Imagine a teacher grading a multiple-choice exam. If the teacher says, “Only this one answer has any value, and all […]
Label Smoothing: Intuition, Mathematics, Gradients, and Practical Use Read More »
Decision trees are one of the simplest ways to turn data into a sequence of human-readable rules. You can think
How Decision Trees Work: A Practical Machine Learning Guide Read More »
Machine Learning is often described as “data + algorithms”, but mathematics is the glue that makes everything work. At its
The Core Mathematical Foundations of Machine Learning Read More »
Before LightGBM entered the scene, another algorithm reigned supreme in the world of machine learning competitions and industrial applications: XGBoost.
XGBoost: Extreme Gradient Boosting — A Complete Deep Dive Read More »
Two critical issues that often arise in training deep neural networks are vanishing gradients and exploding gradients. These issues can
The Vanishing and Exploding Gradient Problem in Neural Networks: How to Overcome It Read More »
With the advances of deep learning come challenges, most notably the issue of overfitting. Overfitting occurs when a model learns
Regularization Techniques in Neural Networks Read More »
Central to the transformer architecture is its capacity for handling large datasets and its attention mechanisms, allowing for contextualized representation
Weight Tying In Transformers: Learning With Shared Weights Read More »
Neural networks have revolutionized various fields, from image and speech recognition to natural language processing. The primary goal of training
Optimization Techniques in Neural Networks: A Comprehensive Guide Read More »
