METU CENG 2013-Present

CENG 111 Introduction to Computer Engineering

Some intro to ceng/cs stuff written in python

CENG 140 C Programming

Every assignment given in the course

CENG 232 Logic Design

Some verilog stuff

CENG 242 Programming Language Concepts

Many tasks done in prolog, haskell, and c++ to understand concepts in different programming languages.

CENG 280 Formal Languages and Abstract Machines

CENG 315 Algorithms

CENG 331 Computer Organization

Bomb Lab

A task which aims to make people familiar with reverse engineering

Attack Lab

A task which aims to teach people how to smash the stack for fun & profit

Performance Lab

A task which aims to teach optimization methodoligies for x86(64) architecture

CENG 334 Introduction to Operating Systems

HW1

Simple shell for linux.

HW2

Threading mutexes and semaphores practice for linux.

HW3

EXT2 filesystem defragmentation which moves every data block to begining of the disk.

CENG 336 Introduction to Embedded Systems Development

Codes developed to perform various tasks on a PIC18F8722 based board. Both in assembly and C, even a simple RTOS project.

CENG 350 Software Engineering

SRS

SDD

CENG 351 Data Management and File Structures

CENG 384 Signals and Systems for Computer Engineers

MATH 407 Game Theory

CENG 435 Data Communications and Networking

HW1

HW2

CENG 783 Deep Learning

Homeworks and project done during 2016-2017 Spring Semester. No learning framework has been used in neither homeworks nor the project all codes have been written in python from scratch using only numpy, without help of any frameworks.

HW1

k-Nearest Neighbor SVM/Softmax Classifier Fully Connected Networks for regression/classification Image classification Next Character Prediction using Fully Connected Networks

HW2

Denoising Auto Encoders CNNs Saliency Maps Fooling CNNs Deep Dreaming CIFAR-10 Dataset TinyImageNet-100-A

HW3

Vanilla RNNs LSTMs Image Captioning using Microsoft COCO Dataset Next Character Prediction using RNNs and LSTMs

Project

Basic Question Answering using synthetic dataset generated in Turkish. Dataset Generator 3 Different approaches using RNNs and Fully connected layers Comparison of those approaches