As a passionate and results-driven Machine Learning Engineer, I specialize in building production-grade AI systems and scalable cloud solutions. My expertise lies at the intersection of machine learning, software engineering, and competitive problem-solving.
Proficient in languages such as Python, C/C++, and Java, with familiarity in Go and C#, I enjoy tackling complex challenges. My experience spans across Machine Learning, Data Science, NLP, GenAI, and Cloud platforms like AWS and GCP. I'm also skilled in Deep Learning frameworks, Docker, and Git.
With a proven track record of success in national hackathons and a dedication to continuous learning, I am committed to pushing the boundaries of what's possible in AI and technology.
- 🔭🚀 I’m currently working on: Developing production-grade RAG pipelines with vision models,AWS services and Few Research projects
- 🌱🧠 I’m currently learning: AR/VR prototyping, Cloud Simulation,CyberSecurity & Quantum Computing
- 💬 Ask me about: Machine Learning Algorithms & Neural Networking, Software Development Operations and Competitive Programming.
- 📫 How to reach me: Follow the links above to connect👆,or just raise a issue and join us in one of the discussions.
- QWhale & SARSAWhale Hybrid Reinforcement Learning Algorithm for Energy Efficient Optimization and Scheduling
- QWhale and SARSAWhale: Energy-Efficient and Energy-Aware Algorithms for High-Load Cloud Environments
- Crystal Quantum Shield (CQS): A Post-Quantum Cybersecurity Framework for API and Data Protection
- Understanding Quantum Processing Units (QPUs): The Future of Computing
- Balancing Energy Efficiency and Performance in Modern Processing Units: CPU, GPU, DPU, TPU, NPU, VPU, and QPU
- Comparative Analysis of TPUs and CPUs: Specialized Efficiency vs. Versatile Performance
- Liquid Neural Networks: Real-Time Adaptability and Temporal Processing Excellence
- Nuclear Fusion Dynamics: Reinforcement Learning and LLMs for Plasma Stability Optimization
- Scaling New Heights: LLaMA 3.1 405 B and H100 Tensor GPUs
- Exploring Mesop: A Comprehensive Python Web Framework Beyond Streamlit for AI and ML Applications
- Qwen/QwQ-32B vs GPT-O1 and Sonnet: What’s Different?
- NVIDIA Triton Inference Server
About Me = [
"I'm a student with a passion for Artificial Intelligence(Automation & Robotics) and a journey of steep continuous learning.",
"Proficient in languages like Python,Java,C#/C++,Kotlin,Dart,R; as well as web technologies including Full stack and MERN.",
"Dedicated to daily exploration, we uncover new algorithms and concepts, forging ahead with curiosity.",
"In a realm where every byte counts and speed reigns supreme, I'm the code whisperer, crafting efficiency into existence. 💻✨"
]
Copyright (c) 2022-2032 Aakarshit Srivastava and the PerfectCube Team
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
