Name: Andac Demir
Type: User
Company: PhD Candidate at Northeastern University
Bio: Ph.D. Northeastern University (Deep Learning, Signal Processing) ·
B.S. Tufts University '17 - Electrical Engineering ·
Robert College '13
Location: Boston, Massachusetts
Blog: andacdemir.com/
Andac Demir's Projects
It predicts the next word based on the last a few characters to expedite the typing process as well as autocorrecting the misspelled words. Trained with an LSTM model.
List of AI Residency Programs
Object Detection with Faster R-CNN in Chainer
Predicts the Bitcoin, Ethereum, IOTA and Tron prices.
Image restoration with neural networks but without learning.
Creates a program that finds the shortest path through a graph using its edges.
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
--Undisclosed--
This code completes a tutorial about gaussian mixture models (gmm) in python using scikit-learn
Keras implementations of Generative Adversarial Networks.
Residual networks implementation using Keras-1.0 functional API
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
MixedRealityToolkit-Unity uses code from the base MixedRealityToolkit repository and makes it easier to consume in Unity.
This repository consists of implementations of Levinson Algorithm, Reverse Levinson Algorithm and Schur Algorithm
A multilayer perceptron to classify MNIST dataset.
TensorFlow (Python API) implementation of Neural Style
Automatically buy a Nike Shoe/Sneaker that is expected to come out, given a Nike Store account.
Working out at the (OpenAI) gym
PyTorch training/tool code for Polygon-RNN++ (CVPR 2018)
PyTorch implementations of Generative Adversarial Networks.
Ocular Artifacts Removal in EEG Signals Using Extended-Infomax-ICA
tensorboard for pytorch (and chainer, mxnet, numpy, ...)
A list of all named GANs!
Removing the large TMS artifacts that cover on the EEG signals.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.