Yapay zeka denildi\u011finde akla genellikle y\u00fcksek performansl\u0131 GPU’lar, devasa veri merkezleri ve milyarlarca parametreli modeller gelir. Ancak bu yolculukta, k\u0131s\u0131tl\u0131 kaynaklarla, bir GPU’nun bile l\u00fcks oldu\u011fu ortamlarda yapay zeka \u00e7\u00f6z\u00fcmleri geli\u015ftirmenin m\u00fcmk\u00fcn oldu\u011funu ke\u015ffettim. Bu makale, “k\u0131r\u0131k” veya geleneksel olarak yetersiz g\u00f6r\u00fclen modellerden, ger\u00e7ek d\u00fcnyada i\u015flevsel, verimli ve “canl\u0131” sistemler in\u015fa etme ser\u00fcvenimi anlat\u0131yor.<\/p>\n
Yapay zeka alan\u0131ndaki h\u0131zl\u0131 geli\u015fmeler, \u00f6zellikle derin \u00f6\u011frenme modellerinin y\u00fckseli\u015fiyle birlikte, GPU’lar\u0131n vazge\u00e7ilmez bir donan\u0131m oldu\u011fu alg\u0131s\u0131n\u0131 peki\u015ftirdi. Ancak bu alg\u0131, her zaman ger\u00e7e\u011fi yans\u0131tm\u0131yor.<\/p>\n
Derin \u00f6\u011frenme modellerinin e\u011fitimi, matris \u00e7arp\u0131mlar\u0131 gibi yo\u011fun paralel hesaplamalar gerektirir. GPU’lar, binlerce \u00e7ekirde\u011fi sayesinde bu t\u00fcr i\u015flemleri CPU’lara g\u00f6re kat kat daha h\u0131zl\u0131 yapabilir. Bu nedenle, b\u00fcy\u00fck veri k\u00fcmeleri ve karma\u015f\u0131k sinir a\u011flar\u0131 ile \u00e7al\u0131\u015f\u0131rken GPU kullan\u0131m\u0131 standart hale gelmi\u015ftir. Ancak bu durum, yapay zekan\u0131n t\u00fcm alanlar\u0131na genellenemez.<\/p>\n
Hay\u0131r, her zaman gerekli de\u011fil. Yapay zeka, sadece derin \u00f6\u011frenmeden ibaret de\u011fildir. Geleneksel makine \u00f6\u011frenimi algoritmalar\u0131, \u00f6zellik m\u00fchendisli\u011fi ve dikkatli model se\u00e7imi ile bir\u00e7ok problem, GPU’ya ihtiya\u00e7 duymadan \u00e7\u00f6z\u00fclebilir. \u00d6zellikle s\u0131n\u0131rl\u0131 veri setleri, daha basit g\u00f6revler veya u\u00e7 cihazlarda \u00e7al\u0131\u015ft\u0131r\u0131lacak modeller i\u00e7in GPU’lar \u00e7o\u011fu zaman a\u015f\u0131r\u0131ya ka\u00e7an bir yat\u0131r\u0131m olabilir.<\/p>\n
GPU’lar d\u0131\u015f\u0131nda da yapay zeka h\u0131zland\u0131rma donan\u0131mlar\u0131 mevcuttur. TPU’lar (Tensor Processing Units) Google taraf\u0131ndan belirli i\u015f y\u00fckleri i\u00e7in geli\u015ftirilmi\u015fken, FPGA’lar (Field-Programmable Gate Arrays) ve \u00e7e\u015fitli n\u00f6romorfik \u00e7ipler de enerji verimli ve \u00f6zelle\u015ftirilebilir \u00e7\u00f6z\u00fcmler sunar. Ancak bu makalede as\u0131l odak noktam\u0131z, standart CPU’lar ve k\u0131s\u0131tl\u0131 bellek gibi temel kaynaklarla nas\u0131l ba\u015far\u0131l\u0131 olunaca\u011f\u0131d\u0131r.<\/p>\n
GPU olmadan yapay zeka geli\u015ftirmek, temelde verimlili\u011fi her \u015feyin \u00f6n\u00fcne koymak demektir. Bu, algoritma se\u00e7iminden veri i\u015fleme tekniklerine kadar her a\u015famada titiz bir optimizasyon gerektirir.<\/p>\n
Lojistik regresyon, Destek Vekt\u00f6r Makineleri (SVM), Karar A\u011fa\u00e7lar\u0131, Rastgele Ormanlar ve Gradient Boosting gibi geleneksel algoritmalar, do\u011fru kullan\u0131ld\u0131\u011f\u0131nda \u015fa\u015f\u0131rt\u0131c\u0131 derecede iyi sonu\u00e7lar verebilir. Bu algoritmalar genellikle daha az hesaplama g\u00fcc\u00fc gerektirir ve daha az veriyle bile anlaml\u0131 \u00e7\u0131kar\u0131mlar yapabilir. \u00d6rne\u011fin, Scikit-learn gibi k\u00fct\u00fcphaneler, CPU \u00fczerinde son derece optimize edilmi\u015f bu algoritmalar\u0131 sunar.<\/p>\n
\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import accuracy_score\n\n# \u00d6rnek veri seti\ndata = pd.read_csv(\"veri_seti.csv\")\nX = data.drop(\"hedef\", axis=1)\ny = data[\"hedef\"]\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# GPU gerektirmeyen basit bir Random Forest modeli\nmodel = RandomForestClassifier(n_estimators=100, max_depth=10, random_state=42)\nmodel.fit(X_train, y_train)\n\npredictions = model.predict(X_test)\nprint(f\"Model Do\u011frulu\u011fu: {accuracy_score(y_test, predictions):.2f}\")\n<\/pre>\n<\/code><\/p>\n
\u00d6zellik M\u00fchendisli\u011fi ve Veri Optimizasyonu<\/h4>\n
Modelin karma\u015f\u0131kl\u0131\u011f\u0131n\u0131 art\u0131rmak yerine, veriyi daha anlaml\u0131 hale getirmek, GPU ihtiyac\u0131n\u0131 azaltman\u0131n anahtarlar\u0131ndan biridir. Etkili \u00f6zellik m\u00fchendisli\u011fi, ham veriden modelin \u00f6\u011frenmesini kolayla\u015ft\u0131racak yeni, a\u00e7\u0131klay\u0131c\u0131 \u00f6zellikler \u00e7\u0131karmay\u0131 i\u00e7erir. Ayr\u0131ca, gereksiz veya g\u00fcr\u00fclt\u00fcl\u00fc verileri temizlemek, veri boyutunu k\u00fc\u00e7\u00fcltmek ve dengesiz veri setlerini dengelemek de modelin daha h\u0131zl\u0131 ve do\u011fru \u00f6\u011frenmesine yard\u0131mc\u0131 olur.<\/p>\n
Model Boyutunu K\u00fc\u00e7\u00fcltme Teknikleri<\/h4>\n
Modelin kendisini daha hafif hale getirmek, \u00f6zellikle da\u011f\u0131t\u0131m a\u015famas\u0131nda kritik \u00f6neme sahiptir. Bu, daha az katmana sahip a\u011flar kullanmak, daha az parametreli modeller se\u00e7mek veya mevcut modelleri s\u0131k\u0131\u015ft\u0131rmak anlam\u0131na gelebilir. Boyut k\u00fc\u00e7\u00fcltme teknikleri aras\u0131nda PCA (Temel Bile\u015fen Analizi) gibi y\u00f6ntemler, veri boyutunu azalt\u0131rken \u00f6nemli bilgileri korumak i\u00e7in kullan\u0131labilir.<\/p>\n
K\u0131s\u0131tl\u0131 Ortamlarda Derin \u00d6\u011frenme (Minimalist Yakla\u015f\u0131m)<\/h3>\n
Derin \u00f6\u011frenmeden tamamen vazge\u00e7mek zorunda de\u011filsiniz. Ancak k\u0131s\u0131tl\u0131 kaynaklarla \u00e7al\u0131\u015f\u0131rken, derin \u00f6\u011frenme modellerini \"minimalist\" bir yakla\u015f\u0131mla ele almak gerekir.<\/p>\n
Nicemleme (Quantization) ve Budama (Pruning)<\/h4>\n
Nicemleme, modelin a\u011f\u0131rl\u0131klar\u0131n\u0131 ve aktivasyonlar\u0131n\u0131 daha d\u00fc\u015f\u00fck bit hassasiyetine (\u00f6rne\u011fin 32-bit float'tan 8-bit integer'a) d\u00f6n\u00fc\u015ft\u00fcrerek model boyutunu k\u00fc\u00e7\u00fclt\u00fcr ve hesaplama h\u0131z\u0131n\u0131 art\u0131r\u0131r. Budama ise, modelin performans\u0131n\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde etkilemeden, a\u011fdaki gereksiz veya \u00f6nemsiz ba\u011flant\u0131lar\u0131 ve n\u00f6ronlar\u0131 kald\u0131rma i\u015flemidir. Bu iki teknik, modelin bellek ayak izini ve hesaplama gereksinimlerini dramatik \u015fekilde azaltabilir.<\/p>\n
Bilgi Dam\u0131tma (Knowledge Distillation)<\/h4>\n
Bilgi dam\u0131tma, b\u00fcy\u00fck ve karma\u015f\u0131k bir \"\u00f6\u011fretmen\" modelin \u00f6\u011frendi\u011fi bilgiyi, daha k\u00fc\u00e7\u00fck ve basit bir \"\u00f6\u011frenci\" modele aktarma y\u00f6ntemidir. \u00d6\u011frenci model, \u00f6\u011fretmen modelin \u00e7\u0131kt\u0131lar\u0131n\u0131 taklit ederek e\u011fitilir ve b\u00f6ylece orijinal modelin performans\u0131na yak\u0131n bir ba\u015far\u0131 elde ederken, \u00e7ok daha az kaynak t\u00fcketir.<\/p>\n
Hafif Mimariye Sahip A\u011flar (MobileNets, SqueezeNets)<\/h4>\n
Mobil cihazlar ve u\u00e7 bili\u015fim i\u00e7in \u00f6zel olarak tasarlanm\u0131\u015f derin \u00f6\u011frenme mimarileri mevcuttur. MobileNets, SqueezeNets ve EfficientNets gibi modeller, do\u011falar\u0131 gere\u011fi daha az parametreye ve daha d\u00fc\u015f\u00fck hesaplama maliyetine sahiptir. Bu a\u011flar, \u00f6zellikle g\u00f6r\u00fcnt\u00fc i\u015fleme g\u00f6revlerinde, GPU'ya ihtiya\u00e7 duymadan CPU \u00fczerinde bile makul performans sergileyebilir.<\/p>\n
Edge AI ve TinyML'in Y\u00fckseli\u015fi<\/h3>\n
GPU'suz yapay zeka yolculu\u011funun do\u011fal bir uzant\u0131s\u0131 olarak Edge AI (U\u00e7 Yapay Zeka) ve TinyML (K\u00fc\u00e7\u00fck Yapay Zeka) kavramlar\u0131 \u00f6ne \u00e7\u0131k\u0131yor. Bu alanlar, yapay zekay\u0131 do\u011frudan veri kayna\u011f\u0131n\u0131n yak\u0131n\u0131nda veya \u00fczerinde \u00e7al\u0131\u015ft\u0131rmay\u0131 hedefler.<\/p>\n
U\u00e7 Cihazlarda Yapay Zeka<\/h4>\n
Verilerin buluta g\u00f6nderilmeden, do\u011frudan cihaz \u00fczerinde i\u015flenmesi, gecikmeyi azalt\u0131r, gizlili\u011fi art\u0131r\u0131r ve bant geni\u015fli\u011fi maliyetlerini d\u00fc\u015f\u00fcr\u00fcr. Ak\u0131ll\u0131 kameralar, sens\u00f6rler ve giyilebilir cihazlar gibi u\u00e7 cihazlar, k\u0131s\u0131tl\u0131 i\u015flem g\u00fcc\u00fc ve enerjiye sahip olduklar\u0131 i\u00e7in GPU'suz AI yakla\u015f\u0131mlar\u0131 i\u00e7in ideal platformlard\u0131r.<\/p>\n
Enerji Verimlili\u011fi ve Ger\u00e7ek Zamanl\u0131 \u0130\u015fleme<\/h4>\n
U\u00e7 cihazlarda yapay zeka \u00e7al\u0131\u015ft\u0131rman\u0131n en b\u00fcy\u00fck avantajlar\u0131ndan biri enerji verimlili\u011fidir. Pil \u00f6mr\u00fcn\u00fcn kritik oldu\u011fu durumlarda, optimize edilmi\u015f ve hafif modeller, s\u00fcrekli \u00e7al\u0131\u015fabilen sistemler olu\u015fturmak i\u00e7in elzemdir. Ayr\u0131ca, ger\u00e7ek zamanl\u0131 tepki gerektiren uygulamalar (\u00f6rne\u011fin, otonom ara\u00e7lar veya end\u00fcstriyel otomasyon) i\u00e7in verinin an\u0131nda i\u015flenmesi hayati \u00f6nem ta\u015f\u0131r.<\/p>\n
\u00d6rnek Kullan\u0131m Senaryolar\u0131<\/h4>\n\n- IoT Cihazlar\u0131:<\/strong> Ak\u0131ll\u0131 ev sens\u00f6rleri, anomalileri yerel olarak tespit ederek enerji tasarrufu sa\u011flar.<\/li>\n
- Giyilebilir Teknolojiler:<\/strong> Ak\u0131ll\u0131 saatler, sa\u011fl\u0131k verilerini (kalp at\u0131\u015f\u0131, aktivite) analiz ederek ki\u015fiselle\u015ftirilmi\u015f geri bildirimler sunar.<\/li>\n
- End\u00fcstriyel Otomasyon:<\/strong> Fabrika makineleri, \u00fcretim hatt\u0131ndaki hatalar\u0131 an\u0131nda tespit ederek duru\u015f s\u00fcrelerini minimize eder.<\/li>\n
- Tar\u0131m:<\/strong> Ak\u0131ll\u0131 sens\u00f6rler, bitki sa\u011fl\u0131\u011f\u0131n\u0131 ve toprak ko\u015fullar\u0131n\u0131 analiz ederek su ve g\u00fcbre kullan\u0131m\u0131n\u0131 optimize eder.<\/li>\n<\/ul>\n
A\u00e7\u0131k Kaynak ve Topluluk G\u00fcc\u00fc<\/h3>\n
Bu yolculukta yaln\u0131z de\u011fildim. A\u00e7\u0131k kaynak d\u00fcnyas\u0131 ve canl\u0131 yapay zeka topluluklar\u0131, GPU'suz \u00e7\u00f6z\u00fcmler geli\u015ftirmek i\u00e7in paha bi\u00e7ilmez bir destek sa\u011flad\u0131.<\/p>\n
K\u00fct\u00fcphaneler ve \u00c7er\u00e7eveler<\/h4>\n
Scikit-learn, TensorFlow Lite, PyTorch Mobile ve ONNX Runtime gibi k\u00fct\u00fcphaneler, modelleri optimize etmek, farkl\u0131 donan\u0131mlarda \u00e7al\u0131\u015ft\u0131rmak ve da\u011f\u0131tmak i\u00e7in g\u00fc\u00e7l\u00fc ara\u00e7lar sunar. \u00d6zellikle TensorFlow Lite ve PyTorch Mobile, derin \u00f6\u011frenme modellerini mobil ve u\u00e7 cihazlarda \u00e7al\u0131\u015ft\u0131rmak \u00fczere tasarlanm\u0131\u015ft\u0131r. ONNX (Open Neural Network Exchange) ise, farkl\u0131 \u00e7er\u00e7evelerde e\u011fitilmi\u015f modelleri standart bir formatta d\u0131\u015fa aktararak donan\u0131mdan ba\u011f\u0131ms\u0131z da\u011f\u0131t\u0131m\u0131 kolayla\u015ft\u0131r\u0131r.<\/p>\n
\n# TensorFlow Lite ile model d\u00f6n\u00fc\u015f\u00fcm\u00fc (pseudocode)\nimport tensorflow as tf\n\n# E\u011fitilmi\u015f bir Keras modeli y\u00fckle\nmodel = tf.keras.models.load_model(\"egitilmis_model.h5\")\n\n# TFLite d\u00f6n\u00fc\u015ft\u00fcr\u00fcc\u00fcs\u00fcn\u00fc ba\u015flat\nconverter = tf.lite.TFLiteConverter.from_keras_model(model)\n\n# Nicemleme (Quantization) uygula\nconverter.optimizations = [tf.lite.Optimize.DEFAULT]\n\n# TFLite modelini olu\u015ftur\ntflite_model = converter.convert()\n\n# Modeli kaydet\nwith open(\"model.tflite\", \"wb\") as f:\n f.write(tflite_model)\n<\/pre>\n<\/code><\/p>\n
Topluluk Deste\u011fi ve Kaynak Payla\u015f\u0131m\u0131<\/h4>\n
Kaggle, GitHub, Stack Overflow ve \u00e7e\u015fitli forumlar, kar\u015f\u0131la\u015f\u0131lan sorunlara \u00e7\u00f6z\u00fcm bulmak, yeni teknikler \u00f6\u011frenmek ve ilham almak i\u00e7in harika platformlard\u0131r. Deneyimli geli\u015ftiricilerin payla\u015ft\u0131\u011f\u0131 projeler, kod \u00f6rnekleri ve makaleler, k\u0131s\u0131tl\u0131 kaynaklarla \u00e7al\u0131\u015fanlar i\u00e7in yol g\u00f6sterici olmu\u015ftur.<\/p>\n
Kendi \u00c7\u00f6z\u00fcmlerinizi Geli\u015ftirme<\/h4>\n
Bazen mevcut k\u00fct\u00fcphaneler veya modeller tam olarak ihtiyac\u0131n\u0131z\u0131 kar\u015f\u0131lamayabilir. Bu durumda, temel algoritmalar\u0131 anlayarak ve kendi \u00f6zel ihtiya\u00e7lar\u0131n\u0131za g\u00f6re optimize edilmi\u015f \u00e7\u00f6z\u00fcmler geli\u015ftirerek ilerlemek m\u00fcmk\u00fcnd\u00fcr. Bu, daha derin bir anlay\u0131\u015f kazand\u0131r\u0131r ve ger\u00e7ek anlamda \"canl\u0131\" sistemler yaratman\u0131za olanak tan\u0131r.<\/p>\n
Yolculu\u011fumdan Dersler ve Gelecek Vizyonu<\/h3>\n
Bu yolculuk, sadece teknik bir \u00f6\u011frenme s\u00fcreci de\u011fil, ayn\u0131 zamanda problem \u00e7\u00f6zmeye ve inovasyona bak\u0131\u015f a\u00e7\u0131m\u0131 da de\u011fi\u015ftiren bir deneyim oldu.<\/p>\n
Ba\u015far\u0131s\u0131zl\u0131klar ve \u00d6\u011frenilenler<\/h4>\n
Elbette, her zaman ba\u015far\u0131ya ula\u015f\u0131lmad\u0131. Yanl\u0131\u015f algoritma se\u00e7imleri, yetersiz optimizasyonlar veya veri kalitesi sorunlar\u0131 nedeniyle projeler ba\u015far\u0131s\u0131z oldu. Ancak her ba\u015far\u0131s\u0131zl\u0131k, bir \u00f6\u011frenme f\u0131rsat\u0131yd\u0131. Hangi algoritmalar\u0131n hangi veri setleriyle daha iyi \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131, ne zaman nicemleme veya budaman\u0131n faydal\u0131 oldu\u011funu ve en \u00f6nemlisi, bir problemi \u00e7\u00f6zmek i\u00e7in her zaman en karma\u015f\u0131k araca ihtiya\u00e7 duyulmad\u0131\u011f\u0131n\u0131 \u00f6\u011frendim.<\/p>\n
S\u00fcrd\u00fcr\u00fclebilir Yapay Zeka Geli\u015ftirme<\/h4>\n
GPU'suz yapay zeka, sadece k\u0131s\u0131tl\u0131 kaynaklarla \u00e7al\u0131\u015fmakla kalm\u0131yor, ayn\u0131 zamanda daha s\u00fcrd\u00fcr\u00fclebilir bir yakla\u015f\u0131m\u0131 da temsil ediyor. Daha az enerji t\u00fcketimi, daha d\u00fc\u015f\u00fck donan\u0131m maliyetleri ve daha az \u00e7evresel etki, yapay zekay\u0131 daha eri\u015filebilir ve sorumlu hale getiriyor. Bu, gelecekte yapay zeka uygulamalar\u0131n\u0131n yayg\u0131nla\u015fmas\u0131 i\u00e7in kritik bir fakt\u00f6r olacakt\u0131r.<\/p>\n
Yeni Nesil \"Canl\u0131 Sistemler\"<\/h4>\n
\"K\u0131r\u0131k modellerden canl\u0131 sistemlere\" ifadesi, sadece teknik bir ge\u00e7i\u015fi de\u011fil, ayn\u0131 zamanda bir zihniyet de\u011fi\u015fimini de ifade ediyor. Amac\u0131m\u0131z, sadece y\u00fcksek do\u011fruluk oranlar\u0131 elde etmek de\u011fil, ayn\u0131 zamanda ger\u00e7ek d\u00fcnyada s\u00fcrekli \u00e7al\u0131\u015fabilen, adaptif, enerji verimli ve ba\u011flam\u0131na duyarl\u0131 sistemler yaratmakt\u0131r. Bu sistemler, bir laboratuvar ortam\u0131nda de\u011fil, ger\u00e7ek hayat\u0131n zorlu ko\u015fullar\u0131nda \"canl\u0131\" kalabilen ve de\u011fer \u00fcretebilen sistemlerdir.<\/p>\n
Sonu\u00e7<\/h3>\n
Yapay zeka d\u00fcnyas\u0131, GPU'lara ba\u011f\u0131ml\u0131 olmak zorunda de\u011fildir. K\u0131s\u0131tl\u0131 kaynaklarla, do\u011fru yakla\u015f\u0131mlar, algoritmalar ve optimizasyon teknikleriyle, \u015fa\u015f\u0131rt\u0131c\u0131 derecede g\u00fc\u00e7l\u00fc ve verimli yapay zeka \u00e7\u00f6z\u00fcmleri geli\u015ftirmek m\u00fcmk\u00fcnd\u00fcr. Bu yolculuk, bana sadece teknik beceriler kazand\u0131rmakla kalmad\u0131, ayn\u0131 zamanda problem \u00e7\u00f6zme esnekli\u011finin ve inovasyonun donan\u0131mdan \u00e7ok daha de\u011ferli oldu\u011funu g\u00f6sterdi. Gelecekte, \"canl\u0131 sistemler\" olarak adland\u0131rd\u0131\u011f\u0131m bu hafif, adaptif ve enerji verimli yapay zeka modellerinin, hayat\u0131m\u0131z\u0131n her alan\u0131nda daha fazla yer bulaca\u011f\u0131na inan\u0131yorum.<\/p>\n
SSS (S\u0131k Sorulan Sorular)<\/h3>\n1. GPU olmadan hangi t\u00fcr yapay zeka g\u00f6revleri yap\u0131labilir?<\/h4>\n
G\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma, metin analizi, tahminleme, anomali tespiti ve do\u011fal dil i\u015fleme gibi bir\u00e7ok g\u00f6rev, geleneksel makine \u00f6\u011frenimi algoritmalar\u0131 veya optimize edilmi\u015f hafif derin \u00f6\u011frenme modelleri kullan\u0131larak GPU olmadan ba\u015far\u0131yla ger\u00e7ekle\u015ftirilebilir. \u00d6zellikle veri miktar\u0131 ve model karma\u015f\u0131kl\u0131\u011f\u0131 orta d\u00fczeyde olan projeler i\u00e7in idealdir.<\/p>\n
2. CPU \u00fczerinde derin \u00f6\u011frenme e\u011fitimi ne kadar yava\u015f olur?<\/h4>\n
GPU'ya k\u0131yasla CPU \u00fczerinde derin \u00f6\u011frenme e\u011fitimi \u00e7ok daha yava\u015f olacakt\u0131r. Ancak, k\u00fc\u00e7\u00fck veri setleri, daha az katmana sahip modeller veya \u00f6nceden e\u011fitilmi\u015f modellerin ince ayarlanmas\u0131 gibi durumlarda CPU \u00fczerinde e\u011fitim hala pratik olabilir. \u00d6nemli olan, e\u011fitim s\u00fcresinin projenin gereksinimleriyle uyumlu olup olmad\u0131\u011f\u0131n\u0131 de\u011ferlendirmektir.<\/p>\n
3. Modelleri k\u00fc\u00e7\u00fcltmek performans kayb\u0131na neden olur mu?<\/h4>\n
Evet, nicemleme veya budama gibi model k\u00fc\u00e7\u00fcltme teknikleri genellikle bir miktar performans (do\u011fruluk) kayb\u0131na neden olabilir. Ancak bu kay\u0131p, \u00e7o\u011fu zaman kabul edilebilir d\u00fczeydedir ve elde edilen bellek ve h\u0131z avantajlar\u0131 kar\u015f\u0131s\u0131nda feda edilebilir. \u00d6nemli olan, performans kayb\u0131n\u0131 minimumda tutarken maksimum s\u0131k\u0131\u015ft\u0131rma sa\u011flamakt\u0131r.<\/p>\n
4. Ba\u015flang\u0131\u00e7 seviyesinde GPU olmadan yapay zeka \u00f6\u011frenmek i\u00e7in hangi ara\u00e7lar\u0131 \u00f6nerirsiniz?<\/h4>\n
Python dili ve Scikit-learn k\u00fct\u00fcphanesi, geleneksel makine \u00f6\u011frenimi algoritmalar\u0131n\u0131 \u00f6\u011frenmek i\u00e7in harika bir ba\u015flang\u0131\u00e7 noktas\u0131d\u0131r. Derin \u00f6\u011frenmeye ge\u00e7mek isterseniz, TensorFlow Lite veya PyTorch Mobile dok\u00fcmantasyonlar\u0131 ve \u00f6rnekleri, hafif modellerle \u00e7al\u0131\u015fmaya ba\u015flamak i\u00e7in de\u011ferli kaynaklard\u0131r. Ayr\u0131ca, Kaggle gibi platformlardaki CPU dostu yar\u0131\u015fmalara kat\u0131larak pratik yapabilirsiniz.<\/p>\n","protected":false},"excerpt":{"rendered":"Yapay zeka alan\u0131ndaki h\u0131zl\u0131 geli\u015fmeler, \u00f6zellikle derin \u00f6\u011frenme modellerinin y\u00fckseli\u015fiyle birlikte, GPU’lar\u0131n vazge\u00e7ilmez bir donan\u0131m oldu\u011fu alg…","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-37015","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\n
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