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Forward Propagation Input layer-->hidden layer-->activation function-->output layer --------------------------------------------------------------> in order The input data is fed in the forward direction through the network. Each hidden layer accepts the input data, processes it as per the activation function and passes to the successive layer. In order to generate output, the input data should ..

Feed Forward Neural Network(FFNN) An extension of the logistic regression algorithm. FFNN are stacking multiple hidden layers (any layers that's not the input or output layer) followed by non linear activation, such as the sigmoid or softmax activations. FFNN are only made of fully connected layers, where each neuron in one layer is connected to all the neurons in the previous layer. y=f*(x) y :..

Perceptron It is a linear classifier, an algorithm for supervised learning of binary classifiers. Input(multiple x)-->Output(one y) x : input W : Weight y : output Each x has each weights. Larger w, more important x. Step function ∑W * x >=threshold(θ)-->output(y) : 1 ∑W * x output(y) : 0 Threshold(θ) can be expressed b(bias) such as Single-Layer Perceptron It can learn only linearly separable p..

Entropy The level of uncertainty. It must range between 0 and 1. 0

Support Vector Machine(SVM) The objective of the support vector machine algorithm is to find a hyperplane in an N-dimensional space that distinctly classifies the data points when given the dichotomous data . Support vectors are data points that are closer to the hyperplane and influence the position and orientation of the hyperplane. Using these support vectors, we maximize the margin of the cl..