coursera neural networks

There ' s something magical about recurrent neural Networks (Rnns). I still remember I trained my recurrent network forimage. Within a few dozen minutes of training my The baby model (with rather Arbitrarily-chosen hyperparameters) started to Gen Erate very nice looking descriptions of images this were on the edge of making sense. Sometimes the ratio of how simple your model are to the quality of the result

Recently fascinated by the direction of Neuro-evolution (neuroevolution), the feeling is a very good research field after deep learning. One of the leading factors in this field is the evolution of network parameters and structures, modeled on human genetic mechanisms. Note that even the network structure can evolve, that is, unlike traditional neural networks, structures are defined in advance.The most rec

How the reverse propagation algorithm works In the previous article, we saw how neural networks learn through gradient descent algorithms to change weights and biases. However, before we discussed how to calculate the gradient of the cost function, this is a great pity. In this article, we will introduce a fast computational gradient algorithm called reverse propagation.

in Google, if the landing Google is difficult to come here to provide you with a stable landing method, one months 10 yuan is not expensive.(1) Ngiam, Jiquan,koh Pang wei,chen Zheng hao,bhaskar sonia,ng Andrew Y. Sparse Filtering,[c]. Advances in Neural information processing Systems 24:25th annual Conference on Neural information processing Systems,2011 : 1125-1133.(2) Zhen dong,ming tao Pei,yang he,ting

" because of "mountain climbing". The stochastic neural networks to be explained in this paper: Simulated annealing (simulated annealing) and Boltzmann machines (Boltzmann machine) are capable of "mountain climbing" by certain probability to ensure that the search falls into local optimum. The comparison of this image can be see:There are two main differences between random

the composition of a convolutional neural network Image classification can be considered to be given a test picture as input Iϵrwxhxc Iϵrwxhxc, the output of this picture belongs to which category. The parameter W is the width of the image, H is the height, C is the number of channels, and C = 3 in the color image, and C = 1 in the grayscale image. The total number of categories will be set, for example in a total of 1000 categories in the Imagenet c

Next. The previous two articles explained that neural networks are a black box with a small sphere (neuron) connected one by one. By changing the connection mode and parameters of neurons, you can implement a compliant neural network. Next we will give an example of a BP neural network to deepen our understanding. Befo

then immediately scaled back. This is an example of a neural network. The temperature produced by the fire opponent is the input layer (input) of Figure 2, and the scaled-down or not scaled-down is the output layer of Figure 2 ). But scale-down occurs only when the temperature in the hand reaches a certain level, for example, 40 degrees. Figure 2 is used to represent the preceding situation: X1 = temperature produced by fire opponents W1 = the w

of encoding. There are only one-dimensional non-0 features .Different types of neural networksFeed-forward Neural Networks (forward propagation neural network)More than one layer of hidden layer is the deep neural network.Recurrent netw