Deep-learning

• Activation Functions in Neural Networks: Intuition, Visuals, and Trade-offs

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  Why activation functions matter, popular choices, and how they shape learning—illustrated.
• Building a PyTorch RNN-Based Question Answering System

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  Learn 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?

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  An 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 Networks

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  Feed 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?

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  The 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 Language

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  Tokenization isn't just splitting text—it's defining the fundamental units of meaning that determine how AI models perceive and understand language.