Automatic Differentiation

Automatic Differentiation

As the number of computer programs that need the gradient of functions increase, (such as machine learning programs that use gradient descent algorithms) efficient derivation of gradients problem has risen. Many machine learning / deep learning algorithms use derivations at each step. So the efficiency of the derivation matters a lot for those programs. There are three main ways to differentiate a function. On this project, we implemented automatic differentiation which is the most suitable one of these methods for our purpose of use.

Project Poster: 

Project Members: 

Beyza Gül Gürbüz
Esra Aydemir

Project Advisor: 

Ali Taylan Cemgil

Project Status: 

Project Year: 

  • Spring

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Department of Computer Engineering, Boğaziçi University,
34342 Bebek, Istanbul, Turkey

  • Phone: +90 212 359 45 23/24
  • Fax: +90 212 2872461

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