Activation Functions in Neural Networks: Intuition, Visuals, and Trade-offsdeep-learningneural-networksactivation-functionsWhy activation functions matter, popular choices, and how they shape learning—illustrated.
Building a PyTorch RNN-Based Question Answering Systempytorchrnnneural-networksquestion-answeringdeep-learningLearn how to build a simple RNN-based QA system using PyTorch from scratch - covering tokenization, embeddings, and sequence modeling.
What is an epoch in machine learning?machine-learningtrainingdeep-learningbasicsAn epoch is one full pass over the training dataset. Learn how it differs from batches and steps—with an interactive animation.
What is Feed Forward? Understanding the Foundation of Neural Networksneural-networksmachine-learningdeep-learningartificial-intelligenceFeed forward is the fundamental process in neural networks where data flows unidirectionally from input to output layers, enabling pattern recognition and prediction without feedback loops.
What is learning rate in machine learning?machine-learningoptimizationgradient-descentdeep-learningThe learning rate controls how big each step is during optimization—too small is slow, too large overshoots.
What Is Tokenization? The Foundation That Shapes How LLMs Understand Languagemachine-learningnlptokenizationllmdeep-learningTokenization isn't just splitting text—it's defining the fundamental units of meaning that determine how AI models perceive and understand language.