Machine Learning for Neural Networks (NN) - intro

Scheduled topic: --------------------- - Linear regression in one variable - Gradient descent for linear regression - Linear algebra (vectors and matrices operations) - Multivariate linear regression - Multivariate linear regression continued: normal equations and vectorized gradient descent - Logistic regression - Nonlinearity, overfitting, regularization - Multiclass classification - Cross-validation - Neural Nets Intro - Forward/Backward propagation demo - Momentum - Hidden layer - Softmax classification function - Neural net demo - Cross-entropy loss demo --------------------- - Discrete convolutions - Pooling - Convolution (padding, zero-padding, strides) - Transposed convolution