mlp neural network

Preface I have been dealing with neural networks (ANN) for a long time. I used to learn the principles. I have done a BPN exercise. I have not summarized it systematically. I recently read the torch source code, I have a better understanding of MLP, and I have made a summary by writing what I learned!Features of ANN (1) high concurrency Artificial Neural Networks

Note: Organize the PPT from shiming teacherContent Summary 1 Development History2 Feedforward Network (single layer perceptron, multilayer perceptron, radial basis function network RBF) 3 Feedback Network (Hopfield network,Lenovo Storage Network, SOM,Boltzman and restr

=Datetime.datetime.now ()Print("Time Cost :") Print(Tend-tstart)Analysis:1. Forward Propagation: for in range (1, Len (synapselist), 1): Synapselist is a weight matrix.2. Reverse propagationA. Calculating the error of the output of the hidden layer on the inputdef GETW (Synapse, Delta): = [] # traverse the hidden layer each hidden unit to each output weight, such as 8 hidden units, each hidden unit two output each has 2 weights for in Range (Synapse.shape

through its axis bursts send a faint current to other neurons. This is a nerve that connects to the input nerve or to another neuron's dendrites, and the neuron then receives the message to do some calculations. It has the potential to transmit its own messages on the axon to other neurons. This is the model of all human thinking: our neurons compute the messages we receive and pass information to other neurons. This is how we feel and how our muscles work, and if you want to live a muscle, it

processing of high dimensional input data and the realization of automatic extraction of the core characteristics of the original data.Activation layer: The function is to process the linear output of the previous layer through the nonlinear activation function, so as to simulate any function, and then enhance the network's representation ability. In the field of depth learning, Relu (rectified-linear unit, fixed linear Element) is a more active function now, because it converges faster and doe

Main reference: http://colah.github.io/posts/2015-08-Understanding-LSTMs/ RNN (recurrent neuralnetworks, cyclic neural network) For a common neural network, the previous information does not have an impact on the current understanding, for example, reading an article, we need to use the vocabulary learned before, and t

Building your deep neural network:step by StepWelcome to your third programming exercise of the deep learning specialization. You'll implement all the building blocks of a neural network and use these building blocks in the next assignment to Bui LD a neural network of any a

can be combined into the accumulation, simplifying the expression, so you can get:And:The following deduction will take the form of (5.9). If you see the fourth chapter on the Perception Machine (perception) Introduction, you will find that the above form is equivalent to using a two-layer perceptron model, but also because of this, the neural network model is also known as Multilayer perceptron (the multi

ancestors were the Hopfield network proposed in 1982.The Hopfield network was replaced by a 86-year Feedforward network because of the difficulty of implementation, plus the lack of suitable applications.The 90 's coincided with the decline of neural networks, and the Feedforward