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Assessing your cultural and operational fit
ChessBoard
July 28, 2017 – July 28, 2017
A young-simple-naive classifier of chess board image.
View ProjectC51DQN
July 27, 2017 – August 25, 2017
A TensorFlow implementation of DeepMind's A Distributional Perspective on Reinforcement Learning.(C51-DQN)
View ProjectA3C
July 23, 2017 – November 9, 2017
Tensorflow implementation of A3C, both discrete & continuous action space.
View ProjectNoisyNetDQN
July 19, 2017 – August 2, 2017
Tensorflow implementation for "Noisy network for exploration"
View ProjectStyleTransfer
July 7, 2017 – July 26, 2017
An implementation of Neural Style transfer & Fast Style Transfer in python2.7 & Tensorflow1.2.0.
View ProjectDQfD
April 26, 2017 – December 5, 2017
An implement of DQfD(Deep Q-learning from Demonstrations) raised by DeepMind:Learning from Demonstrations for Real World Reinforcement Learning
View ProjectCultural Fit Analysis
The candidate's project portfolio is heavily focused on personal, academic-style AI/ML implementations, which indicates a strong drive for self-learning and technical exploration. However, the lack of team-based projects or professional experience makes it difficult to assess cultural fit for a collaborative backend engineering role. The diversity of languages (Python, Java, C++) is a positive, but the project types are very niche.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. The candidate's profile primarily showcases technical project work without details on collaboration, communication, or work attitude.