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Resume

🚴‍♂️ Introduction

  • I'm Giri, an avid Artificial Intelligence enthusiast with a keen interest in crafting and implementing AI/ML-driven solutions, leveraging the agile and scrum software development life cycle. 😊

  • I hold a Master's degree in Embedded Systems from the Technical University of Chemnitz, where my research focused on Unsupervised Learning and Generative Adversarial Networks. https://github.com/rebelgiri/thesis-domain-adaptation-of-synthetic-document-images-using-neural-networks

  • I am presently employed at Fusionsystems GmbH in Chemnitz, Germany, where my role involves overseeing automotive software development and testing while ensuring compliance with ISO 26262 and ASIL standards. Concurrently, I am engaged in collaborative projects with automotive software companies such as Cariad, FD Tech, and Intenta in the Saxony region.

🚅 Projects and Experiences

  • Predictive Maintenance project using Deep Learning and Vibration data for Condition Monitoring of Industrial Machinary 😍. https://github.com/rebelgiri/predictive-maintenance-using-deep-learning
  • A research project in the area of Multi-Object Multi-Camera Tracking for Pedestrians 😍.
  • Atively contributed to numerous C++ projects with a primary focus on Sensor Data Fusion. One notable example includes utilizing Bayesian Networks to estimate the driving direction of lanes.
  • Extensive work in C and C++ programming for Cross-Platform application development. I possess a strong grasp of the GNU Linux operating system, having served as a Linux System Programmer at renowned companies like Huawei Technologies and UST Global (deputed at Intel).

⛷️ Skills

  • Languages : C, C++ and Python
  • Tools : Git, JIRA and Confluence
  • Frameworks : Google Test, TensorFlow, Keras and PyTorch
  • Libraries : OpenCV, Scikit-learn and NumPy

🧘‍♂️ Professional Interests

  • C++ software development and unit testing using Google Test & gMocks
  • Computer Vision
  • Deep Learning and Machine Learning (PyTorch, TensorFlow and Keras)
  • Algorithms and Data Structures
  • Bayesian Networks and Predictive Filters like Kalman
  • Sensor data fusion

giriraj.pawar2012@gmail.com

LinkedIn

Credits

😊 I have created my profile by referring an article created by Lakshmanan Meiyappan.

Giriraj Pawar's Projects

fashion-mnist icon fashion-mnist

A MNIST-like fashion product database. Benchmark :point_right:

flatbuffers icon flatbuffers

FlatBuffers: Memory Efficient Serialization Library

gcloud icon gcloud

Google Cloud tutorial and setup

googletest icon googletest

GoogleTest - Google Testing and Mocking Framework

hackerrank-solutions icon hackerrank-solutions

hackerrank video tutorial|hackerrank | hackerrank cracking the coding interview | hackerrank data structures | hackerrank java | hackerrank algorithms | hackerrank youtube | hackerrank solution | hackerrank challenge | hackerrank solutions | hackerrank coding challenge | hackerrank problems | hackerrank algorithms solutions | hackerrank cracking the coding interview solutions | hackerrank problem solving | coding interview | coding | programming | JAVAAID | Kanahaiya Gupta | Coding Interview Preparation

handson-ml2 icon handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

i2s-microphone icon i2s-microphone

A collection of documentation and examples for Infineon's I2S microphones.

imageq icon imageq

Qt based image processing application.

imgur5k-handwriting-dataset icon imgur5k-handwriting-dataset

IMGUR5K handwriting set. It is a handwritten in-the-wild dataset, which contains challenging real world handwritten samples from different writers.The dataset is shared as a set of image urls with annotations. This code downloads the images and verifies the hash to the image to avoid data contamination.

jina icon jina

An easier way to build neural search on the cloud

keras-io icon keras-io

Keras documentation, hosted live at keras.io

minisom icon minisom

:red_circle: MiniSom is a minimalistic implementation of the Self Organizing Maps

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