Yukar\u0131daki ilk maddede de belirtildi\u011fi gibi, Python'\u0131n pop\u00fclaritesi b\u00fcy\u00fck \u00f6l\u00e7\u00fcde \u00e7e\u015fitli ve kullan\u0131m\u0131 kolay k\u00fct\u00fcphaneleriyle ilgilidir. Python k\u00fct\u00fcphaneleri, s\u0131f\u0131rdan ba\u015flamam\u0131za gerek kalmadan kod yazmam\u0131z\u0131 sa\u011flayan kullan\u0131\u015fl\u0131 i\u015flevlerden olu\u015fan bir koleksiyondur. 137.000'den fazla k\u00fct\u00fcphaneyle Python, makine \u00f6\u011frenimi, veri bilimi, veri g\u00f6rselle\u015ftirme, g\u00f6r\u00fcnt\u00fc ve veri i\u015fleme ve daha bir\u00e7ok alanda program ve modeller olu\u015fturmak i\u00e7in kullan\u0131labilir.<\/p>\n
1. Pandalar<\/h2>\n
![\"\"](\"https:\/\/cdn.itpiran.net\/2024\/01\/13132053\/Pandas.png\")
Pandas, BSD (Berkeley Yaz\u0131l\u0131m Da\u011f\u0131t\u0131m\u0131) lisansl\u0131 a\u00e7\u0131k kaynakl\u0131 bir k\u00fct\u00fcphanedir. Bu pop\u00fcler k\u00fct\u00fcphane, veri bilimi alan\u0131nda yayg\u0131n olarak kullan\u0131lmaktad\u0131r. \u00c7o\u011funlukla veri analizi, d\u00fczenleme, temizleme vb. ama\u00e7lar i\u00e7in kullan\u0131l\u0131rlar. Pandas, R gibi ba\u015fka bir dile ge\u00e7meye gerek kalmadan veri modelleme ve veri analizi i\u015flemlerine olanak tan\u0131r. Python k\u00fct\u00fcphaneleri genellikle a\u015fa\u011f\u0131daki veri t\u00fcrlerini destekler:<\/p>\n
- \n
- Bir veri k\u00fcmesindeki veriler.<\/li>\n
- S\u0131ral\u0131 ve s\u0131ras\u0131z verileri i\u00e7eren zaman serileri.<\/li>\n
- Matris verilerinin sat\u0131rlar\u0131 ve s\u00fctunlar\u0131 etiketlenir.<\/li>\n
- Etiketlenmemi\u015f bilgi.<\/li>\n
- Herhangi bir di\u011fer istatistiksel bilgi t\u00fcr\u00fc.<\/li>\n<\/ul>\n
2. NumPy<\/h2>\n
\nNumPy, bilimsel hesaplamalara odaklanan en yayg\u0131n kullan\u0131lan a\u00e7\u0131k kaynakl\u0131 Python k\u00fct\u00fcphanelerinden biridir. H\u0131zl\u0131 hesaplamalar i\u00e7in yerle\u015fik matematiksel fonksiyonlara sahiptir ve b\u00fcy\u00fck matrisler ile \u00e7ok boyutlu verileri destekler. "Say\u0131sal Python", "NumPy" terimiyle tan\u0131mlan\u0131r. Lineer cebirde, genel veriler i\u00e7in \u00e7ok boyutlu bir kapsay\u0131c\u0131 olarak ve rastgele say\u0131 \u00fcreteci olarak kullan\u0131labilir. NumPy'deki baz\u0131 \u00f6nemli fonksiyonlar \u015funlard\u0131r: arcsin(), arccos(), tan(), radians() vb. Python'da, daha az bellek kaplad\u0131\u011f\u0131, daha h\u0131zl\u0131 ve kullan\u0131m\u0131 daha kolay oldu\u011fu i\u00e7in Listelere g\u00f6re NumPy Dizisi tercih edilir.<\/p>\n\u00d6zellikler:<\/h5>\n
- \n
- Etkile\u015fimli: Numpy olduk\u00e7a etkile\u015fimli ve kullan\u0131c\u0131 dostu bir k\u00fct\u00fcphanedir.<\/li>\n
- Matematik: NumPy, zor matematiksel denklemlerin y\u00fcr\u00fct\u00fclmesini kolayla\u015ft\u0131r\u0131r.<\/li>\n
- Sezgisel: Kodlamay\u0131 ve konular\u0131 anlamay\u0131 kolayla\u015ft\u0131r\u0131r.<\/li>\n
- Y\u00fcksek etkile\u015fim: Yayg\u0131n olarak kullan\u0131ld\u0131\u011f\u0131 i\u00e7in etkile\u015fimi y\u00fcksektir, dolay\u0131s\u0131yla a\u00e7\u0131k kaynak katk\u0131s\u0131 da fazlad\u0131r.<\/li>\n<\/ol>\n
NumPy aray\u00fcz\u00fc, g\u00f6rselle\u015ftirme i\u00e7in g\u00f6r\u00fcnt\u00fcleri, ses dalga formlar\u0131n\u0131 ve di\u011fer ham ikili ak\u0131\u015flar\u0131 ger\u00e7ek de\u011ferlerden olu\u015fan N boyutlu bir dizi olarak temsil etmek i\u00e7in kullan\u0131labilir. Bu k\u00fct\u00fcphaneyi makine \u00f6\u011frenimi i\u00e7in uygulayacak tam y\u0131\u011f\u0131n geli\u015ftiricilerin NumPy bilgisine sahip olmas\u0131 gerekir.<\/p>\n
3. Keras<\/h2>\n
\nKeras, derin sinir a\u011flar\u0131n\u0131 h\u0131zl\u0131 bir \u015fekilde test etmemizi sa\u011flayan, a\u00e7\u0131k kaynakl\u0131, Python tabanl\u0131 bir sinir a\u011f\u0131 k\u00fct\u00fcphanesidir. Derin \u00f6\u011frenme giderek daha pop\u00fcler hale geldik\u00e7e, Keras harika bir se\u00e7enek olarak ortaya \u00e7\u0131k\u0131yor \u00e7\u00fcnk\u00fc yarat\u0131c\u0131lar\u0131na g\u00f6re makineler i\u00e7in de\u011fil, insanlar i\u00e7in tasarlanm\u0131\u015f bir API (uygulama programlama aray\u00fcz\u00fc). Keras, end\u00fcstri ve ara\u015ft\u0131rma camias\u0131nda TensorFlow veya Theano'dan daha y\u00fcksek bir benimsenme oran\u0131na sahiptir. Keras'\u0131 kurmadan \u00f6nce TensorFlow Arka U\u00e7 Motoru'nu kurman\u0131z \u00f6nerilir.<\/p>\n\u00d6zellikler:<\/h5>\n
- \n
- Hem CPU (Merkezi \u0130\u015flem Birimi) hem de GPU (Grafik \u0130\u015flem Birimi) \u00fczerinde sorunsuz \u00e7al\u0131\u015fmaktad\u0131r.<\/li>\n
- Keras, tam ba\u011flant\u0131l\u0131, evri\u015fimli, havuzlanm\u0131\u015f, yinelemeli, g\u00f6mme vb. dahil olmak \u00fczere hemen hemen t\u00fcm sinir a\u011f\u0131 modellerini destekler. Bu modeller ayr\u0131ca daha karma\u015f\u0131k modeller olu\u015fturmak i\u00e7in birle\u015ftirilebilir.<\/li>\n
- Keras'\u0131n mod\u00fcler tasar\u0131m\u0131 onu son derece etkileyici, uyarlanabilir ve ileri d\u00fczey ara\u015ft\u0131rmalar i\u00e7in uygun hale getiriyor.<\/li>\n
- Keras, \u00e7e\u015fitli model ve projelerin hata ay\u0131klamas\u0131n\u0131 ve ke\u015ffedilmesini kolayla\u015ft\u0131ran Python tabanl\u0131 bir framework't\u00fcr.<\/li>\n<\/ol>\n
4. TensorFlow<\/h2>\n
\nTensorFlow, a\u00e7\u0131k kaynakl\u0131, y\u00fcksek performansl\u0131 bir say\u0131sal hesaplama k\u00fct\u00fcphanesidir. Ayr\u0131ca derin \u00f6\u011frenme ve makine \u00f6\u011frenimi algoritmalar\u0131nda da kullan\u0131l\u0131r. Google'\u0131n Yapay Zeka organizasyonundaki Google Brain ekibindeki ara\u015ft\u0131rmac\u0131lar taraf\u0131ndan olu\u015fturulmu\u015ftur ve g\u00fcn\u00fcm\u00fczde matematik, fizik ve makine \u00f6\u011frenimi ara\u015ft\u0131rmac\u0131lar\u0131 taraf\u0131ndan karma\u015f\u0131k matematiksel hesaplamalar i\u00e7in yayg\u0131n olarak kullan\u0131lmaktad\u0131r. TensorFlow, h\u0131zl\u0131 olacak \u015fekilde tasarlanm\u0131\u015ft\u0131r ve h\u0131zl\u0131 do\u011frusal cebir hesaplamalar\u0131 ger\u00e7ekle\u015ftirmek i\u00e7in XLA (XLA veya H\u0131zland\u0131r\u0131lm\u0131\u015f Do\u011frusal Cebir, kaynak kodunu de\u011fi\u015ftirmeden TensorFlow modellerini h\u0131zland\u0131rabilen, do\u011frusal cebir i\u00e7in alana \u00f6zg\u00fc bir derleyicidir) gibi teknikler kullan\u0131r.<\/p>\n\u00d6zellikler:<\/h5>\n
- \n
- Duyarl\u0131 yap\u0131: Numpy veya SciKit ile m\u00fcmk\u00fcn olmayan grafi\u011fin herhangi bir b\u00f6l\u00fcm\u00fcn\u00fc TensorFlow ile kolayca g\u00f6rselle\u015ftirebiliriz.<\/li>\n
- \u00c7ok Y\u00f6nl\u00fc: Tensorflow'un en \u00f6nemli \u00f6zelliklerinden biri, makine \u00f6\u011frenimi modellerine g\u00f6re performans\u0131nda esnek olmas\u0131d\u0131r, yani mod\u00fclerdir ve par\u00e7alar\u0131n\u0131 kendiniz in\u015fa etmenize olanak tan\u0131r.<\/li>\n
- TensorFlow'da makine \u00f6\u011frenimi modellerini e\u011fitmek basittir: Makine \u00f6\u011frenimi modelleri, da\u011f\u0131t\u0131lm\u0131\u015f bilgi i\u015flem i\u00e7in CPU'lar ve GPU'lar \u00fczerinde TensorFlow kullan\u0131larak kolayca e\u011fitilebilir.<\/li>\n
- Paralel sinir a\u011f\u0131 e\u011fitimi: TensorFlow, birden fazla sinir a\u011f\u0131n\u0131 ve GPU'yu ayn\u0131 anda e\u011fitmenize olanak tan\u0131r.<\/li>\n
- A\u00e7\u0131k kaynak ve geni\u015f bir topluluk: \u015e\u00fcphesiz, Google taraf\u0131ndan geli\u015ftiriliyorsa, s\u00fcrekli kararl\u0131l\u0131k iyile\u015ftirmeleri \u00fczerinde \u00e7al\u0131\u015fan \u00f6nemli bir yaz\u0131l\u0131m uzmanlar\u0131 ekibi zaten mevcut. Bu makine \u00f6\u011frenimi k\u00fct\u00fcphanesinin en iyi yan\u0131, a\u00e7\u0131k kaynakl\u0131 olmas\u0131, yani internet eri\u015fimi olan herkesin kullanabilmesi.<\/li>\n<\/ol>\n
5. Scikit Learn<\/h2>\n
\nScikit Learn, Python ortam\u0131nda \u00e7al\u0131\u015fan makine \u00f6\u011frenimi algoritmalar\u0131 i\u00e7in a\u00e7\u0131k kaynakl\u0131 bir k\u00fct\u00fcphanedir. Denetimli ve denetimsiz \u00f6\u011frenme algoritmalar\u0131yla kullan\u0131labilir. K\u00fct\u00fcphane, pop\u00fcler algoritmalar\u0131n yan\u0131 s\u0131ra NumPy, Matplotlib ve SciPy paketlerini de i\u00e7erir. Scikit Learn, en \u00e7ok Spotify'da m\u00fczik \u00f6nerilerinde kullan\u0131lmas\u0131yla bilinir. \u015eimdi Scikit Learn'\u00fcn baz\u0131 temel \u00f6zelliklerini derinlemesine inceleyelim:<\/p>\n- \n
- \u00c7apraz Do\u011frulama: Scikit Learn ile denetlenen modellerin daha \u00f6nce g\u00f6r\u00fclmemi\u015f veriler \u00fczerindeki do\u011frulu\u011funu kontrol etmek i\u00e7in \u00e7e\u015fitli y\u00f6ntemler mevcuttur; \u00f6rne\u011fin train_test_split y\u00f6ntemi, cross_val_score vb.<\/li>\n
- G\u00f6zetimsiz \u00f6\u011frenme teknikleri: K\u00fcmeleme, fakt\u00f6r analizi, temel bile\u015fen analizi ve g\u00f6zetimsiz sinir a\u011flar\u0131 gibi \u00e7ok \u00e7e\u015fitli g\u00f6zetimsiz \u00f6\u011frenme algoritmalar\u0131 mevcuttur.<\/li>\n
- \u00d6zellik \u00e7\u0131karma: Resimlerden ve metinlerden \u00f6zellik \u00e7\u0131karmak kullan\u0131\u015fl\u0131 bir ara\u00e7t\u0131r (Bag of Words gibi).<\/li>\n<\/ol>\n
6. Eli5<\/h2>\n
<\/p>\n
Makine \u00f6\u011frenimi modellerinin tahminleri genellikle yanl\u0131\u015ft\u0131r ve Eli5 Python makine \u00f6\u011frenimi k\u00fct\u00fcphanesi, bu yanl\u0131\u015f tahmin zorlu\u011funun \u00fcstesinden gelmeye yard\u0131mc\u0131 olur. T\u00fcm makine \u00f6\u011frenimi modellerinin g\u00f6rselle\u015ftirilmesi ve hata ay\u0131klamas\u0131n\u0131n yan\u0131 s\u0131ra, bir algoritman\u0131n t\u00fcm \u00e7al\u0131\u015fma s\u00fcre\u00e7lerini izler. Eli5 taraf\u0131ndan desteklenen k\u00fct\u00fcphanelerden baz\u0131lar\u0131 XGBoost, lightning, scikit-learn ve sklearn crfsuite'dir.<\/p>\n
\u015eimdi Eli5'in baz\u0131 uygulamalar\u0131ndan bahsedelim:<\/h5>\n
- \n
- ELI5, makine \u00f6\u011frenimi s\u0131n\u0131fland\u0131r\u0131c\u0131lar\u0131n\u0131 incelemek ve tahminlerini a\u00e7\u0131klamak i\u00e7in kullan\u0131lan bir Python paketidir. Genellikle sklearn regres\u00f6rleri ve XGBoost, CatBoost, Keras vb. s\u0131n\u0131fland\u0131r\u0131c\u0131lar gibi algoritmalarda hata ay\u0131klamak i\u00e7in kullan\u0131l\u0131r.<\/li>\n
- Di\u011fer Python paketleriyle ba\u011f\u0131ml\u0131l\u0131klar\u0131n oldu\u011fu durumlarda Eli5 olmazsa olmazd\u0131r.<\/li>\n
- Eli5, eski yaz\u0131l\u0131mlar\u0131n ve yenilik\u00e7i yakla\u015f\u0131mlar\u0131n uyguland\u0131\u011f\u0131 \u00e7e\u015fitli sekt\u00f6rlerde de kullan\u0131lmaktad\u0131r.<\/li>\n<\/ol>\n
7. SciPy<\/h2>\n
\nScipy, bilimsel hesaplama, veri i\u015fleme ve y\u00fcksek performansl\u0131 hesaplama i\u00e7in kullan\u0131lan \u00fccretsiz ve a\u00e7\u0131k kaynakl\u0131 bir Python k\u00fct\u00fcphanesidir. K\u00fct\u00fcphane, h\u0131zl\u0131 hesaplama i\u00e7in \u00e7ok say\u0131da kullan\u0131c\u0131 dostu rutin i\u00e7erir. Veri i\u015fleme ve g\u00f6rselle\u015ftirmenin yan\u0131 s\u0131ra \u00fcst d\u00fczey komutlara da olanak tan\u0131yan NumPy uzant\u0131s\u0131na dayan\u0131r. Scipy, matematiksel hesaplamalar i\u00e7in NumPy ile birlikte kullan\u0131l\u0131r. NumPy, dizi verilerinin s\u0131ralanmas\u0131n\u0131 ve indekslenmesini sa\u011flarken, SciPy say\u0131sal kodu depolar. K\u00fcme, sabit, fftpack, birle\u015ftirme, enterpolasyon, io, linalg, ndimage, odr, optimize, sinyal, seyrek, uzamsal, \u00f6z ve istatistik, SciPy'de bulunan \u00e7ok say\u0131da alt paketten sadece birka\u00e7\u0131d\u0131r. Bunlar\u0131 SciPy'den i\u00e7e aktarmak i\u00e7in "from scipy import subpackage-name" komutu kullan\u0131labilir. Ancak NumPy, SciPy k\u00fct\u00fcphanesi, Matplotlib, IPython, Sympy ve Pandas temel SciPy paketleridir.<\/span><\/p>\n\u00d6zellikler:<\/h5>\n
- \n
- SciPy'nin en \u00f6nemli \u00f6zelli\u011fi NumPy'de yaz\u0131lm\u0131\u015f olmas\u0131 ve dizilerinin NumPy'yi yo\u011fun bir \u015fekilde kullanmas\u0131d\u0131r.<\/li>\n
- SciPy, optimizasyon, say\u0131sal entegrasyon ve daha bir\u00e7oklar\u0131 gibi t\u00fcm etkili say\u0131sal algoritmalar\u0131 sa\u011flamak i\u00e7in \u00f6zel alt mod\u00fcllerini kullan\u0131r.<\/li>\n
- T\u00fcm fonksiyonlar SciPy mod\u00fclleri alt\u0131nda kapsaml\u0131 bir \u015fekilde belgelenmi\u015ftir. SciPy'nin temel veri yap\u0131s\u0131 NumPy dizileridir ve \u00e7e\u015fitli pop\u00fcler bilimsel programlama uygulamalar\u0131 i\u00e7in mod\u00fcller i\u00e7erir. SciPy, do\u011frusal cebir, integral alma (kalk\u00fcl\u00fcs), adi diferansiyel denklemleri \u00e7\u00f6zme ve sinyal i\u015fleme gibi g\u00f6revleri kolayca halleder.<\/li>\n<\/ol>\n
8. PyTorch<\/h3>\n
\nPyTorch, Facebook taraf\u0131ndan ilk kez 2017 y\u0131l\u0131nda tan\u0131t\u0131lan ve iki \u00fcst d\u00fczey yetene\u011fi bir araya getiren bir Python k\u00fct\u00fcphanesidir: \u00f6nemli \u00f6l\u00e7\u00fcde GPU h\u0131zland\u0131rmal\u0131 tens\u00f6r hesaplamas\u0131 (NumPy'ye benzer) ve esneklik ve h\u0131z sunan derin sinir a\u011f\u0131 tabanl\u0131 platformlar.<\/p>\n\u00d6zellikler:<\/h5>\n
- \n
- Python ve k\u00fct\u00fcphaneleri PyTorch taraf\u0131ndan desteklenmektedir.<\/li>\n
- Facebook'un derin \u00f6\u011frenme gereksinimleri bu teknolojinin kullan\u0131lmas\u0131n\u0131 zorunlu hale getirdi.<\/li>\n
- Kullan\u0131labilirli\u011fi ve anla\u015f\u0131l\u0131rl\u0131\u011f\u0131 art\u0131ran, kullan\u0131m\u0131 kolay bir API sa\u011flar.<\/li>\n
- PyTorch'ta kod y\u00fcr\u00fctme s\u0131ras\u0131nda herhangi bir noktada grafikler dinamik olarak ayarlanabilir ve hesaplanabilir.<\/li>\n
- PyTorch'ta kodlama basittir ve i\u015flem h\u0131zl\u0131d\u0131r.<\/li>\n
- CUDA (CUDA, yaz\u0131l\u0131mlar\u0131n genel ama\u00e7l\u0131 i\u015flemler i\u00e7in belirli t\u00fcrdeki grafik i\u015fleme birimlerini kullanmas\u0131na olanak tan\u0131yan paralel bir hesaplama platformu ve uygulama programlama aray\u00fcz\u00fcd\u00fcr; bu yakla\u015f\u0131ma GPU'larda genel ama\u00e7l\u0131 hesaplama denir) desteklendi\u011finden, GPU makinelerinde \u00e7al\u0131\u015ft\u0131r\u0131labilir.<\/li>\n<\/ol>\n
9. LightGBM<\/h3>\n
\nGradient Boosting, geli\u015ftiricilerin karar a\u011fa\u00e7lar\u0131 gibi basit modelleri yeniden tan\u0131mlayarak yeni algoritmalar geli\u015ftirmelerine yard\u0131mc\u0131 olan \u00f6nemli bir makine \u00f6\u011frenimi paketidir. Sonu\u00e7 olarak, bu gradyan art\u0131rma y\u00f6ntemini h\u0131zl\u0131 ve verimli bir \u015fekilde uygulamak i\u00e7in kullan\u0131labilecek \u00f6zel k\u00fct\u00fcphaneler mevcuttur. S\u00f6z konusu k\u00fct\u00fcphaneler LightGBM, XGBoost ve CatBoost'tur. Bu k\u00fct\u00fcphanelerin t\u00fcm\u00fc, bir problemi \u00e7\u00f6zmeye yard\u0131mc\u0131 olan ve neredeyse ayn\u0131 \u015fekilde kullan\u0131labilen rakiplerdir.<\/p>\n\u00d6zellikler:<\/h5>\n
- \n
- \u00c7ok h\u0131zl\u0131 hesaplamalar ile y\u00fcksek \u00fcretim verimlili\u011fi garanti alt\u0131na al\u0131nm\u0131\u015ft\u0131r.<\/li>\n
- Sezgisel oldu\u011fu i\u00e7in kullan\u0131c\u0131 dostudur.<\/li>\n
- Bir\u00e7ok derin \u00f6\u011frenme k\u00fct\u00fcphanesinin e\u011fitilmesi bu k\u00fct\u00fcphaneden daha uzun s\u00fcrer.<\/li>\n
- NaN de\u011ferleri ve di\u011fer kanonik de\u011ferlerin kullan\u0131lmas\u0131 durumunda herhangi bir hata olu\u015fmayacakt\u0131r.<\/li>\n<\/ol>\n
10. Theano<\/h2>\n
\nTheano, di\u011fer matematik k\u00fct\u00fcphaneleri gibi, kullan\u0131c\u0131lar\u0131n matematiksel ifadeleri tan\u0131mlamas\u0131na, optimize etmesine ve de\u011ferlendirmesine olanak tan\u0131r. Verimli matematiksel i\u015flem i\u00e7in devasa \u00e7ok boyutlu diziler kullan\u0131r. Standart C tabanl\u0131 kod, b\u00fcy\u00fck miktarda veriyle \u00e7al\u0131\u015f\u0131rken yava\u015flar. \u00d6te yandan Theano, zengin k\u00fct\u00fcphanesi sayesinde h\u0131zl\u0131 kod y\u00fcr\u00fctme olana\u011f\u0131 sunar. Ge\u00e7ersiz ifadeler tespit edilip de\u011ferlendirilebildi\u011finden, k\u00fct\u00fcphane NumPy'den daha kullan\u0131\u015fl\u0131d\u0131r.<\/p>\n\u00d6zellikler:<\/h5>\n
- \n
- PyName Entegrasyonu: Theano, Theano uyumlu fonksiyonlarda NumPy dizilerini tam olarak kullanabilir.<\/li>\n
- \u015eeffaf GPU kullan\u0131m\u0131: Veri yo\u011fun i\u015flemleri CPU'dan \u00e7ok daha h\u0131zl\u0131 ger\u00e7ekle\u015ftirmek i\u00e7in kullan\u0131labilir.<\/li>\n
- Verimli sembolik t\u00fcrevleme: Theano, verimli sembolik t\u00fcrevleme kullanarak bir veya daha fazla girdiye sahip fonksiyonlar i\u00e7in t\u00fcrevler ger\u00e7ekle\u015ftirir.<\/li>\n
- H\u0131z ve kararl\u0131l\u0131k i\u00e7in optimize edildi: x \u00e7ok k\u00fc\u00e7\u00fck olsa bile log(1+x) i\u00e7in do\u011fru \u00e7\u00f6z\u00fcm\u00fc bulmak gibi problemlerde Theano iyi \u00e7al\u0131\u015f\u0131r. Bu, Theano'nun kararl\u0131l\u0131\u011f\u0131n\u0131n bir\u00e7ok kan\u0131t\u0131ndan sadece biri.<\/li>\n
- Dinamik C kodu \u00fcretin: Theano ifadeleri h\u0131zl\u0131 bir \u015fekilde de\u011ferlendirebilir ve bu da verimlilikte \u00f6nemli bir art\u0131\u015fa yol a\u00e7ar.<\/li>\n
- Kapsaml\u0131 birim testi ve otomatik do\u011frulama: Theano, kapsaml\u0131 birim testi ve otomatik do\u011frulama ile modeldeki birden fazla sorun ve belirsizli\u011fi belirlemeye ve te\u015fhis etmeye yard\u0131mc\u0131 olabilir.<\/li>\n<\/ol>\n
Sonu\u00e7<\/h2>\n
Kullan\u0131m\u0131 kolay Python programlama dili, \u00e7e\u015fitli ger\u00e7ek d\u00fcnya uygulamalar\u0131nda geni\u015f bir uygulama alan\u0131 bulmu\u015ftur. \u00dcst d\u00fczey, dinamik olarak yaz\u0131lm\u0131\u015f ve yorumlanm\u0131\u015f bir dil olmas\u0131 nedeniyle hata ay\u0131klama alanlar\u0131nda h\u0131zla yayg\u0131nla\u015fmaktad\u0131r. Python, YouTube, Dropbox vb. k\u00fcresel uygulamalarda giderek daha fazla kullan\u0131lmaktad\u0131r. Dahas\u0131, Python k\u00fct\u00fcphanelerinin kullan\u0131labilirli\u011fi sayesinde kullan\u0131c\u0131lar kendi kodlar\u0131n\u0131 yazmak zorunda kalmadan \u00e7e\u015fitli g\u00f6revleri ger\u00e7ekle\u015ftirebilirler. Bu nedenle, Python ve k\u00fct\u00fcphaneleri hakk\u0131nda bilgi edinmek g\u00fcn\u00fcm\u00fczde her gen\u00e7 yetenek i\u00e7in \u00e7ok \u00f6nemlidir. Python'\u0131n veri bilimi, makine \u00f6\u011frenimi, yaz\u0131l\u0131m m\u00fchendisli\u011fi vb. gibi \u00e7e\u015fitli alanlarda kullan\u0131m\u0131, onu kesinlikle gelece\u011fin dili haline getirmektedir.<\/p>","protected":false},"excerpt":{"rendered":"Giri\u015f Teknolojinin hayat\u0131m\u0131z\u0131n her alan\u0131nda giderek daha \u00f6nemli bir rol oynad\u0131\u011f\u0131 g\u00fcn\u00fcm\u00fcz d\u00fcnyas\u0131nda,\u2026","protected":false},"author":1,"featured_media":14604,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_yoast_wpseo_focuskw":"\u0628\u0647\u062a\u0631\u06cc\u0646 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0647\u0627\u06cc \u067e\u0627\u06cc\u062a\u0648\u0646","_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","_yoast_wpseo_canonical":"","_yoast_wpseo_opengraph-description":"","_yoast_wpseo_opengraph-image":"","_yoast_wpseo_twitter-description":"","_yoast_wpseo_twitter-image":"","_yoast_wpseo_focuskeywords":"","_yoast_wpseo_primary_category":"193","footnotes":""},"categories":[193,363],"tags":[377,376,366,360],"class_list":{"0":"post-14603","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-tutorials","8":"category-programming","9":"tag-library","10":"tag-python","11":"tag-366","12":"tag-360"},"yoast_head":"\n
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