According to the Neural Network structure shown in the following figure, and assume that: 1. All weights are set as: 0.5 0.2*x, x < 0 2. Activation function is LeakyReLU: f(x) = {0.2 * n - x, x ≥0 3. Loss function is defined as: L = ½-½ 11(y; — ŷ;)², where y; is the ground truth and ŷi is the output of the Neural Network. W₁₁ (Σ (Σ x1 Σ Σ Σ ŷ (Σ (Σ) Given a dataset D = {(x1,y1), (x2, y2), (X3, Y3), (x4, Y4)}, and • x₁ = (1,3), y₁ = 5 • x2 = (-1,2), y₂ = 3 • x3 = (2,6), y3 = 7 • x4 = (4,2), y = 2 Q1: [15%] [Forward Propagation] calculate the corresponding output ŷ1, ŷ2, Ŷ3, ŷ4 JL Q2: [20%] [Backward Propagation] calculate the loss function and gradient: (red weight in the structure) aw 1

Branch of computer science concerned with using technology to solve problems that typically require human intelligence.