I am a Computer Science student with a strong interest in Artificial Intelligence, Machine Learning, and Data Science, focusing on research-driven learning, strong theoretical foundations, and real-world problem solving.

I enjoy building structured learning systems, experimenting with models, reading research papers, and continuously improving my understanding of modern AI systems.

"I teach machines to learn—and myself to survive the bugs."

⚡ Currently exploring deep learning architectures and research-focused ML systems.

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• Designed and trained a custom CNN architecture using PyTorch
• Performed character-wise accuracy analysis across 59 classes
• Implemented train/validation/test split with reproducible preprocessing pipeline
• Evaluated using confusion matrix and per-class metrics
• Achieved 98.92% test accuracy on held-out test set (126k+ total images across 59 classes)

Tech Stack: Python, PyTorch, NumPy, Matplotlib

• Designed and implemented a complete NLP pipeline: scraping, preprocessing, manual labeling
• Implemented TF-IDF + Logistic Regression baseline
• Compared against LSTM/Deep Learning model
• Evaluated using precision, recall, F1-score

Tech Stack: Python, Scikit-learn, PyTorch, Pandas

• Computer Vision
• Deep Learning Architectures
• NLP for Low-Resource Languages
• AI for Nepali Language Processing

• Advanced Deep Learning (CNN architectures, optimization techniques)
• Research Paper Implementation
• Mathematical Foundations for Machine Learning

• Website: https://www.bishwaghimire.com.np/
• LinkedIn: https://www.linkedin.com/in/bishwaghimire
• Facebook: https://www.facebook.com/bishwaghimire4560
• Hugging Face: https://huggingface.co/bishwaghimire

⭐ This profile is actively maintained and reflects my continuous learning and research journey.