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afaq-ahmad/

Hi there šŸŒ

Iā€™m a highly motivated Data Scientist/computer vision Engineer with over two years of research and development experience and currently working on building AI and computer vision technologies that have impact on the "real world". I have worked on Multiple Projects like Document Digitalization, Surveillance System and text analytics. Moreover I have also experience with edge computing and solution deployment on hardware accelerators like Jetson and Corel boards. I have done my Master in Robotics and Intelligent machines and bachelor in Electrical Engineering. Other than my professional time spending, I like Travelling, Gardening and Gaming.

  • šŸ’¬ Feel free to ask me about Deep Learning, Python, Tensorflow / Keras, Linux, Machine Learning, Php, NLP (Bert, Albert and feedback based learning mechanisms).
  • šŸ“– Learning about Machine Learning, Computer Vision and Psychology

šŸ”­ I have Experience in:

python tensorflow keras scikit-learn opencv jupyter-notebook aws azure matlab qt raspberry-pi atom ubuntu linux

šŸŒ± Iā€™m currently learning:

django r sql react kubernetes docker electron

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šŸ“« How to reach me:

gmail linkedin

Afaq Ahmad's Projects

docbank icon docbank

DocBank: A Benchmark Dataset for Document Layout Analysis

horse-and-field-detection-based-on-color-and-motion icon horse-and-field-detection-based-on-color-and-motion

The main task of this project is to detect the horse based on computer vision techniques. We have used different methods to find where the horse in field and finding the boundary of of barn area. The steps that are used in the code are explained below:

hotel-booking-demand-case-study icon hotel-booking-demand-case-study

This notebook contains a series of exercises designed to explore a range of data science, python scripting, and quantitative reasoning skills. You can, in principle. solve these exercises using a number of different programming languages/environments, but it will likely be easiest for you to simply fill out this notebook with your solutions in the relevant sections following each exercise.

k-nearest-neighbors-classifier-for-letters-concept-ocr icon k-nearest-neighbors-classifier-for-letters-concept-ocr

k-Nearest Neighbors Classifier has been used because of its simplicity, fastness and efficiency. The problem with Neural Network based models DNN or CNN required a lot of data, but in out cases we have limited amount of data just around 3 thousand images of characters

kalman-and-bayesian-filters-in-python icon kalman-and-bayesian-filters-in-python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

mediapipe icon mediapipe

Cross-platform, customizable ML solutions for live and streaming media.

miles-deep icon miles-deep

Deep Learning Porn Video Classifier with tf.keras

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