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Görüntü İşleme Öğrenme Planı - Coggle Diagram
Görüntü İşleme Öğrenme Planı
Hazırlanma
Programlama
Github
Linux
https://careerkarma.com/blog/how-to-learn-linux/
Algoritma
Doğrusal Cebir
Essence of Linear Algebra - 3Blue1Brown
Elementary Linear Algebra - Howard Anton (beginner)
Matrix Analysis - Carl D. Meyer (advanced)
İstatistik
Statistics (Freedman, Pisani & Purves) is hands down the best introductory book for statistical thinking. There's almost no math in here, but reading this and doing the exercises will force you to engage with the material.
Probability and Statistics for Engineering and the Sciences (Devore) is a perfectly good introduction to basic applied statistics.
Linear Regression Analysis (Seber & Lee) is a nice introduction to linear regression for someone with a strong background in linear algebra.
Bayesian Data Analysis (Gelman, Carlin, Dunson, Vehtari & Rubin) is the only reasonable choice for starting out with applied Bayesian methods.
Data Analysis Using Regression and Multilevel/Hierarchical Models (Gelman & Hill) is a fantastic introduction to very important class of regression models. This one is accessible to a wide audience.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Hastie, Tibshirani & Friedman) is a classical text on machine learning methods. I'm not going to try to give a comprehensive list of books on ML, but I wouldn't feel right completely leaving it out, and this is still the standard by which other books are judged.
Temel
Görüntü İşleme
Digital Image Processing - Gonzalez
Fundamentals of Digital Image Processing - Anil K. Jain
Multiple View Geometry - Hartley
Computer Vision : Algorithms and Applications - Richard Szeleski
İleri
Makine Öğrenmesi
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Yapay Sinir Ağları
https://qr.ae/pvPtOd
Nesne Tanıma