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forward propagation 2

Forward Propagation, Forward Propagation Computation

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..

Deep Learning 2021.04.07

Forward Propagation, Batch Gradient, Stochastic Gradient Descent, SGD, Mini Batch Gradient Descent, Momentum, Adagrad, Rprop, RMSprop, Adam, Epoch, Batch size, Iteration

Forward Propagation Input layer-->hidden layer-->activation function-->output layer--------------------------------------------------------------> in orderThe 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 be..

Deep Learning 2021.04.06
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