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🌱 I am Research Scholar and currenlty working as Data Scientist at Elevate Services, Chandigarh. I'm passionate about Artificial Intelligence, Deep Learning, Machine Learning, Computer Vision and Natural Language Processing. I create machine learning systems and apply ML algorithms to generate accurate predictions to resolve business problems. I have developed an BERT architecture using the CUAD model for the Question and Answer from the Legal Document Contract and Agreements.

🔭 I have previously worked in the fields of Signal and Thermal Image Processing, Non-stationary Thermal Imaging, Object Classification and Pattern Recognition. I have developed a feature extraction approach based on unsupervised/supervised learning i.e., Independent Component Analysis (ICA), Principal Component Analysis (PCA) and Artificial Neural Network (ANN) that identify the hidden abnormality and patterns inhibited inside the steel structure using thermal images. I also evaluated the 95% lower confidence score and 90% probability of detecting the abnormality/defects using for each methods. I have published the carried in 5 reputed international journal like in IEEE Sensor, Elsevier, and IET tha have recived 45 citations at Google Scholar.

🎓 I have completed PhD in Engineering Sciences on the topic "Non-Stationary Thermal Wave Imaging for Subsurface Defect Detection in Metals" from the Academy of Scientific and Innovative Research, AcSIR, under the supervision of Prof. H. K. Sardana, Chief Scientist at CSIR-CSIO Chandigarh and Dr. Ravibabu Mulaveesala, Associate Professor at IIT Delhi in 2021. I received my Masters in Advanced Instrumentation Engineering with a specialization in Machine Learning and Deep Learning from the AcSIR in 2016 and completed B.Tech in Electronics and Instrumentation Engineering from MJPRU Bareilly in 2013.

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Javed Ahmad's Projects

coursemldl icon coursemldl

A series of interactive labs we prepared for the Introduction into Machine Learning and Deep Learning course. The content of the series is based on Python, IPython Notebook, and PyTorch.

deepai icon deepai

Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

getting-things-done-with-pytorch icon getting-things-done-with-pytorch

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER

insurance-claim-prediction icon insurance-claim-prediction

In this Data set we are Predicting the Insurance Claim by each user, Machine Learning algorithms for Regression analysis are used and Data Visualization are also performed to support Analysis.

keras icon keras

Deep Learning library for Python. Runs on TensorFlow, Theano, or CNTK.

machine-learning-with-python icon machine-learning-with-python

Small scale machine learning projects to understand the core concepts . Give a Star 🌟If it helps you. BONUS: Interview Bank coming up..!

seaborn icon seaborn

This is the Seaborn cheat sheet I made to go along with my Seaborn Tutorial Series

youtubeli icon youtubeli

Github repo to upload demo files of youtube videos and linkedin

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