PYTHON İLE MAKİNE ÖĞRENİMİ VE DERİN ÖĞRENME (Machine Learning & Deep Learning via Python)
QAR 59
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- Python ile Makine Öğrenimi ve Derin Öğrenme kitabının başlangıcında Python ile program geliştirme sürecinin öğretilmesi anlatılmaktadır. Ardından yapay zeka ve makine öğreniminin temel kavramları tanıtılarak gradyan iniş optimizasyon algoritması, doğrusal regresyon modeli, doğrusal sınıflandırma modeli ve algılayıcı modeli gibi en temel makine öğrenimi modelleri ilginç uygulama örnekleriyle açıklanmaktadır. Sonrasında, sinir ağı modeli ve ilgili eğitim algoritmalarına tanıtılmaktadır. Okuyucuların sinir ağı modelini hızlı bir şekilde tanıyabilmeleri için önce algılayıcı sinir ağı modeli kolay anlaşılır bir dille anlatılmakta, ardından sinir ağı modelinin temel bileşenleri bir araya getirilerek kapsamlı bir açıklama sunulmaktadır. Kitap, kod yazma sürecinden başlayarak sinir ağı modellerinin eğitim adımları ve eğitim algoritmalarını derinlemesine incelemekte ve hiperparametre ayarlama, model testi gibi temel teknikleri mizahi bir dille analiz ederek sunmaktadır. Yeni başlayanlar için sağlam bir derin öğrenme temeli oluşturmayı hedefleyen kitap, aşırı uyum sorunu ve düzenlileştirme stratejilerinin yanı sıra aktivasyon fonksiyonlarının tasarım fikirlerine de odaklanmakta; programlama temelli bir yaklaşımla derin öğrenmenin temel ilkelerini ve modellerini anlaşılır bir şekilde tanıtmaktadır. Daha fazla göster
| Yayıncı | Nobel Akademik Yayıncılık |
| Yayınlanma Tarihi | 13 Şubat 2025 |
| Baskı | 1. |
| Dil | Türkçe |
| Baskı Uzunluğu | 330 sayfa |
| ISBN-10 | 6253860011 |
| ISBN-13 | 978-6253860011 |
| Boyutlar | 4 x 19 x 27 cm |
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Ali Şir Attila Format: Kağıt Kapak Editorial Review
PYTHON İLE MAKİNE ÖĞRENİMİ VE DERİN ÖĞRENME (Machine Learning & Deep Learning via Python) is a comprehensive resource published by Nobel Akademik Yayıncılık, designed for Turkish-speaking audiences. Spanning 330 pages, this book serves as a rich guide for tackling the complexities of machine learning and deep learning using Python. With its detailed explanations and practical examples, readers will be well-equipped to navigate the intricacies of these advanced topics. The first edition, released on February 13, 2025, not only appeals to learners but also professionals looking to enhance their skills in a rapidly evolving field.
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Pros
- Comprehensive content on machine learning concepts
- Practical examples for better understanding
- Well-structured for Turkish-speaking readers
- Published by a reputable academic publisher
- Lengthy yet concise coverage of topics
Cons
- Only available in Turkish language
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