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Hirdesh Viikram

Software developer

About Me

Hello, I'm Hirdesh Viikram. I have a strong interest in machine learning and distributed systems, looking foward to working on scalable, innovative solutions that address real-world challenges. I am enthusiastic about working with robust architectures and cutting-edge algorithms, and I eagerly anticipate exploring diverse fields and topics to uncover additional areas of interest. View certifications

My Projects

Learning Pathways

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Location

Bangalore, India

Topics

Neural Network Architectures

Exploring the foundational structures of deep learning models, including feedforward networks, convolutional networks, and recurrent networks, which are essential for tasks like image recognition and natural language processing.

Optimization Techniques

Understanding methods such as gradient descent, stochastic gradient descent, and advanced algorithms like Adam and RMSprop, which are crucial for training efficient and accurate machine learning models.

Distributed Computing

Delving into the principles and practices of distributing computational tasks across multiple machines, enabling the handling of large-scale data and complex computations in AI applications.

Edge Computing

Bringing computation and data storage closer to the sources of data, reducing latency and bandwidth usage, and enabling real-time processing in applications like IoT devices and autonomous vehicles.

Projects Studio

YouTube Skeleton Clone

Serverless video processing using GCP & Firebase. Utilizes Google Cloud Storage for hosting, Cloud Pub/Sub for event-driven processing, FFmpeg for transcoding, and scalable Cloud Run workers. Features a responsive Next.js client with Firebase Auth.

Technologies: GCP, Firebase, Next.js, FFmpeg
GitHub

AI-Powered Health Assistant

A backend health monitoring system featuring ML-based analysis (Random Forest), NLP-driven voice interaction, and real-time data visualization with Plotly. Built with FastAPI, WebSockets for live updates, and persistent storage using SQLAlchemy.

Technologies: FastAPI, SQLAlchemy, WebSockets, Plotly, Random Forest
GitHub