Deep Learning #

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Log Reg Nn

Logistic Regression as a Neural Network # Architecture # 𝐺𝑖𝑣𝑒𝑛 𝑥 , 𝑦̂ = 𝑃(𝑦 = 1|𝑥), where 0 ≤ 𝑦̂ ≤ 1 Parameters of logistic regression Input observation,features matrix X Target vector Y Weights w Threshold or bias b Output: 𝑦̂, sigmoid(z) where z = 𝑤 𝑇 *𝑥 + 𝑏 To get the parameters w and b (i.e. learning), we optimize on: 𝐽(𝑤, 𝑏) = 1/m (∑ 𝐿(𝑦̂ (𝑖) , 𝑦 (𝑖) ))