coursera neural networks for machine learning

the input terminal I is simply)Alpha factor:Alpha or E represents the annotation of the J-input word and the importance of the i-1 hidden state of the decoder end, so that the resulting CI will pay attention for some locations, equivalently as the translation word I to the original input some position pay AttetnionUsing BIRNN:This paper uses bidirectional rnn to catch the forward and backward hi stitching together, so that the annotation can represent the information around the input word I.Net

Rosenblatt SensorPerceptron is the simplest neural network model for classifying linear sub-modes (patterns located on both sides of the plane), essentially consisting of a neuron with adjustable synaptic weights and biases.Rosenblatt proves that the perceptron algorithm is convergent when the pattern (vector) used to train the Perceptron is taken from two linearly-divided classes, and the decision surface is a hyper-plane between the two classes. The

Weight vector W, training sample X1. Initialize the weight vector to 0, or initialize each component to any decimal between [0,1]2. Input the training sample into the Perceptron to get the classification result (-1 or 1)3. Update weight vectors based on classification resultsPerceptron algorithm for Tuyi data samples that are linearly delimitedMachine learning--perceptron data classification algorithm step (MU-class network-to achieve a simple