Skip to content
View sohamk10's full-sized avatar

Block or report sohamk10

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
sohamk10/README.md

Welcome to My GitHub Profile!

  • 👋 Hi, I’m Soham Kalghatgi

  • I am a Mechanical Engineer persuing a Master's in Mechatronics.

  • I am a passionate individual with a strong interest in the field of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, and Cloud Computing. I am constantly exploring new technologies and techniques to expand my knowledge and skills in these areas.

  • Skills: Python, C, C++, MATLAB, Simulink, Linux, Kubernetes, Docker, Continuous Integration (CI), AWS S3.

  • Libraries: numpy, pandas, scikit-learn, matplotlib, seaborn, plotly, keras, tensorflow, torch, PIL, opencv-python, ipywidgets, boto3, kubernetes

Amazon S3

Docker

Git

GitLab

Ubuntu

Python

Kubernetes

Matlab

Repository Highlights

The projects that I have worked on are:

Artificial intelligence

  • Fault Injection in Autonomous Vehicles.
  • Deep Fake detection.
  • Image reconstruction and Anomaly detection.
  • Image Denoising.

Automotive

  • Specially Abled Utility Vehicle (SAUV).
  • SAEINDIA Supra.
  • SAEINDIA Baja.

Contact Me

You can reach me via the following channels:

Popular repositories Loading

  1. Image-reconstruction-and-Anomaly-detection Image-reconstruction-and-Anomaly-detection Public

    CNN autoencoder is trained on the MNIST numbers dataset for image reconstruction. Anomaly detection is carried out by calculating the Z-score. The framework used is Keras.

    Jupyter Notebook 8 2

  2. Deep-Fake-Detection Deep-Fake-Detection Public

    Learning how to distinguish fake content from genuine content with machine learning . The Machine Learning framework used is PyTorch.

    Python 2

  3. Analyzing-the-effects-of-Fault-Injection-into-a-camera-based-autonomous-vehicle-prototype Analyzing-the-effects-of-Fault-Injection-into-a-camera-based-autonomous-vehicle-prototype Public

    Identifying failure modes of vehicle cameras in the domain of autonomous driving (ADAS) and, designing a Fault Injection Module (FIM) to inject these faults through image processing into the autono…

    Jupyter Notebook 2

  4. sohamk10 sohamk10 Public

    Config files for my GitHub profile.

  5. Image-denoising Image-denoising Public

    DnCNN model trained by residual learning formulation to recover a clean image x from a noisy observation y. The noisy observation y is a combination of a clean image x and residual image v. y = x +…

  6. Specially-Abled-Utility-vehicle-SAUV Specially-Abled-Utility-vehicle-SAUV Public

    A safe, ergonomic, detachable hand-controlled mechanism that allows full coordinated actuation of accelerator, brake, and clutch of a manual transmission vehicle, by just one hand without any -leg …