Who am I ?
A Machine Learning Researcher / Developer
Final-year B.Tech student passionate about AI and ML. Experienced in startups and research, eager to contribute skills to tech innovation.
Personal Info
- Birthdate : 09/12/2002
- Email : pranaysaha61@gmail.com
- Phone : + (91) 7044720385
- Address : 10A Belghoria Kolkata India.
My Expertise
Data Science
Machine Learning
Computer Vision
Language Models
My Resume
Expertise
Indian Statistical Institute
Junior ML Researcher
•Designed a novel symmetry-based method for Person-Vehicle Reidentification using a Neural Network trained on 4 datasets.
•Implemented and optimized data cleaning pipelines, reducing data cleaning time by 30%.
•Conducted a comparative analysis, demonstrating an approximately 8% improvement in accuracy compared to existing models (State-of-the-Art).
Yubky
Machine Learning Engineer
•Collaborated with 10 developers in the strategic development of Facial Recognition, Liveliness, Speech, and Audio Processing for Video KYC.
•Designed and secured an efficient KYC system, achieving a remarkable cost reduction from $130 to $4 per verification.
Education
Institute of Engineering & Management
B-Tech Computer Science Engineering Specilaization AIML
CGPA: 9.01
AIML Lead at Google Developer Student Club 2022 - 2023
Courses: Data Structures and Algorithms, Machine Learning, Pattern Recognition, Database Management System, Object Oriented Programming
St Xavier's Institution
High School Degree
Courses : Physics, Chemistry, Maths, English, Computer
Skills
Python
Java
C
SQL
TensorFlow
PyTorch
Git
Docker
Latest Blog
Dive into MLX: Performance & Flexibility for Apple Silicon
In the dynamic landscape of machine learning frameworks like Tensorflow, PyTorch, and Jax, we have a new contender Apple’s MLX. Designed specifically for machine learning research on Apple Silicon devices, MLX offers a NumPy-inspired, low-level Python API and a fully-featured C++ API that closely mirrors it.
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consectetur adipisicing elit. Impedit excepturi laborum enim, vitae ipsam atque eum, ad iusto consequuntur voluptas, esse doloribus. Perferendis porro quisquam vitae exercitationem aliquid, minus eos laborum repudiandae, cumque debitis iusto omnis praesentium? Laborum placeat sit adipisci illum tempore maxime, esse qui quae? Molestias excepturi corporis similique doloribus. Esse vitae earum architecto nulla non dolores illum at perspiciatis quod, et deleniti cupiditate reiciendis harum facere, delectus eum commodi soluta distinctio sit repudiandae possimus sunt. Ipsum, rem.
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