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Foundations

Foundations of Regression0/8
  • Linear regression: lines, SSR, and gradient descent
  • Why do we need logistic regression?
  • The sigmoid: the secret sauce of logistic regression
  • Logistic regression and decision boundaries
  • Intuition behind logistic regression
  • Log likelihood instead of squared error
  • Interview Readiness, Foundations of Regression
Deep Neural Networks0/12
  • From linear regression to the perceptron
  • Layers in a deep neural network
  • Activation functions
  • Loss functions
  • How neural networks make predictions (forward pass)
  • How neural networks learn (backward pass)
  • Trainable parameters and hyperparameters
  • Overfitting in neural networks
  • Neural network architecture (binary classification recap)
  • Neural networks from scratch (NumPy)
  • Interview Readiness, Deep Neural Networks