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Extra resources for Electronics for Hobbyists (Unit 3. Active Devices)
2. Presentation of training samples. Present the network with an epoch of training examples. For each example in the sample, perform the forward and backward computations, as described in steps 3 and 4. 3. Forward computation. For a training example denoted by x k; d k, compute the induced local ﬁelds and function signals of the network by proceeding h forward through the network, layer-by-layer. 40) i0 where y i h 1 k is the output signal of neuron i at iteration k, and w jih k is the synaptic weight of neuron j in layer h that is fed from neuron i in layer h 1.
Besides, in a statistical context, batch learning may be viewed as a form of statistical inference. Therefore, it is well suited for solving nonlinear regression problems. 2 Online Learning In online learning, adjustments to the synaptic weights of the multilayer perceptron are performed on the example-by-example basis. Thus, the cost function to be minimized is the total instantaneous error energy E k. Consider an epoch of K training examples arranged in the order fx 1; d 1g, fx 2; d 2g, .
1 describes two classes of patterns in the two-dimensional plane. 18. 1 Pattern Classiﬁcation x1 x2 d Class 2 1 2 2 2 0 0 1 0 1 @1 @2 @1 @2 2 1 Now, we describe the iterative process of the training algorithm as follows, with the purpose of classifying the patterns: 1. Set w 0 0. 2. Compute y 0 ϕ 0 0 0 1 2 2 T ϕ 0 1 Since the actual response is not equal to the desired response, we update the weight and bias as w 1 0 0 T 0 0 1 1 2 2 T 1 2 2 T 3. Compute y 1 ϕ 1 2 2 1 1 2 T ϕ 1 1 The actual response is equal to the desired response, so we do not need to update the weight and bias.