Fundamentals of AI

In this section, we explore the mathematical and conceptual foundations that power AI.

Topics

• Linear Regression
  Learn the basics of modeling relationships with straight lines.

  • Simple Training Intuition
    Understand how a simple algorithm adjusts its weights to fit a given target number.

• Loss Functions in Machine Learning
  Discover how to measure prediction errors.

• Gradient Descent
  Explore the algorithm that optimizes model performance.

• Activation Functions
  Understand how this core building block of neural networks works.

• Softmax & Cross-Entropy
  Explaining how softmax works and how the cross entropy classifier measures the loss.

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• Neural Networks
  Put it all together: layers, neurons, and end-to-end training.

• Weight Initialization Why setting initial weights matters and how it affects neural network training?