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Understanding Stochastic Gradient Descent (SGD)
A detailed guide to gradient descent variants, highlighting the mechanics, trade-offs, and practical insights of Stochastic Gradient Descent (SGD).
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Gradient Descent - A Detailed Walkthrough
An in-depth exploration of gradient descent, including its convergence and step size considerations.
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Empirical Risk Minimization (ERM)
Exploring Empirical Risk Minimization - Balancing approximation, estimation, and optimization errors to build effective supervised learning models.
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Understanding the Supervised Learning Setup
An in-depth exploration of the supervised learning setup, covering key concepts like prediction functions, loss functions, risk evaluation, and the Bayes optimal predictor.
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Timeline of Machine Learning History
A concise timeline of machine learning's history, showcasing key milestones and breakthroughs that shaped the field.