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Dropout - One way to avoid overfitting, using several neurons only rather than whole neurons when machine training.- It does not have any learnable parameters and has only one hyperparameter.- It does not behave similarly during training and testing.  To be understand this, let us consider a simple fully connected layer containing 10 neurons. We are using a dropout probability of 0.5. Well durin..

Forward Propagation Computation z : variable, accumulated sum from previous layers of x * w, before applied sigmoid(or whatever activation function) function z1=W1x1+W2x2=0.3×0.1+0.25×0.2=0.08z1=W1x1+W2x2=0.3×0.1+0.25×0.2=0.08 z2=W3x1+W4x2=0.4×0.1+0.35×0.2=0.11 Then, z1 and z2 shoulc through sigmoid(or whatever activation function) function in each hidden layer. h1=sigmoid(z1)=0.51998934h1=sigmo..

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..