I am an associate professor of Computer Engineering at Koç University in Istanbul working at the Artificial Intelligence Laboratory. Previously I was at the MIT AI Lab and later co-founded Inquira, Inc. My research is in natural language processing and machine learning. For prospective students here are some research topics, papers, classes, blog posts and past students.
Koç Üniversitesi Bilgisayar Mühendisliği Bölümü'nde öğretim üyesiyim ve Yapay Zeka Laboratuarı'nda çalışıyorum. Bundan önce MIT Yapay Zeka Laboratuarı'nda çalıştım ve Inquira, Inc. şirketini kurdum. Araştırma konularım doğal dil işleme ve yapay öğrenmedir. İlgilenen öğrenciler için araştırma konuları, makaleler, verdiğim dersler, Türkçe yazılarım, ve mezunlarımız.

May 30, 2018

Knet-the-Julia-dope: An interactive book on deep learning.

Written by Manuel Antonio Morales (@moralesq). This repo is the Julia translation of the mxnet-the-straight-dope repo, a collection of notebooks designed to teach deep learning, MXNet, and the gluon interface. This project grew out of the MIT course 6.338 Modern Numerical Computing with Julia taught by professor Alan Edelman. Our main objectives are:
  • Introduce the Julia language and its main packages in the context of deep learning
  • Introduce Julia's package Knet: an alternative/complementary option to MXNet
  • Leverage the strengths of Jupyter notebooks to present prose, graphics, equations, and code together in one place

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May 29, 2018

Wasserstein GAN: a Julia/Knet implementation

Written by Cem Eteke (@ceteke). This repository contains implementation of WGAN and DCGAN in Julia using Knet. Here is a detailed report about WGAN.
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May 28, 2018

Relational networks: a Julia/Knet implementation

Written by Erenay Dayanık (@ereday). Knet implementation of "A simple neural network module for relational reasoning" by Santoro et al. (2017). (Relational Networks, arXiv:1706.01427, blog post)
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May 27, 2018

Neural Style Transfer: a Julia notebook

Written by Cemil Cengiz (@cemilcengiz).

This notebook implements deep CNN based image style transfer algorithm from "Image Style Transfer Using Convolutional Neural Networks" (Gatys et al., CVPR 2016). The proposed technique takes two images as input, i.e. a content image (generally a photograph) and a style image (generally an artwork painting). Then, it produces an output image such that the content(objects in the image) resembles the "content image" whereas the style i.e. the texture is similar to the "style image". In order words, it re-draws the "content image" using the artistic style of the "style image".

The images below show an original photograph followed by two different styles applied by the network.

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