Sharing what I learn while working on new ideas.
In a world where time seems to slip through our fingers, Live Dots offers a unique and beautiful way to visualize your year’s progress right on your phone’s wallpaper. This innovative Android live wallpaper app turns each day of the year into a visual...
I fine-tuned Meta’s LLaMA 3.1-8B model to translate medical text from English to Nepali, using only a free Google Colab GPU (Tesla T4). The result?An 8.9× performance improvement over zero-shot translation—turning an unusable model into something genuinely helpful for 30 million Nepali speakers. Key...
Abstract As Large Language Models (LLMs) evolve from simple text generators to reasoning engines, the focus of AI development has shifted toward agentic workflows—systems capable of autonomous planning, tool use, and self-correction. To explore the efficacy of modern orchestration frameworks, I engineered the Deep...
Stop AI hallucinations: Discover how RLHF uses human feedback to make chatbots truthful, safe, and actually helpful.
The F1 score is a crucial metric in the field of machine learning, particularly in the evaluation of classification models. It provides a balance between precision and recall, making it especially useful in scenarios where the class distribution is imbalanced. This document will delve...
Decision trees are a fundamental building block in machine learning, particularly in the context of ensemble methods like Random Forest. A decision tree is a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. In machine learning,...
Random Forest is a versatile and robust machine learning algorithm that belongs to the family of ensemble learning methods. It combines multiple decision trees to create a more accurate and stable predictive model. How Random Forest Works Key Advantages of Random Forest Applications of...
Gradient Boosting and Random Forest are two powerful ensemble learning techniques that have become essential tools in the machine learning practitioner’s toolkit. Both methods combine multiple base models to create a more accurate and robust predictive model. However, they differ significantly in their underlying...
Learn the fundamentals of ML with this easy-to-follow PDF. Perfect for beginners and curious minds.