Position: Machine Learning Research Engineer
Category: Python
English level: intermediate
Years of experience: 3

Projects:
1. Developed boots type recognition system using CNN (fine-tuning).
2. Developed sentimental analysis system using LSTM and Convolutional Nets;
3. Developed time-series prediction system using Recurrent Neural Networks (LSTM);
4. Supporting face recognition server;

Completed online courses:
1. Machine learning course  – Stanford University, Andrew Ng, Coursera.
2. Neural networks for machine learning – Toronto University, Geoffrey Hinton, Coursera.
3. CS231n: Convolutional Neural Networks for Visual Recognition – Stanford University, Fei-Fei Li, Andrej Karpathy, Justin Jonson.
Programming languages: Python, Java, C#, C++, C.

Libraries and technologies: Numpy, SciPy, Keras, Pandas, UnityEngine, RegExp, Qt, JSON, XML, Git, Tortoise SVN, ZeroMQ.

Familiar with: OOP, Design Patterns, Caffe, Theano, CUDA, Matlab, SQL, OpenCV, Android SDK, boost.

Deep learning: MLP, CNNs, RNN (LSTM, GRU), Back-propagation algorithm, optimization and regularization algorithms, data augmentationm, backprop to image, etc.

Commercial experience:
Software Engineer - 2 years.
Machine Learning Engineer - 0.5+ years.
My research interests lie mostly in computer vision problem - object recognition, detection, scene understanding, etc. Also, I'm very curious about reinforcement learning fields, robotics and other AI related fields.

Not very interested in projects related to ads.