Hi ! I'm Dinesh Kumar M R

A passionate ML Engineer, Software developer from India

I am excited to apply my skills and knowledge as a web developer and machine learning engineer. With a strong background in programming languages such as Python, C/C++, and Node.js, as well as experience in machine learning, web development, and data science, I am confident in my ability to deliver outstanding results. My experience in data structures and networking concepts further enhances my skillset. I am eager to continue learning and growing in this field and am excited for the opportunity to make a positive impact.

Skills

🔭 I’m currently working on [MediVQA - A Medical Visual Question Answering System]
🌱 I’m currently exploring more on LLMs and NLP (Natural Language Processing)
👯 I’m looking to collaborate on more ML and DL projects
👨‍💻 All of my projects are available at https://github.com/dinesh-kumar-mr
💬 Ask me about Machine Learning and Deep Learning
📫 How to reach me [email protected]

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Dinesh Kumar 's Projects

admm-and-its-applications

In this review, we argue that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.

blip

PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

dinesh-kumar-mr

My personal repository

gene-prediciton-and-editing

Naturally if we can identify a gene that is responsible for a phenotype then we can use that information to edit the gene to better suit us. (this tells us where to look for and what to change to get desired results).

image-denoising-using-decomposition-methods

Image noise refers to random variations in brightness and color information that are not usually part of the original image. We have made a comparative analysis of the algorithms we used so that it performs better in different noise levels.

medivqa

Part of our final year project work involving complex NLP tasks along with experimentation on various datasets and different LLMs

predicting-the-type-and-target-of-offensive-posts-in-twitter

In this project, we have proposed a meta-learning approach for text classification using a combination of a base model and a meta-learner model. The base model, based on the BERT architecture, is used to extract contextualized representations of text.

scientific-calculator

Done using Java with significant GUI and deployed as an app.

twitter-data-sentimental-analysis

Collecting the real-time data (streaming) using the TwitterAPI and doing sentimental analysis using some appropriate models (Go through README file) and finally visualizing the final results in MONGODB Charts using appropriate graphs.

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