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Ahmet Bayrak's Projects

c-data-structure icon c-data-structure

Calculating with hexadecimal and integer numbers using Stack & Queue. Good example for dynamic data structure (stacks and queues) on C programming language.

c-maze-treasure-game icon c-maze-treasure-game

In this experiment, we are expected to gain knowledge on C language including read-write files, arrays, matrices, recursion and dynamic memory allocation.

java-agenda-application icon java-agenda-application

Develop a dedicated Agenda application, like Google Calendar or other popular calendar applications. Exercise of Interface, Collections, Exceptions

java-pharmacy-stock-management icon java-pharmacy-stock-management

Program reads prescriptions and price list from input files and calculates total cost of a prescription based on the price list.

java-salary-calculate-with-inheritance icon java-salary-calculate-with-inheritance

Program reads working hours from text file and calculates total salary this month. Good exercise for inheritance mechanism which is an important property of object-oriented programming.

nlp-hidden-markov-model-viterbi-algorithm icon nlp-hidden-markov-model-viterbi-algorithm

This project includes implementing a NER tagger using Hidden Markov Models (HMMs). For predicting the NER tags, program uses Viterbi Algorithm. The dataset is a CoNLL 2003 NER dataset that is composed of sentences with words per line 5 possible labels : “LOC, ORG, MISC, PER, O”, that represent the NER classes. The “LOC, ORG, MISC, PER, O” labels are abbreviation of “LOCATION, ORGANIZATI ̇ON, MISCELLANEOUS, PERSON, OTHER” respectively.

nlp-recurrent-neural-networks- icon nlp-recurrent-neural-networks-

In this assignment, we were expected to implement a text generation model using deep learning models (bigram level Feed-Forward Neural Network). We use DyNet deep learning library for implementing FFNN and GloVe 6B word-embeddings for the pretrained word vectors.

nlp-unigram-bigram-trigram-generate-sentence icon nlp-unigram-bigram-trigram-generate-sentence

This project includes building unigram, bigram and trigram models and then generating sentences with these models. Finally perplexity, sentence probability and smoothed sentence probability of generated sentences will be calculated. The dataset is released by Facebook that is used to train artificial intelligence software to understand childrens stories and predict the word that was missing from a given sentence in a story.

react-native-lawyerapp icon react-native-lawyerapp

Avukatların dava alabileceği ya da davalarını devredebileceği bir mobil uygulama. (henüz tam olarak tamamlanmadı)

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