sinir a\u011flar\u0131 (YSA), ad\u0131ndan da anla\u015f\u0131laca\u011f\u0131 gibi, biyolojik beyinlerin \u00e7al\u0131\u015fma prensiplerinden ilham alan algoritmik yap\u0131lard\u0131r. Ancak, biyolojik beynin karma\u015f\u0131kl\u0131\u011f\u0131na k\u0131yasla YSA&#8217;lar olduk\u00e7a basitle\u015ftirilmi\u015f modellerdir. Amac\u0131m\u0131z, beynin t\u00fcm i\u015flevlerini kopyalamak de\u011fil, belirli g\u00f6revleri yerine getirebilen \u00f6\u011frenme yetene\u011fine sahip sistemler in\u015fa etmektir. Bir yapay sinir a\u011f\u0131na yak\u0131ndan bakt\u0131\u011f\u0131m\u0131zda, baz\u0131 temel yap\u0131 ta\u015flar\u0131 ve kavramlarla kar\u015f\u0131la\u015f\u0131r\u0131z. Bu kavramlar, a\u011f\u0131n nas\u0131l \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 ve nas\u0131l \u00f6\u011frendi\u011fini anlamam\u0131z i\u00e7in kilit rol oynar.<\/p>\n<h3>\u0130nsan Beyni: \u0130lham Kayna\u011f\u0131m\u0131z Nas\u0131l Oldu?<\/h3>\n<p>\u0130nsan beyni, milyarlarca n\u00f6ron ve trilyonlarca sinaptik ba\u011flant\u0131dan olu\u015fan inan\u0131lmaz derecede karma\u015f\u0131k bir yap\u0131d\u0131r. Her bir n\u00f6ron, di\u011fer n\u00f6ronlardan elektrik sinyalleri al\u0131r, bu sinyalleri i\u015fler ve belirli bir e\u015fi\u011fi a\u015farsa kendi sinyalini di\u011fer n\u00f6ronlara iletir. Sinapslar ise bu ba\u011flant\u0131lar\u0131n g\u00fcc\u00fcn\u00fc belirler ve \u00f6\u011frenme s\u00fcreci asl\u0131nda bu sinaptik ba\u011flant\u0131lar\u0131n g\u00fc\u00e7lenmesi veya zay\u0131flamas\u0131yla ger\u00e7ekle\u015fir. Yapay sinir a\u011flar\u0131 da tam olarak bu prensipten yola \u00e7\u0131kar: basit i\u015flem birimlerinin (yapay n\u00f6ronlar) birbirleriyle ba\u011flant\u0131l\u0131 oldu\u011fu ve bu ba\u011flant\u0131lar\u0131n a\u011f\u0131rl\u0131klar\u0131 de\u011fi\u015ftirilerek \u00f6\u011frenmenin sa\u011fland\u0131\u011f\u0131 bir sistem.<\/p>\n<p>Bu ilham, yapay zek\u00e2n\u0131n ilk d\u00f6nemlerinden beri ara\u015ft\u0131rmac\u0131lara yol g\u00f6stermi\u015ftir. \u00d6zellikle, beynin paralelle\u015ftirilmi\u015f i\u015fleme yetene\u011fi ve hata tolerans\u0131, yapay sinir a\u011flar\u0131n\u0131n temel tasar\u0131m felsefesine \u0131\u015f\u0131k tutmu\u015ftur. Bu sistemler, tek bir g\u00fc\u00e7l\u00fc i\u015flemci yerine bir\u00e7ok basit i\u015flem birimini e\u015f zamanl\u0131 olarak \u00e7al\u0131\u015ft\u0131rarak karma\u015f\u0131k problemleri \u00e7\u00f6zebilirler. Ayr\u0131ca, birka\u00e7 ba\u011flant\u0131n\u0131n veya n\u00f6ronun ar\u0131zalanmas\u0131 bile sistemin tamamen \u00e7\u00f6kmesine neden olmaz; bu da onlara do\u011fal bir esneklik kazand\u0131r\u0131r.<\/p>\n<h3>N\u00f6ronlar, A\u011f\u0131rl\u0131klar ve Bias: Bir Sinir A\u011f\u0131n\u0131n Yap\u0131 Ta\u015flar\u0131 Nelerdir?<\/h3>\n<p>Bir yapay sinir a\u011f\u0131n\u0131n en temel birimi, t\u0131pk\u0131 biyolojik bir n\u00f6ron gibi, &#8220;yapay n\u00f6ron&#8221; veya bazen sadece &#8220;n\u00f6ron&#8221; olarak adland\u0131r\u0131l\u0131r. Bir n\u00f6ron genellikle birden fazla girdi al\u0131r. Bu girdiler, ya orijinal verilerdir ya da \u00f6nceki katmandaki n\u00f6ronlar\u0131n \u00e7\u0131kt\u0131lar\u0131d\u0131r. Her girdi ile birlikte bir &#8220;a\u011f\u0131rl\u0131k&#8221; (weight) ili\u015fkilendirilir. Bu a\u011f\u0131rl\u0131klar, ilgili girdinin n\u00f6ronun \u00e7\u0131kt\u0131s\u0131 \u00fczerindeki etkisinin g\u00fcc\u00fcn\u00fc temsil eder. Bir a\u011f\u0131rl\u0131k ne kadar b\u00fcy\u00fckse, o girdi n\u00f6ronun \u00e7\u0131kt\u0131s\u0131n\u0131 o kadar fazla etkiler.<\/p>\n<p>N\u00f6ron, gelen her girdiyi kendi a\u011f\u0131rl\u0131\u011f\u0131yla \u00e7arpar ve bu \u00e7arp\u0131mlar\u0131n toplam\u0131n\u0131 al\u0131r. Bu toplama bir de &#8220;\u00f6nyarg\u0131&#8221; (bias) de\u011feri eklenir. \u00d6nyarg\u0131, n\u00f6ronun aktivasyon e\u015fi\u011fini de\u011fi\u015ftiren sabit bir de\u011ferdir; yani, n\u00f6ronun ne zaman &#8220;ate\u015flenece\u011fini&#8221; ayarlamaya yard\u0131mc\u0131 olur. Form\u00fclize edersek, bir n\u00f6ronun net girdisi \u015fu \u015fekilde hesaplanabilir: \\(NetGirdi = (Girdi_1 \\times A\u011f\u0131rl\u0131k_1) + (Girdi_2 \\times A\u011f\u0131rl\u0131k_2) + \\dots + (Girdi_n \\times A\u011f\u0131rl\u0131k_n) + Bias\\). Bu a\u011f\u0131rl\u0131klar ve bias de\u011ferleri, bir yapay sinir a\u011f\u0131n\u0131n &#8220;\u00f6\u011frendi\u011fi&#8221; parametrelerdir. Ba\u015flang\u0131\u00e7ta rastgele atan\u0131rlar ve e\u011fitim s\u00fcreci boyunca optimize edilerek ayarlan\u0131rlar.<\/p>\n<h3>Aktivasyon Fonksiyonlar\u0131 Neden \u00d6nemli?<\/h3>\n<p>Bir n\u00f6ronun net girdiyi hesaplad\u0131ktan sonraki ad\u0131m\u0131, bu toplam\u0131 bir &#8220;aktivasyon fonksiyonundan&#8221; ge\u00e7irmektir. Aktivasyon fonksiyonu, n\u00f6ronun \u00e7\u0131kt\u0131s\u0131n\u0131n ne olaca\u011f\u0131n\u0131 belirleyen kritik bir matematiksel fonksiyondur. Temelde iki \u00f6nemli g\u00f6revi vard\u0131r: birincisi, n\u00f6ronun \u00e7\u0131kt\u0131s\u0131n\u0131 belirli bir aral\u0131kta tutmak (\u00f6rne\u011fin 0 ile 1 aras\u0131nda veya -1 ile 1 aras\u0131nda); ikincisi ve en \u00f6nemlisi, yapay sinir a\u011f\u0131na do\u011frusal olmayan bir yetenek kazand\u0131rmakt\u0131r. E\u011fer aktivasyon fonksiyonlar\u0131 olmasayd\u0131, ne kadar katman eklersek ekleyelim, a\u011f\u0131m\u0131z her zaman sadece do\u011frusal ili\u015fkileri modelleyebilirdi ve bu da onu karma\u015f\u0131k problemleri \u00e7\u00f6zme konusunda yetersiz k\u0131lard\u0131.<\/p>\n<p>Yayg\u0131n olarak kullan\u0131lan aktivasyon fonksiyonlar\u0131 aras\u0131nda \u015funlar bulunur:<\/p>\n<ul>\n<li><strong>Sigmoid:<\/strong> Girdiyi 0 ile 1 aras\u0131na s\u0131k\u0131\u015ft\u0131r\u0131r. \u00d6zellikle ikili s\u0131n\u0131fland\u0131rma problemlerinde \u00e7\u0131k\u0131\u015f katman\u0131nda pop\u00fclerdir. \\(f(x) = \\frac{1}{1 + e^{-x}}\\)<\/li>\n<li><strong>ReLU (Rectified Linear Unit):<\/strong> Girdi s\u0131f\u0131rdan k\u00fc\u00e7\u00fckse s\u0131f\u0131r, s\u0131f\u0131rdan b\u00fcy\u00fckse girdinin kendisi olarak \u00e7\u0131kt\u0131 verir. Hesaplama a\u00e7\u0131s\u0131ndan basit ve \u00e7o\u011fu derin \u00f6\u011frenme g\u00f6revinde olduk\u00e7a etkilidir. \\(f(x) = \\max(0, x)\\)<\/li>\n<li><strong>Tanh (Hiperbolik Tanjant):<\/strong> Girdiyi -1 ile 1 aras\u0131na s\u0131k\u0131\u015ft\u0131r\u0131r. Sigmoid&#8217;e benzer ancak \u00e7\u0131kt\u0131s\u0131 s\u0131f\u0131r merkezlidir. \\(f(x) = \\frac{e^x &#8211; e^{-x}}{e^x + e^{-x}}\\)<\/li>\n<\/ul>\n<p>Her aktivasyon fonksiyonunun kendine \u00f6zg\u00fc avantajlar\u0131 ve dezavantajlar\u0131 vard\u0131r ve se\u00e7im, genellikle problem t\u00fcr\u00fcne ve a\u011f\u0131n mimarisine ba\u011fl\u0131d\u0131r.<\/p>\n<h3>Katmanlar ve A\u011f Mimarisi: Veri Nas\u0131l Ak\u0131yor?<\/h3>\n<p>Yapay sinir a\u011flar\u0131, genellikle katmanlar halinde organize edilmi\u015f n\u00f6ronlardan olu\u015fur. Tipik bir ileri beslemeli (feedforward) sinir a\u011f\u0131 \u00fc\u00e7 ana katman t\u00fcr\u00fcne sahiptir:<\/p>\n<ol>\n<li><strong>Giri\u015f Katman\u0131 (Input Layer):<\/strong> A\u011fa ham veriyi alan katmand\u0131r. Bu katmandaki her bir n\u00f6ron genellikle bir veri \u00f6zelli\u011fine kar\u015f\u0131l\u0131k gelir. \u00d6rne\u011fin, bir g\u00f6r\u00fcnt\u00fcdeki her piksel de\u011feri veya bir ev fiyat tahmini modelindeki oda say\u0131s\u0131, metrekare gibi \u00f6zellikler birer giri\u015f n\u00f6ronuna denk gelebilir.<\/li>\n<li><strong>Gizli Katmanlar (Hidden Layers):<\/strong> Giri\u015f katman\u0131 ile \u00e7\u0131k\u0131\u015f katman\u0131 aras\u0131nda yer alan katmanlard\u0131r. Bir a\u011fda birden fazla gizli katman bulunabilir. Derin \u00f6\u011frenme terimi de asl\u0131nda birden fazla gizli katmana sahip yapay sinir a\u011flar\u0131n\u0131 ifade eder. Bu katmanlardaki n\u00f6ronlar, giri\u015f verilerindeki karma\u015f\u0131k desenleri ve hiyerar\u015fik \u00f6zellikleri \u00f6\u011frenmekten sorumludur. \u00d6rne\u011fin, bir g\u00f6r\u00fcnt\u00fc tan\u0131ma a\u011f\u0131nda ilk gizli katmanlar kenarlar\u0131 ve k\u00f6\u015feleri tan\u0131rken, daha derin katmanlar y\u00fczler veya nesneler gibi daha soyut \u00f6zellikler \u00f6\u011frenebilir.<\/li>\n<li><strong>\u00c7\u0131k\u0131\u015f Katman\u0131 (Output Layer):<\/strong> A\u011f\u0131n son katman\u0131d\u0131r ve a\u011f\u0131n tahminini veya karar\u0131n\u0131 \u00fcretir. \u00c7\u0131k\u0131\u015f katman\u0131ndaki n\u00f6ron say\u0131s\u0131, \u00e7\u00f6z\u00fclen problemin t\u00fcr\u00fcne ba\u011fl\u0131d\u0131r. \u00d6rne\u011fin, ikili s\u0131n\u0131fland\u0131rma (evet\/hay\u0131r) i\u00e7in tek bir n\u00f6ron, \u00e7oklu s\u0131n\u0131fland\u0131rma (kedi, k\u00f6pek, ku\u015f) i\u00e7in her s\u0131n\u0131f i\u00e7in bir n\u00f6ron veya regresyon (say\u0131sal de\u011fer tahmini) i\u00e7in tek bir n\u00f6ron kullan\u0131labilir.<\/li>\n<\/ol>\n<p>Bu katmanl\u0131 yap\u0131, a\u011f\u0131n verilerdeki giderek daha karma\u015f\u0131k ve soyut temsilleri \u00f6\u011frenmesini sa\u011flar. Bilgi, giri\u015f katman\u0131ndan ba\u015flar, gizli katmanlar boyunca ilerler ve sonunda \u00e7\u0131k\u0131\u015f katman\u0131nda bir tahmine d\u00f6n\u00fc\u015f\u00fcr. Bu ak\u0131\u015fa &#8220;ileri yay\u0131l\u0131m&#8221; (forward propagation) denir ve a\u011f\u0131n bir girdi verildi\u011finde bir \u00e7\u0131kt\u0131 \u00fcretme \u015feklidir.<\/p>\n<h2>Bir Yapay Sinir A\u011f\u0131 Nas\u0131l \u00d6\u011frenir: Ad\u0131m Ad\u0131m E\u011fitim S\u00fcreci<\/h2>\n<p>Yapay sinir a\u011flar\u0131n\u0131n en b\u00fcy\u00fcleyici y\u00f6nlerinden biri, t\u0131pk\u0131 insanlar gibi hatalar\u0131ndan ders \u00e7\u0131kararak kendilerini geli\u015ftirebilmeleridir. Bu \u00f6\u011frenme s\u00fcreci, genellikle denetimli \u00f6\u011frenme paradigmas\u0131 alt\u0131nda ger\u00e7ekle\u015fir ve \u00fc\u00e7 ana a\u015famadan olu\u015fur: ileri yay\u0131l\u0131m, kay\u0131p fonksiyonu ile hata \u00f6l\u00e7\u00fcm\u00fc ve geri yay\u0131l\u0131m ile a\u011f\u0131rl\u0131klar\u0131n g\u00fcncellenmesi. Bu d\u00f6ng\u00fc, a\u011f\u0131n performans\u0131n\u0131 art\u0131rmak i\u00e7in milyonlarca kez tekrarlan\u0131r.<\/p>\n<h3>\u0130leri Yay\u0131l\u0131m (Forward Propagation): Tahmin Nas\u0131l Yap\u0131l\u0131r?<\/h3>\n<p>\u00d6\u011frenme s\u00fcrecinin ilk ad\u0131m\u0131, a\u011f\u0131n bir girdi \u00f6rne\u011fi \u00fczerinde bir tahmin yapmas\u0131d\u0131r. Buna &#8220;ileri yay\u0131l\u0131m&#8221; denir. Bu a\u015famada, giri\u015f katman\u0131ndaki veriler, a\u011f\u0131rl\u0131klar ve bias&#8217;lar kullan\u0131larak bir sonraki katmana iletilir. Her n\u00f6ron, \u00f6nceki katmandan gelen girdileri kendi a\u011f\u0131rl\u0131klar\u0131yla \u00e7arpar, bu \u00e7arp\u0131mlar\u0131 toplar, bias de\u011ferini ekler ve sonucu aktivasyon fonksiyonundan ge\u00e7irerek bir \u00e7\u0131kt\u0131 \u00fcretir. Bu \u00e7\u0131kt\u0131, bir sonraki katman\u0131n girdisi olur ve bu s\u00fcre\u00e7, giri\u015f katman\u0131ndan \u00e7\u0131k\u0131\u015f katman\u0131na kadar devam eder. Sonunda, \u00e7\u0131k\u0131\u015f katman\u0131, a\u011f\u0131n verilen giri\u015f i\u00e7in yapt\u0131\u011f\u0131 tahmini \u00fcretir.<\/p>\n<p>\u00d6rne\u011fin, bir g\u00f6r\u00fcnt\u00fcy\u00fc s\u0131n\u0131fland\u0131rmaya \u00e7al\u0131\u015fan bir a\u011f d\u00fc\u015f\u00fcnelim. G\u00f6r\u00fcnt\u00fcn\u00fcn piksel de\u011ferleri giri\u015f katman\u0131na beslenir. Bu de\u011ferler, ilk gizli katmandaki n\u00f6ronlara ula\u015f\u0131r, burada a\u011f\u0131rl\u0131klar ve bias&#8217;lar ile i\u015flenir ve aktivasyon fonksiyonundan ge\u00e7irilir. Ortaya \u00e7\u0131kan aktivasyonlar, ikinci gizli katmana girdi olarak verilir ve bu s\u00fcre\u00e7, a\u011f\u0131n sonunda g\u00f6r\u00fcnt\u00fcn\u00fcn &#8220;kedi&#8221; veya &#8220;k\u00f6pek&#8221; olma olas\u0131l\u0131klar\u0131 gibi bir tahmin \u00fcreten \u00e7\u0131k\u0131\u015f katman\u0131na ula\u015fana kadar devam eder. \u0130leri yay\u0131l\u0131m, a\u011f\u0131n bir nevi &#8220;ileriye do\u011fru d\u00fc\u015f\u00fcnme&#8221; veya &#8220;tahmin etme&#8221; yetene\u011fini temsil eder.<\/p>\n<h3>Kay\u0131p Fonksiyonu: Hata Pay\u0131 Nas\u0131l \u00d6l\u00e7\u00fcl\u00fcr?<\/h3>\n<p>A\u011f bir tahmin \u00fcrettikten sonra, bu tahminin ne kadar do\u011fru veya yanl\u0131\u015f oldu\u011funu bilmemiz gerekir. \u0130\u015fte burada &#8220;kay\u0131p fonksiyonu&#8221; (loss function) devreye girer. Kay\u0131p fonksiyonu, a\u011f\u0131n tahmini ile ger\u00e7ek (do\u011fru) de\u011fer aras\u0131ndaki fark\u0131 nicel olarak \u00f6l\u00e7en matematiksel bir fonksiyondur. Ama\u00e7, bu kay\u0131p de\u011ferini m\u00fcmk\u00fcn oldu\u011funca minimize etmektir; yani, a\u011f\u0131n tahminlerinin ger\u00e7ek de\u011ferlere olabildi\u011fince yak\u0131n olmas\u0131n\u0131 sa\u011flamakt\u0131r.<\/p>\n<p>Farkl\u0131 problem t\u00fcrleri i\u00e7in farkl\u0131 kay\u0131p fonksiyonlar\u0131 kullan\u0131l\u0131r:<\/p>\n<ul>\n<li><strong>Ortalama Kare Hata (Mean Squared Error &#8211; MSE):<\/strong> Regresyon problemleri i\u00e7in yayg\u0131nd\u0131r. Tahmin edilen de\u011fer ile ger\u00e7ek de\u011fer aras\u0131ndaki fark\u0131n karesini al\u0131r ve t\u00fcm \u00f6rnekler i\u00e7in ortalamas\u0131n\u0131 hesaplar. B\u00fcy\u00fck hatalar\u0131 daha fazla cezaland\u0131r\u0131r.<\/li>\n<li><strong>\u00c7apraz Entropi (Cross-Entropy):<\/strong> S\u0131n\u0131fland\u0131rma problemleri i\u00e7in kullan\u0131l\u0131r. \u00d6zellikle ikili veya \u00e7ok s\u0131n\u0131fl\u0131 s\u0131n\u0131fland\u0131rmada a\u011f\u0131n bir s\u0131n\u0131f\u0131 do\u011fru tahmin etme olas\u0131l\u0131\u011f\u0131n\u0131 ne kadar y\u00fcksek tuttu\u011funu \u00f6l\u00e7er. A\u011f, do\u011fru s\u0131n\u0131f i\u00e7in y\u00fcksek olas\u0131l\u0131k, yanl\u0131\u015f s\u0131n\u0131flar i\u00e7in d\u00fc\u015f\u00fck olas\u0131l\u0131k tahmin etti\u011finde kay\u0131p d\u00fc\u015fer.<\/li>\n<\/ul>\n<p>Kay\u0131p fonksiyonunun \u00e7\u0131kt\u0131s\u0131, a\u011f\u0131n mevcut performans\u0131n\u0131n bir g\u00f6stergesidir. Y\u00fcksek bir kay\u0131p de\u011feri, a\u011f\u0131n k\u00f6t\u00fc performans g\u00f6sterdi\u011fini, d\u00fc\u015f\u00fck bir kay\u0131p de\u011feri ise iyi performans g\u00f6sterdi\u011fini ifade eder. E\u011fitim s\u00fcrecinin nihai hedefi, bu kay\u0131p de\u011ferini m\u00fcmk\u00fcn oldu\u011funca azaltmakt\u0131r.<\/p>\n<h3>Geri Yay\u0131l\u0131m (Backpropagation) ve Optimizasyon: A\u011f\u0131rl\u0131klar Nas\u0131l Ayarlan\u0131r?<\/h3>\n<p>Kay\u0131p fonksiyonu bize a\u011f\u0131n ne kadar k\u00f6t\u00fc performans g\u00f6sterdi\u011fini s\u00f6yledikten sonra, bu hatay\u0131 d\u00fczeltmek i\u00e7in ne yapmam\u0131z gerekti\u011fini bulmal\u0131y\u0131z. \u0130\u015fte bu noktada yapay sinir a\u011flar\u0131n\u0131n &#8220;\u00f6\u011frenme&#8221; sihirbaz\u0131 olan &#8220;geri yay\u0131l\u0131m&#8221; (backpropagation) algoritmas\u0131 devreye girer. Geri yay\u0131l\u0131m, \u00e7\u0131k\u0131\u015f katman\u0131ndaki hatay\u0131 al\u0131p, bu hatan\u0131n a\u011fdaki her bir a\u011f\u0131rl\u0131k ve bias&#8217;a ne kadar katk\u0131da bulundu\u011funu hesaplayarak geriye do\u011fru yayar.<\/p>\n<p>Bu hesaplama, &#8220;gradyan&#8221; (gradient) ad\u0131 verilen bir de\u011fer \u00fcretir. Gradyan, kay\u0131p fonksiyonunun belirli bir a\u011f\u0131rl\u0131\u011fa g\u00f6re nas\u0131l de\u011fi\u015fti\u011fini, yani o a\u011f\u0131rl\u0131\u011f\u0131 hangi y\u00f6ne (art\u0131rmal\u0131 m\u0131, azaltmal\u0131 m\u0131) ve ne kadar de\u011fi\u015ftirmemiz gerekti\u011fini g\u00f6sterir. Bu gradyanlar, &#8220;optimizasyon algoritmas\u0131&#8221; taraf\u0131ndan kullan\u0131l\u0131r. En yayg\u0131n optimizasyon algoritmas\u0131 &#8220;Gradyan Azaltma&#8221;d\u0131r (Gradient Descent). Gradyan Azaltma, her bir a\u011f\u0131rl\u0131\u011f\u0131 ve bias&#8217;\u0131, gradyan\u0131n i\u015faretinin tersi y\u00f6nde, k\u00fc\u00e7\u00fck bir ad\u0131mla (\u00f6\u011frenme oran\u0131 ad\u0131 verilen bir parametre ile \u00e7arp\u0131larak) g\u00fcnceller. Bu sayede, her iterasyonda kay\u0131p de\u011feri azal\u0131r.<\/p>\n<p>Bu ileri yay\u0131l\u0131m, kay\u0131p hesaplama ve geri yay\u0131l\u0131m\/optimizasyon d\u00f6ng\u00fcs\u00fc, bir &#8220;epoch&#8221; (d\u00f6nem) olarak adland\u0131r\u0131l\u0131r. Bir e\u011fitim seti \u00fczerindeki t\u00fcm veri \u00f6rnekleri bir kez i\u015flendi\u011finde bir epoch tamamlanm\u0131\u015f olur. Genellikle bir yapay sinir a\u011f\u0131, binlerce hatta milyonlarca epoch boyunca e\u011fitilir. Her epoch&#8217;ta a\u011f\u0131rl\u0131klar ve bias&#8217;lar hafif\u00e7e ayarlan\u0131r, b\u00f6ylece a\u011f zamanla daha do\u011fru tahminler yapmay\u0131 \u00f6\u011frenir ve kay\u0131p de\u011feri giderek azal\u0131r. Bu s\u00fcrekli ayarlama ve iyile\u015ftirme s\u00fcreci, makine \u00f6\u011freniminin \u00f6z\u00fcd\u00fcr.<\/p>\n<h3>Bir Basit Sinir A\u011f\u0131 \u00d6rne\u011fi: Kavramsal Kod Anlat\u0131m\u0131<\/h3>\n<p>\u015eimdi gelin, basit bir yapay sinir a\u011f\u0131n\u0131n e\u011fitim s\u00fcrecini kavramsal d\u00fczeyde Python benzeri bir s\u00f6zde kod ile inceleyelim. Bu \u00f6rnek, bir n\u00f6ronun temel i\u015fleyi\u015fini ve a\u011f\u0131rl\u0131klar\u0131n nas\u0131l g\u00fcncellendi\u011fini g\u00f6stermektedir.<\/p>\n<pre><code>\n# Basit bir yapay n\u00f6ron i\u00e7in kavramsal kod \u00f6rne\u011fi\n# (\u00d6\u011frenme S\u00fcreci)\n\nimport numpy as np\n\n# Sigmoid aktivasyon fonksiyonu\ndef sigmoid(x):\n    return 1 \/ (1 + np.exp(-x))\n\n# Sigmoid fonksiyonunun t\u00fcrevi (geri yay\u0131l\u0131m i\u00e7in gerekli)\ndef sigmoid_derivative(x):\n    return x * (1 - x)\n\n# E\u011fitim verisi (\u00f6rne\u011fin, 3 giri\u015f, 1 \u00e7\u0131k\u0131\u015f)\n# Her bir sat\u0131r bir e\u011fitim \u00f6rne\u011fini temsil eder.\n# Giri\u015fler: [hava durumu, s\u0131cakl\u0131k, nem]\n# \u00c7\u0131k\u0131\u015f: [\u015femsiye al\u0131nmal\u0131 m\u0131?] (0: hay\u0131r, 1: evet)\ntraining_inputs = np.array([\n    [0,0,1],\n    [1,1,1],\n    [1,0,1],\n    [0,1,1],\n    [0,0,0]\n])\n\ntraining_outputs = np.array([[0,1,1,0,0]]).T # \u00c7\u0131k\u0131\u015flar\u0131 transpoze et\n\n# Rastgele a\u011f\u0131rl\u0131klarla ba\u015fla (3 giri\u015f i\u00e7in 3 a\u011f\u0131rl\u0131k)\nnp.random.seed(1)\nsynaptic_weights = 2 * np.random.random((3, 1)) - 1 # -1 ile 1 aras\u0131nda rastgele say\u0131lar\n\nprint(\"Ba\u015flang\u0131\u00e7 A\u011f\u0131rl\u0131klar\u0131:\\n\", synaptic_weights)\n\n# E\u011fitim d\u00f6ng\u00fcs\u00fc\nfor iteration in range(10000):\n    # \u0130leri Yay\u0131l\u0131m\n    input_layer = training_inputs\n    # Net girdi = (Giri\u015fler * A\u011f\u0131rl\u0131klar)\n    outputs = sigmoid(np.dot(input_layer, synaptic_weights))\n\n    # Kay\u0131p Fonksiyonu (Hata hesaplama)\n    error = training_outputs - outputs\n\n    # Geri Yay\u0131l\u0131m (Hatan\u0131n a\u011f\u0131rl\u0131klara g\u00f6re gradyan\u0131)\n    # delta = hata * sigmoid_t\u00fcrevi(\u00e7\u0131kt\u0131lar)\n    adjustments = error * sigmoid_derivative(outputs)\n\n    # A\u011f\u0131rl\u0131klar\u0131 g\u00fcncelle\n    synaptic_weights += np.dot(input_layer.T, adjustments)\n\nprint(\"\\nE\u011fitim Sonras\u0131 A\u011f\u0131rl\u0131klar:\\n\", synaptic_weights)\nprint(\"\\nE\u011fitim Sonras\u0131 \u00c7\u0131k\u0131\u015flar (Tahminler):\\n\", outputs)\n\n# Yeni bir girdiyle test et\nnew_input = np.array([1, 0, 0]) # Hava durumu iyi, s\u0131cakl\u0131k d\u00fc\u015f\u00fck, nem d\u00fc\u015f\u00fck\nprediction = sigmoid(np.dot(new_input, synaptic_weights))\nprint(\"\\nYeni girdi [1, 0, 0] i\u00e7in tahmin:\", prediction)\n<\/pre>\n<p><\/code><\/p>\n<p>Bu kodda, <code>synaptic_weights<\/code>, yapay n\u00f6ronumuzun \u00f6\u011frenmesi gereken parametrelerdir. Her iterasyonda (epoch), ileri yay\u0131l\u0131m ile bir tahmin yap\u0131l\u0131r, <code>error<\/code> hesaplan\u0131r, bu hata <code>sigmoid_derivative<\/code> kullan\u0131larak geri yay\u0131l\u0131r ve son olarak <code>synaptic_weights<\/code> g\u00fcncellenir. Bu d\u00f6ng\u00fc, a\u011f\u0131n hatalar\u0131ndan ders \u00e7\u0131kararak daha do\u011fru tahminler yapmas\u0131n\u0131 sa\u011flar. G\u00f6rd\u00fc\u011f\u00fcn\u00fcz gibi, temel prensipler olduk\u00e7a anla\u015f\u0131l\u0131r; karma\u015f\u0131kl\u0131k genellikle a\u011f\u0131n boyutu ve katman say\u0131s\u0131 artt\u0131k\u00e7a ortaya \u00e7\u0131kar.<\/p>\n<h2>Yapay Sinir A\u011flar\u0131 Nerelerde Kullan\u0131l\u0131yor: Ger\u00e7ek D\u00fcnya Senaryolar\u0131 ve Vaka Analizleri<\/h2>\n<p>Yapay sinir a\u011flar\u0131, g\u00fcn\u00fcm\u00fcz\u00fcn en g\u00fc\u00e7l\u00fc yapay zek\u00e2 teknolojilerinden biridir ve \u00e7ok \u00e7e\u015fitli sekt\u00f6rlerde devrim niteli\u011finde uygulamalara sahiptir. Gelin, bu uygulamalardan baz\u0131lar\u0131n\u0131 ve arkalar\u0131ndaki mant\u0131\u011f\u0131 yak\u0131ndan inceleyelim.<\/p>\n<h3>G\u00f6r\u00fcnt\u00fc Tan\u0131ma: Ak\u0131ll\u0131 Telefonlar\u0131m\u0131z Bizi Nas\u0131l Anl\u0131yor?<\/h3>\n<p>Belki de yapay sinir a\u011flar\u0131n\u0131n en bilinen ve etkileyici uygulamalar\u0131ndan biri g\u00f6r\u00fcnt\u00fc tan\u0131mad\u0131r. Ak\u0131ll\u0131 telefonunuzdaki y\u00fcz tan\u0131ma kilidi, bir foto\u011fraf uygulamas\u0131n\u0131n resimdeki nesneleri etiketlemesi veya bir g\u00fcvenlik kameras\u0131n\u0131n \u015f\u00fcpheli hareketleri alg\u0131lamas\u0131 gibi pek \u00e7ok i\u015flev, evri\u015fimli sinir a\u011flar\u0131 (Convolutional Neural Networks - CNN) ad\u0131 verilen \u00f6zel bir YSA t\u00fcr\u00fc sayesinde m\u00fcmk\u00fcn olur.<\/p>\n<p><strong>Vaka Analizi: Y\u00fcz Tan\u0131ma Sistemleri<\/strong><\/p>\n<p>Ak\u0131ll\u0131 telefonlar, g\u00fcvenlik sistemleri ve hatta havalimanlar\u0131ndaki pasaport kontrol noktalar\u0131nda kullan\u0131lan y\u00fcz tan\u0131ma sistemleri, ki\u015fileri kimliklendirmek i\u00e7in CNN'leri kullan\u0131r. Bu sistemler milyonlarca y\u00fcz g\u00f6r\u00fcnt\u00fcs\u00fc \u00fczerinde e\u011fitilir. CNN'ler, g\u00f6r\u00fcnt\u00fclerdeki kenarlar\u0131, \u015fekilleri, renkleri ve di\u011fer g\u00f6rsel desenleri katman katman \u00f6\u011frenir. \u0130lk katmanlar basit \u00f6zellikleri (\u00f6rne\u011fin yatay veya dikey \u00e7izgiler) \u00f6\u011frenirken, daha derin katmanlar burun, g\u00f6z gibi karma\u015f\u0131k y\u00fcz \u00f6zelliklerini ve hatta y\u00fcz ifadelerini tan\u0131may\u0131 \u00f6\u011frenir. Sonu\u00e7 olarak, sisteme yeni bir y\u00fcz g\u00f6r\u00fcnt\u00fcs\u00fc verildi\u011finde, \u00f6\u011frenilen bu \u00f6zellikler sayesinde ki\u015fiyi y\u00fcksek do\u011frulukla tan\u0131yabilir veya bir veri taban\u0131ndaki ki\u015filerle e\u015fle\u015ftirebilir.<\/p>\n<p>CNN'lerin bu alandaki ba\u015far\u0131s\u0131, \u00f6zellikle veri setlerindeki g\u00f6r\u00fcnt\u00fclerin farkl\u0131 a\u00e7\u0131lardan, farkl\u0131 \u0131\u015f\u0131k ko\u015fullar\u0131nda ve farkl\u0131 ifadelerle \u00e7ekilmi\u015f olmas\u0131na ra\u011fmen, ayn\u0131 ki\u015fiyi tan\u0131yabilme yetene\u011finden kaynaklan\u0131r. Bu, manuel kurallar tan\u0131mlayarak elde edilmesi neredeyse imkans\u0131z bir esneklik ve do\u011fruluk sa\u011flar.<\/p>\n<h3>Do\u011fal Dil \u0130\u015fleme: \u00c7eviri ve Chatbotlar Nas\u0131l \u00c7al\u0131\u015f\u0131yor?<\/h3>\n<p>Yapay sinir a\u011flar\u0131, insan dilini anlamak, \u00fcretmek ve \u00e7evirmek gibi do\u011fal dil i\u015fleme (Natural Language Processing - NLP) g\u00f6revlerinde de \u00e7\u0131\u011f\u0131r a\u00e7m\u0131\u015ft\u0131r. Sanal asistanlar (Siri, Google Assistant), makine \u00e7evirisi (Google Translate) ve chatbot'lar bu teknolojinin en belirgin \u00f6rnekleridir. \u00d6zellikle tekrarlayan sinir a\u011flar\u0131 (Recurrent Neural Networks - RNN) ve daha geli\u015fmi\u015f versiyonlar\u0131 olan Uzun K\u0131sa S\u00fcreli Bellek (Long Short-Term Memory - LSTM) a\u011flar\u0131, s\u0131ral\u0131 veriler (yani metinler) \u00fczerinde \u00e7al\u0131\u015fmada \u00e7ok ba\u015far\u0131l\u0131d\u0131r.<\/p>\n<p><strong>Vaka Analizi: Makine \u00c7evirisi<\/strong><\/p>\n<p>Google Translate gibi modern makine \u00e7evirisi sistemleri, genellikle \"sekans-to-sekans\" (sequence-to-sequence) ad\u0131 verilen bir mimariyi kullan\u0131r. Bu mimari, iki RNN'den olu\u015fur: bir \"kodlay\u0131c\u0131\" (encoder) ve bir \"\u00e7\u00f6z\u00fcc\u00fc\" (decoder). Kodlay\u0131c\u0131, kaynak dildeki c\u00fcmleyi (\u00f6rne\u011fin T\u00fcrk\u00e7e) al\u0131r ve onun anlam\u0131n\u0131 \u00f6zetleyen bir \"ba\u011flam vekt\u00f6r\u00fc\" (context vector) olu\u015fturur. \u00c7\u00f6z\u00fcc\u00fc ise bu ba\u011flam vekt\u00f6r\u00fcn\u00fc al\u0131r ve hedef dildeki (\u00f6rne\u011fin \u0130ngilizce) c\u00fcmleyi kelime kelime \u00fcretir. Bu s\u00fcre\u00e7te, kelimeler say\u0131sal vekt\u00f6rlere d\u00f6n\u00fc\u015ft\u00fcr\u00fcl\u00fcr (word embeddings), bu da a\u011f\u0131n kelimelerin anlamsal ili\u015fkilerini kavramas\u0131n\u0131 sa\u011flar.<\/p>\n<p>Bu sistemler, milyonlarca paralel metin (ayn\u0131 metnin farkl\u0131 dillerdeki kar\u015f\u0131l\u0131klar\u0131) \u00fczerinde e\u011fitilerek, sadece kelime kelime \u00e7eviri yapmakla kalmaz, ayn\u0131 zamanda dilin gramer yap\u0131s\u0131n\u0131 ve anlamsal inceliklerini de \u00f6\u011frenirler. Bu sayede, \u00e7ok daha ak\u0131c\u0131 ve do\u011fru \u00e7eviriler \u00fcretebilirler.<\/p>\n<h3>Otonom Ara\u00e7lar: G\u00fcvenli S\u00fcr\u00fc\u015f\u00fcn Arkas\u0131ndaki Teknoloji Ne?<\/h3>\n<p>S\u00fcr\u00fcc\u00fcs\u00fcz otomobiller, yapay zek\u00e2n\u0131n ve yapay sinir a\u011flar\u0131n\u0131n en zorlu ve en umut vadeden uygulamalar\u0131ndan biridir. Bu ara\u00e7lar, \u00e7evreyi alg\u0131lamak, engelleri tan\u0131mak, trafik i\u015faretlerini okumak ve g\u00fcvenli bir \u015fekilde hareket etmek i\u00e7in birden fazla sinir a\u011f\u0131 kullan\u0131r.<\/p>\n<p><strong>Vaka Analizi: Tesla'n\u0131n Otopilot Sistemi<\/strong><\/p>\n<p>Tesla gibi otonom ara\u00e7 \u00fcreticileri, ara\u00e7lar\u0131ndaki kameralar, radar ve ultrasonik sens\u00f6rlerden gelen verileri i\u015flemek i\u00e7in derin evri\u015fimli sinir a\u011flar\u0131n\u0131 (CNN'ler) ve di\u011fer YSA t\u00fcrlerini yo\u011fun bir \u015fekilde kullan\u0131r. Bu a\u011flar \u015funlar\u0131 yapabilir:<\/p>\n<ul>\n<li><strong>Nesne Tespiti:<\/strong> Yoldaki di\u011fer ara\u00e7lar\u0131, yayalar\u0131, bisikletlileri ve engelleri tan\u0131r.<\/li>\n<li><strong>\u015eerit Tespiti:<\/strong> Arac\u0131n \u015feridini ve yol s\u0131n\u0131rlar\u0131n\u0131 belirler.<\/li>\n<li><strong>Trafik \u0130\u015fareti Tan\u0131ma:<\/strong> Dur i\u015faretleri, h\u0131z s\u0131n\u0131rlar\u0131 ve di\u011fer d\u00fczenleyici i\u015faretleri okur.<\/li>\n<li><strong>Derinlik Tahmini:<\/strong> \u00c7evredeki nesnelerin mesafesini hesaplar.<\/li>\n<\/ul>\n<p>Bu a\u011flar, s\u00fcrekli olarak ger\u00e7ek d\u00fcnya s\u00fcr\u00fc\u015f verileri \u00fczerinde e\u011fitilir ve g\u00fcncellemelerle performanslar\u0131 s\u00fcrekli iyile\u015ftirilir. Milyonlarca kilometre s\u00fcr\u00fc\u015f verisi, a\u011flar\u0131n beklenmedik durumlarla ba\u015fa \u00e7\u0131kmay\u0131 \u00f6\u011frenmesini ve en kritik senaryolarda bile g\u00fcvenli kararlar almas\u0131n\u0131 sa\u011flar. Bu sayede, otonom ara\u00e7lar karma\u015f\u0131k trafik ortamlar\u0131nda bile insan \u015fof\u00f6rlerden daha tutarl\u0131 ve g\u00fcvenli bir \u015fekilde hareket edebilirler.<\/p>\n<h3>Sa\u011fl\u0131k ve Finans: Di\u011fer Uygulama Alanlar\u0131 Nelerdir?<\/h3>\n<p>Yapay sinir a\u011flar\u0131, yukar\u0131da bahsedilen alanlarla s\u0131n\u0131rl\u0131 kalmay\u0131p, sa\u011fl\u0131k ve finans gibi hassas sekt\u00f6rlerde de \u00f6nemli rol oynamaktad\u0131r:<\/p>\n<ul>\n<li><strong>Sa\u011fl\u0131k:<\/strong> T\u0131bbi g\u00f6r\u00fcnt\u00fc analizinde (r\u00f6ntgen, MR, tomografi), kanser h\u00fccrelerinin tespitinde, hastal\u0131k te\u015fhisinde, ila\u00e7 ke\u015ffinde ve ki\u015fiselle\u015ftirilmi\u015f tedavi planlar\u0131n\u0131n olu\u015fturulmas\u0131nda kullan\u0131l\u0131r. \u00d6zellikle erken te\u015fhis ve hassas t\u0131p alan\u0131nda b\u00fcy\u00fck potansiyel sunar.<\/li>\n<li><strong>Finans:<\/strong> Doland\u0131r\u0131c\u0131l\u0131k tespiti, hisse senedi piyasas\u0131 tahminleri, kredi riski de\u011ferlendirmesi ve otomatik ticaret stratejilerinin geli\u015ftirilmesinde yayg\u0131n olarak kullan\u0131l\u0131r. YSA'lar, b\u00fcy\u00fck finansal veri setlerindeki karma\u015f\u0131k ve gizli desenleri alg\u0131layarak daha bilin\u00e7li kararlar al\u0131nmas\u0131na yard\u0131mc\u0131 olur.<\/li>\n<\/ul>\n<p>G\u00f6r\u00fcld\u00fc\u011f\u00fc \u00fczere, yapay sinir a\u011flar\u0131, modern d\u00fcnyam\u0131z\u0131n bir\u00e7ok teknolojik geli\u015fiminin temelinde yer almaktad\u0131r ve gelecekte de hayat\u0131m\u0131z\u0131 derinden etkilemeye devam edeceklerdir.<\/p>\n<h2>\u0130leri D\u00fczey Konulara Bak\u0131\u015f: Performans\u0131 Art\u0131rman\u0131n Yollar\u0131<\/h2>\n<p>Temel kavramlar\u0131 ve uygulama alanlar\u0131n\u0131 anlad\u0131\u011f\u0131m\u0131za g\u00f6re, yapay sinir a\u011flar\u0131n\u0131n performans\u0131n\u0131 daha da ileriye ta\u015f\u0131mak i\u00e7in kullan\u0131lan baz\u0131 ileri d\u00fczey tekniklere g\u00f6z atabiliriz. Bu konular, \u00f6zellikle daha karma\u015f\u0131k problemlerle u\u011fra\u015f\u0131rken veya a\u011flar\u0131n\u0131z\u0131n daha verimli \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flamak istedi\u011finizde hayati \u00f6nem ta\u015f\u0131r.<\/p>\n<h3>Farkl\u0131 A\u011f Mimarileri: CNN, RNN ve Daha Fazlas\u0131<\/h3>\n<p>Daha \u00f6nce de k\u0131saca bahsetti\u011fimiz gibi, yapay sinir a\u011flar\u0131 sadece basit ileri beslemeli a\u011flardan ibaret de\u011fildir. Problem t\u00fcr\u00fcne g\u00f6re \u00f6zelle\u015fmi\u015f bir\u00e7ok farkl\u0131 mimari bulunmaktad\u0131r:<\/p>\n<ul>\n<li><strong>Evri\u015fimli Sinir A\u011flar\u0131 (Convolutional Neural Networks - CNN):<\/strong> G\u00f6r\u00fcnt\u00fc ve video i\u015fleme i\u00e7in m\u00fckemmeldir. G\u00f6r\u00fcnt\u00fclerdeki mekansal hiyerar\u015fiyi yakalamak i\u00e7in evri\u015fim (convolution) katmanlar\u0131n\u0131 kullan\u0131r. Bu katmanlar, g\u00f6r\u00fcnt\u00fclerdeki kenarlar, dokular ve \u015fekiller gibi yerel \u00f6zellikleri \u00f6\u011frenir ve bu \u00f6zellikleri birle\u015ftirerek daha karma\u015f\u0131k g\u00f6rsel desenleri alg\u0131lar.<\/li>\n<li><strong>Tekrarlayan Sinir A\u011flar\u0131 (Recurrent Neural Networks - RNN):<\/strong> S\u0131ral\u0131 veriler (metin, ses, zaman serileri) i\u00e7in tasarlanm\u0131\u015ft\u0131r. \u00d6nceki ad\u0131mlardan gelen bilgileri \"belle\u011finde\" tutarak, bir sonraki ad\u0131m\u0131 tahmin etme yetene\u011fine sahiptir. Ancak, uzun s\u00fcreli ba\u011f\u0131ml\u0131l\u0131klar\u0131 \u00f6\u011frenmede baz\u0131 zorluklar\u0131 vard\u0131r.<\/li>\n<li><strong>Uzun K\u0131sa S\u00fcreli Bellek A\u011flar\u0131 (Long Short-Term Memory - LSTM):<\/strong> RNN'lerin bir t\u00fcr\u00fcd\u00fcr ve uzun vadeli ba\u011f\u0131ml\u0131l\u0131k sorununu \u00e7\u00f6zmek i\u00e7in \u00f6zel \"ge\u00e7it\" (gate) mekanizmalar\u0131na sahiptir. Makine \u00e7evirisi ve ses tan\u0131ma gibi alanlarda yayg\u0131n olarak kullan\u0131l\u0131r.<\/li>\n<li><strong>Transformat\u00f6rler (Transformers):<\/strong> \u00d6zellikle NLP alan\u0131nda devrim yaratm\u0131\u015ft\u0131r. Paralel i\u015flemeye daha uygun olan \"dikkat mekanizmas\u0131\" (attention mechanism) kullanarak, s\u0131ral\u0131 verilerdeki uzun mesafeli ba\u011f\u0131ml\u0131l\u0131klar\u0131 \u00e7ok daha verimli bir \u015fekilde \u00f6\u011frenebilirler. BERT ve GPT gibi b\u00fcy\u00fck dil modellerinin temelini olu\u015ftururlar.<\/li>\n<\/ul>\n<p>Do\u011fru mimariyi se\u00e7mek, projenizin ba\u015far\u0131s\u0131 i\u00e7in kritik \u00f6neme sahiptir. Veri t\u00fcr\u00fcn\u00fcze ve \u00e7\u00f6zmeye \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131z probleme g\u00f6re en uygun mimariyi ara\u015ft\u0131rmal\u0131 ve denemelisiniz.<\/p>\n<h3>A\u015f\u0131r\u0131 \u00d6\u011frenme (Overfitting) ve Az \u00d6\u011frenme (Underfitting) Nas\u0131l Engellenir?<\/h3>\n<p>Yapay sinir a\u011flar\u0131n\u0131 e\u011fitirken kar\u015f\u0131la\u015f\u0131lan en yayg\u0131n sorunlardan ikisi a\u015f\u0131r\u0131 \u00f6\u011frenme (overfitting) ve az \u00f6\u011frenme (underfitting)dir. Bu durumlar\u0131 anlamak ve \u00e7\u00f6zmek, ba\u015far\u0131l\u0131 modeller geli\u015ftirmek i\u00e7in elzemdir:<\/p>\n<ul>\n<li><strong>Az \u00d6\u011frenme (Underfitting):<\/strong> A\u011f\u0131n e\u011fitim verilerini bile yeterince iyi \u00f6\u011frenemedi\u011fi durumu ifade eder. Model \u00e7ok basitse, yeterli say\u0131da gizli katmana veya n\u00f6rona sahip de\u011filse veya yeterli e\u011fitim s\u00fcresi verilmediyse az \u00f6\u011frenme meydana gelebilir. \u00c7\u00f6z\u00fcmler: Daha karma\u015f\u0131k bir model kullanmak, daha fazla \u00f6zellik eklemek, daha uzun s\u00fcre e\u011fitmek.<\/li>\n<li><strong>A\u015f\u0131r\u0131 \u00d6\u011frenme (Overfitting):<\/strong> A\u011f\u0131n e\u011fitim verilerini ezberledi\u011fi, ancak yeni, daha \u00f6nce g\u00f6rmedi\u011fi verilere genelleme yapamad\u0131\u011f\u0131 durumu ifade eder. Model e\u011fitim verisine o kadar uyum sa\u011flar ki, g\u00fcr\u00fclt\u00fcy\u00fc ve veri setindeki rastgele dalgalanmalar\u0131 da \u00f6\u011frenir. \u00c7\u00f6z\u00fcmler: Daha fazla e\u011fitim verisi sa\u011flamak, modeli basitle\u015ftirmek, d\u00fczenlile\u015ftirme (regularization) teknikleri kullanmak, erken durdurma (early stopping) uygulamak.<\/li>\n<\/ul>\n<p>Bu sorunlar\u0131 te\u015fhis etmek i\u00e7in e\u011fitim setindeki performans\u0131 ile test setindeki performans\u0131 kar\u015f\u0131la\u015ft\u0131rmak \u00f6nemlidir. E\u011fer e\u011fitimde \u00e7ok iyi performans g\u00f6sterirken testte k\u00f6t\u00fc performans g\u00f6steriyorsa, b\u00fcy\u00fck olas\u0131l\u0131kla a\u015f\u0131r\u0131 \u00f6\u011frenme sorunuyla kar\u015f\u0131 kar\u015f\u0131yas\u0131n\u0131zd\u0131r.<\/p>\n<h3>Hiperparametre Ayarlamas\u0131: En \u0130yi Sonucu Nas\u0131l Elde Ederiz?<\/h3>\n<p>Bir yapay sinir a\u011f\u0131n\u0131n performans\u0131n\u0131 etkileyen iki t\u00fcr parametre vard\u0131r: a\u011f\u0131n e\u011fitim s\u0131ras\u0131nda \u00f6\u011frendi\u011fi a\u011f\u0131rl\u0131klar ve bias'lar ile bizim manuel olarak belirledi\u011fimiz \"hiperparametreler\". Hiperparametreler, \u00f6\u011frenme s\u00fcrecini ve a\u011f\u0131n mimarisini kontrol eden ayarlard\u0131r. \u00d6nemli hiperparametreler \u015funlar\u0131 i\u00e7erir:<\/p>\n<ul>\n<li><strong>\u00d6\u011frenme Oran\u0131 (Learning Rate):<\/strong> Optimizasyon algoritmas\u0131n\u0131n a\u011f\u0131rl\u0131klar\u0131 ne kadar b\u00fcy\u00fck ad\u0131mlarla g\u00fcncelleyece\u011fini belirler. \u00c7ok y\u00fcksek bir \u00f6\u011frenme oran\u0131 kay\u0131p fonksiyonunun minimumunu ka\u00e7\u0131rmas\u0131na neden olabilirken, \u00e7ok d\u00fc\u015f\u00fck bir oran e\u011fitimin \u00e7ok yava\u015f ilerlemesine neden olabilir.<\/li>\n<li><strong>Epoch Say\u0131s\u0131:<\/strong> E\u011fitim veri setinin a\u011f \u00fczerinden ka\u00e7 kez ge\u00e7irilece\u011fini belirler.<\/li>\n<li><strong>Batch Boyutu (Batch Size):<\/strong> Bir a\u011f\u0131rl\u0131k g\u00fcncellemesi yapmadan \u00f6nce ka\u00e7 veri \u00f6rne\u011finin i\u015flenece\u011fini belirler.<\/li>\n<li><strong>Katman Say\u0131s\u0131 ve N\u00f6ron Say\u0131s\u0131:<\/strong> A\u011f\u0131n mimarisini belirler.<\/li>\n<li><strong>Aktivasyon Fonksiyonu Se\u00e7imi:<\/strong> Her katmanda hangi aktivasyon fonksiyonunun kullan\u0131laca\u011f\u0131n\u0131 belirler.<\/li>\n<\/ul>\n<p>Do\u011fru hiperparametreleri bulmak genellikle bir deneme yan\u0131lma s\u00fcrecidir. Izgara aramas\u0131 (Grid Search), rastgele arama (Random Search) veya Bayesian optimizasyonu gibi teknikler, en iyi hiperparametre kombinasyonunu bulmaya yard\u0131mc\u0131 olabilir. Bu ayarlar\u0131n dikkatli bir \u015fekilde yap\u0131lmas\u0131, modelinizin performans\u0131n\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde art\u0131rabilir.<\/p>\n<div class=\"expert-tip\">\n  Uzman \u0130pucu: Hiperparametre ayar\u0131 yaparken \"\u00c7apraz Do\u011frulama\" (Cross-Validation) kullanmak, modelinizin yeni verilere genelleme yetene\u011fi hakk\u0131nda daha g\u00fcvenilir bir fikir edinmenizi sa\u011flar. Bu teknikle, veri setinizi birden fazla e\u011fitim\/do\u011frulama k\u00fcmesine b\u00f6lerek modelinizin tutarl\u0131l\u0131\u011f\u0131n\u0131 test edebilirsiniz.\n<\/div>\n<h3>Transfer \u00d6\u011frenme: Mevcut Bilgiyi Kullanmak Neden Avantajl\u0131?<\/h3>\n<p>S\u0131f\u0131rdan bir yapay sinir a\u011f\u0131 e\u011fitmek, \u00f6zellikle b\u00fcy\u00fck ve karma\u015f\u0131k modeller i\u00e7in \u00e7ok fazla veri ve hesaplama g\u00fcc\u00fc gerektirebilir. \u0130\u015fte burada \"transfer \u00f6\u011frenme\" (transfer learning) inan\u0131lmaz bir avantaj sunar. Transfer \u00f6\u011frenme, bir problem \u00fczerinde zaten e\u011fitilmi\u015f bir modelin (\u00f6nceden e\u011fitilmi\u015f model), farkl\u0131 ama ilgili bir problem \u00fczerinde yeniden kullan\u0131lmas\u0131d\u0131r.<\/p>\n<p>\u00d6rne\u011fin, bir g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma projeniz varsa ve kendi veri setiniz s\u0131n\u0131rl\u0131ysa, milyonlarca g\u00f6r\u00fcnt\u00fc \u00fczerinde e\u011fitilmi\u015f (ImageNet gibi b\u00fcy\u00fck veri setleri \u00fczerinde) ve genellikle derin katmanlara sahip bir CNN modelini (\u00f6rne\u011fin ResNet, VGG, Inception) alabilirsiniz. Bu \u00f6nceden e\u011fitilmi\u015f modelin ilk katmanlar\u0131 genellikle genel g\u00f6r\u00fcnt\u00fc \u00f6zelliklerini (kenarlar, dokular) \u00f6\u011frenmi\u015ftir. Siz sadece bu modelin son katmanlar\u0131n\u0131 kendi \u00f6zel s\u0131n\u0131fland\u0131rma g\u00f6revinize uyacak \u015fekilde de\u011fi\u015ftirebilir ve k\u00fc\u00e7\u00fck veri setinizle yeniden e\u011fitebilirsiniz. Bu, modelin zaten \u00f6\u011frenmi\u015f oldu\u011fu genel \u00f6zelliklerden faydalanarak e\u011fitim s\u00fcresini ve gereken veri miktar\u0131n\u0131 b\u00fcy\u00fck \u00f6l\u00e7\u00fcde azalt\u0131r.<\/p>\n<p>Transfer \u00f6\u011frenme, \u00f6zellikle kaynaklar\u0131n k\u0131s\u0131tl\u0131 oldu\u011fu durumlarda veya alan bilginizin belirli bir g\u00f6reve odaklanmas\u0131 gerekti\u011finde son derece g\u00fc\u00e7l\u00fc bir tekniktir. Bu sayede, \"tekerle\u011fi yeniden icat etme\" ihtiyac\u0131n\u0131 ortadan kald\u0131r\u0131r ve daha h\u0131zl\u0131, daha etkili \u00e7\u00f6z\u00fcmler geli\u015ftirmenizi sa\u011flar.<\/p>\n<h2>Sonu\u00e7: Yapay Sinir A\u011flar\u0131n\u0131n Gelece\u011fi Bize Ne Vaat Ediyor?<\/h2>\n<p>Bu makalede, makinelerin nas\u0131l \u00f6\u011frendi\u011finin temelinde yatan yapay sinir a\u011flar\u0131n\u0131, n\u00f6ronlardan aktivasyon fonksiyonlar\u0131na, ileri ve geri yay\u0131l\u0131m s\u00fcre\u00e7lerinden optimizasyona kadar detayl\u0131 bir \u015fekilde inceledik. Ayr\u0131ca, g\u00f6r\u00fcnt\u00fc tan\u0131ma, do\u011fal dil i\u015fleme ve otonom ara\u00e7lar gibi ger\u00e7ek d\u00fcnya uygulamalar\u0131yla bu karma\u015f\u0131k yap\u0131lar\u0131n hayat\u0131m\u0131z\u0131 nas\u0131l de\u011fi\u015ftirdi\u011fini g\u00f6rd\u00fck. A\u015f\u0131r\u0131 \u00f6\u011frenme, transfer \u00f6\u011frenme ve farkl\u0131 mimariler gibi ileri d\u00fczey konulara da de\u011finerek, bu alandaki derinli\u011fe k\u0131sa bir bak\u0131\u015f att\u0131k.<\/p>\n<p>Yapay sinir a\u011flar\u0131, b\u00fcy\u00fck veri k\u00fcmelerinden \u00f6\u011frenme ve karma\u015f\u0131k desenleri anlama konusundaki e\u015fsiz yetenekleriyle modern yapay zek\u00e2n\u0131n temelini olu\u015fturmaktad\u0131r. Her ge\u00e7en g\u00fcn daha da geli\u015fen algoritmalar ve artan hesaplama g\u00fcc\u00fcyle, yapay sinir a\u011flar\u0131n\u0131n potansiyeli neredeyse s\u0131n\u0131rs\u0131zd\u0131r. T\u0131p, bilim, m\u00fchendislik, sanat ve daha bir\u00e7ok alanda insanl\u0131\u011f\u0131n kar\u015f\u0131la\u015ft\u0131\u011f\u0131 en zorlu sorunlara yenilik\u00e7i \u00e7\u00f6z\u00fcmler sunmaya devam edeceklerdir. Gelecekte, daha otonom sistemler, daha ak\u0131ll\u0131 ki\u015fisel asistanlar ve bilimin s\u0131n\u0131rlar\u0131n\u0131 zorlayan ke\u015fifler, yapay sinir a\u011flar\u0131n\u0131n katk\u0131lar\u0131yla m\u00fcmk\u00fcn olacakt\u0131r. Bu heyecan verici alandaki geli\u015fmeler, hepimizi \u015fa\u015f\u0131rtmaya ve ilham vermeye devam edecek.<\/p>\n<div class=\"faq-section\">\n<h2>S\u0131k\u00e7a Sorulan Sorular<\/h2>\n<div class=\"faq-item\">\n<p class=\"faq-question\">1. Yapay sinir a\u011flar\u0131 ile geleneksel programlama aras\u0131ndaki temel fark nedir?<\/p>\n<p class=\"faq-answer\">Geleneksel programlamada, bir g\u00f6revi tamamlamak i\u00e7in a\u00e7\u0131k ve ad\u0131m ad\u0131m kurallar belirlersiniz. Yapay sinir a\u011flar\u0131 ise, verilerden kendi ba\u015flar\u0131na \u00f6\u011frenir ve kurallar\u0131 otomatik olarak \u00e7\u0131kar\u0131r. Yani, onlara ne yapacaklar\u0131n\u0131 de\u011fil, neyi nas\u0131l \u00f6\u011freneceklerini s\u00f6ylersiniz.<\/p>\n<\/p><\/div>\n<div class=\"faq-item\">\n<p class=\"faq-question\">2. Derin \u00f6\u011frenme tam olarak nedir?<\/p>\n<p class=\"faq-answer\">Derin \u00f6\u011frenme, yapay sinir a\u011flar\u0131n\u0131n bir alt k\u00fcmesidir. \"Derin\" terimi, a\u011f\u0131n birden fazla gizli katmana sahip olmas\u0131 anlam\u0131na gelir. Bu \u00e7ok katmanl\u0131 yap\u0131, modelin verilerdeki daha karma\u015f\u0131k ve soyut desenleri, hiyerar\u015fik bir \u015fekilde \u00f6\u011frenmesini sa\u011flar.<\/p>\n<\/p><\/div>\n<div class=\"faq-item\">\n<p class=\"faq-question\">3. Yapay sinir a\u011flar\u0131 her t\u00fcrl\u00fc problem i\u00e7in uygun mudur?<\/p>\n<p class=\"faq-answer\">Yapay sinir a\u011flar\u0131 bir\u00e7ok problemde etkileyici sonu\u00e7lar verse de, her zaman en iyi \u00e7\u00f6z\u00fcm olmayabilir. \u00d6zellikle s\u0131n\u0131rl\u0131 veri setlerinde veya daha basit, do\u011frusal ili\u015fkilerin oldu\u011fu durumlarda daha geleneksel makine \u00f6\u011frenimi algoritmalar\u0131 (\u00f6rne\u011fin karar a\u011fa\u00e7lar\u0131, regresyon) daha verimli ve anla\u015f\u0131l\u0131r olabilir. Ayr\u0131ca, y\u00fcksek hesaplama g\u00fcc\u00fc gerektirirler.<\/p>\n<\/p><\/div>\n<div class=\"faq-item\">\n<p class=\"faq-question\">4. Bir yapay sinir a\u011f\u0131 nas\u0131l \"unutabilir\" veya eski bilgilerini de\u011fi\u015ftirebilir?<\/p>\n<p class=\"faq-answer\">Yapay sinir a\u011flar\u0131, \u00f6\u011frenme s\u00fcrecinde a\u011f\u0131rl\u0131klar\u0131n\u0131 s\u00fcrekli g\u00fcncelleyerek \"bilgilerini\" de\u011fi\u015ftirirler. Yeni verilerle e\u011fitildiklerinde veya yeniden e\u011fitildiklerinde, a\u011f\u0131rl\u0131klar yeni verilere uyum sa\u011flayacak \u015fekilde ayarlan\u0131r. Bu durum, eski bilgilerin de\u011fi\u015fmesine veya yeni bilgilere g\u00f6re adapte olmas\u0131na neden olabilir. Bazen bu, \"katastrofik unutma\" (catastrophic forgetting) olarak bilinen bir soruna yol a\u00e7abilir.<\/p>\n<\/p><\/div>\n<div class=\"faq-item\">\n<p class=\"faq-question\">5. Yapay sinir a\u011flar\u0131 ne kadar veriyle e\u011fitilmelidir?<\/p>\n<p class=\"faq-answer\">Yapay sinir a\u011flar\u0131, \u00f6zellikle derin a\u011flar, performans\u0131 i\u00e7in genellikle \u00e7ok b\u00fcy\u00fck miktarda veri gerektirir. \"Ne kadar \u00e7ok veri, o kadar iyi\" ilkesi ge\u00e7erlidir. Ancak, gereken miktar problem karma\u015f\u0131kl\u0131\u011f\u0131na, a\u011f\u0131n mimarisine ve istenen do\u011fruluk seviyesine g\u00f6re de\u011fi\u015fir. Transfer \u00f6\u011frenme gibi teknikler, daha az veriyle bile ba\u015far\u0131l\u0131 modeller geli\u015ftirmeye yard\u0131mc\u0131 olabilir.<\/p>\n<\/p><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"G\u00fcn\u00fcm\u00fcz d\u00fcnyas\u0131nda ak\u0131ll\u0131 asistanlar, otonom ara\u00e7lar ve ki\u015fiselle\u015ftirilmi\u015f \u00f6neri sistemleri gibi teknolojiler hayat\u0131m\u0131z\u0131n vazge\u00e7ilmez bir par\u00e7as\u0131 haline gelmi\u015ftir.&hellip;","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-30991","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.5 (Yoast SEO v25.3.1) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Makineler Nas\u0131l \u00d6\u011frenir: Yapay Sinir A\u011flar\u0131n\u0131n Temellerini Anlamak<\/title>\n<meta name=\"description\" content=\"G\u00fcn\u00fcm\u00fcz d\u00fcnyas\u0131nda ak\u0131ll\u0131 asistanlar, otonom ara\u00e7lar ve ki\u015fiselle\u015ftirilmi\u015f \u00f6neri sistemleri gibi teknolojiler hayat\u0131m\u0131z\u0131n vazge\u00e7ilmez bir par\u00e7as\u0131 haline gelmi\u015ftir. Peki, bu makineler nas\u0131l &quot;d\u00fc\u015f\u00fcn\u00fcyor&quot; veya daha do\u011fru ifadeyle &quot;\u00f6\u011freniyor&quot;? Bu makine \u00f6\u011frenimi mucizesinin arkas\u0131nda yatan temel mekanizma genellikle yapay sinir a\u011flar\u0131d\u0131r. Bu yaz\u0131da, makinelerin nas\u0131l \u00f6\u011frendi\u011fini, yapay sinir a\u011flar\u0131n\u0131n temel kavramlar\u0131n\u0131 ve ger\u00e7ek d\u00fcnyadaki uygulamalar\u0131n\u0131 ba\u015flang\u0131\u00e7 seviyesinden ileri d\u00fczeye do\u011fru ke\u015ffedece\u011fiz. Haz\u0131rsan\u0131z, bu heyecan verici yolculu\u011fa ba\u015flayal\u0131m!\" \/>\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\/makineler-nasil-ogrenir-yapay-sinir-aglarinin-temellerini-anlamak\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Makineler Nas\u0131l \u00d6\u011frenir: Yapay Sinir A\u011flar\u0131n\u0131n Temellerini Anlamak\" \/>\n<meta property=\"og:description\" content=\"G\u00fcn\u00fcm\u00fcz d\u00fcnyas\u0131nda ak\u0131ll\u0131 asistanlar, otonom ara\u00e7lar ve ki\u015fiselle\u015ftirilmi\u015f \u00f6neri sistemleri gibi teknolojiler hayat\u0131m\u0131z\u0131n vazge\u00e7ilmez bir par\u00e7as\u0131 haline gelmi\u015ftir. Peki, bu makineler nas\u0131l &quot;d\u00fc\u015f\u00fcn\u00fcyor&quot; veya daha do\u011fru ifadeyle &quot;\u00f6\u011freniyor&quot;? Bu makine \u00f6\u011frenimi mucizesinin arkas\u0131nda yatan temel mekanizma genellikle yapay sinir a\u011flar\u0131d\u0131r. Bu yaz\u0131da, makinelerin nas\u0131l \u00f6\u011frendi\u011fini, yapay sinir a\u011flar\u0131n\u0131n temel kavramlar\u0131n\u0131 ve ger\u00e7ek d\u00fcnyadaki uygulamalar\u0131n\u0131 ba\u015flang\u0131\u00e7 seviyesinden ileri d\u00fczeye do\u011fru ke\u015ffedece\u011fiz. Haz\u0131rsan\u0131z, bu heyecan verici yolculu\u011fa ba\u015flayal\u0131m!\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fatihsoysal.com\/blog\/makineler-nasil-ogrenir-yapay-sinir-aglarinin-temellerini-anlamak\/\" \/>\n<meta property=\"og:site_name\" content=\"Kodlar\u0131n Gizemli D\u00fcnyas\u0131\" \/>\n<meta property=\"article:published_time\" content=\"2025-10-04T08:31:44+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=\"28 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/makineler-nasil-ogrenir-yapay-sinir-aglarinin-temellerini-anlamak\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/makineler-nasil-ogrenir-yapay-sinir-aglarinin-temellerini-anlamak\/\"},\"author\":{\"name\":\"Fatih Soysal\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"headline\":\"Makineler Nas\u0131l \u00d6\u011frenir: Yapay Sinir A\u011flar\u0131n\u0131n Temellerini Anlamak\",\"datePublished\":\"2025-10-04T08:31:44+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/makineler-nasil-ogrenir-yapay-sinir-aglarinin-temellerini-anlamak\/\"},\"wordCount\":5453,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/makineler-nasil-ogrenir-yapay-sinir-aglarinin-temellerini-anlamak\/#respond\"]}],\"copyrightYear\":\"2025\",\"copyrightHolder\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/makineler-nasil-ogrenir-yapay-sinir-aglarinin-temellerini-anlamak\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/makineler-nasil-ogrenir-yapay-sinir-aglarinin-temellerini-anlamak\/\",\"name\":\"Makineler Nas\u0131l \u00d6\u011frenir: Yapay Sinir A\u011flar\u0131n\u0131n Temellerini Anlamak\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\"},\"datePublished\":\"2025-10-04T08:31:44+00:00\",\"description\":\"G\u00fcn\u00fcm\u00fcz d\u00fcnyas\u0131nda ak\u0131ll\u0131 asistanlar, otonom ara\u00e7lar ve ki\u015fiselle\u015ftirilmi\u015f \u00f6neri sistemleri gibi teknolojiler hayat\u0131m\u0131z\u0131n vazge\u00e7ilmez bir par\u00e7as\u0131 haline gelmi\u015ftir. 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