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Deep Learning Engineer

Location:
Cincinnati, Ohio, United States
Salary:
110,000
Posted:
September 24, 2019

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Resume:

Adarsh Chitradurga

Achutha

Machine learning engineer currently working on ML and DL frameworks for

classifying sonar data for scene recognition. Have worked in Data analytics in projects involving data collection, data analysis, conjoint analysis, Forecasting and pricing strategies

adafrn@r.postjobfree.com

513-***-****

222 Senator Place, Apt-26, Cincinnati, USA

linkedin.com/in/adarsh-chitradurga-achutha-56986093 EDUCATION

08/2016 – Present

Master of Science in Mechanical Engineering

University of Cincinnati

08/2008 – 06/2012

Bachelor of Technology

Indian Institute of Technology Roorkee

WORK EXPERIENCE

Graduate Student Researcher

University of Cincinnati

Currently working on implementing an efficient deep neural network framework to effectively classify sonar data for scene recognition.

Contact: Dr. Vanderlest Dieter – adafrn@r.postjobfree.com 04/2016 – 08/2016

Senior Business Analyst

Merilytics

Worked in a team to develop and monitor a pricing strategy for a major food company in the United States

Developed a model for selecting a suitable location for customer support center by using various parameters and by using only the data provided by US national surveys and public organizations. 08/2014 – 09/2015

Business Analyst

ZS Associates

Worked on Market research studies from developing the survey and data collection on Confirmit, Run market share, segmentation and conjoint analysis studies based on the data

07/2012 – 06/2014

Manager

Reliance Industries Limited

Part of the maintenance team working on various projects like Boiler overhauling, Steam turbine overhauling and balancing. On a day to day basis managed the maintenance of various non critical equipment and material handling and inventory management SKILLS

Deep Learning Machine Learning TensorFlow Keras

Python PCA C# C++ Matlab Fortran

Unity Engine

PROJECTS

Path Planning Algorithms (08/2018 – 11/2018)

Implemented Bug1, Bug2, DistBug and Dynamic programming (Value iteration) as part of the decision engineering. The algorithms were coded from scratch and implemented in MATLAB and simulated for various obstacle scenarios Math Methods in Decision making (08/2018 – 11/2018) Algorithms were implemented as part of small projects like genetic algorithm for TSP, Swarm intelligence, Kalman Filter, Bayesian networks, Ant colony optimization, Particle swarm optimization

CERTIFICATES

Deep Learning Specialization

Coursera

Neural Networks and Deep Learning

Coursera

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

Coursera

Structuring Machine Learning Projects

Coursera

Convolutional Neural Networks

Coursera

Sequence Models

Coursera

Responsibilities

Responsibilities

Responsibilities

Responsibilities



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