coursera neural networks

Building your recurrent neural network-step by step Welcome to Course 5 ' s-A-assignment! In this assignment, you'll implement your The recurrent neural network in NumPy. Recurrent neural Networks (RNN) are very effective for Natural Language processing and other sequence tasks because they h Ave "Memory". They can re

, the objective function of SVM is still convex. Not specifically expanded in this chapter, the seventh chapter is detailed.Another option is to fix the number of base functions in advance, but allow them to adjust their parameters during the training process, which means that the base function can be adjusted. In the field of pattern recognition, the most typical algorithm for this method is the forward neural network (Feed-forward

Now that the "neural network" and "Deep neural network" are mentioned, there is no difference between the two, the neural network can not be "deep"? Our usual logistic regression can be thought of as a neural network with sigmoid (logistic) for output layer activation functions without hidden layers, and it is clear th

I. Convolutionconvolutional Neural Networks (convolutional neural Networks) are neural networks that share parameters spatially. Multiply by using a number of layers of convolution, rather than a matrix of layers. In the process o

in the first convolutional layer and the first fully connected layer. Finally, they reduced the number of first convolutional neurons from 20 to 10, reducing the number of neurons in the first fully-connected layer from 500 to 100. After 2000 iterations, the network accuracy rate reached 98.2%.Figure 9 Mnist network analysis diagram. From left to right, the first convolution layer, the second convolutional layer, the first fully connected layer, and the second fully connected layer.In general,

convolutional neural Network (CNN) is the foundation of deep learning. The traditional fully-connected neural network (fully connected networks) takes numerical values as input.If you want to work with image-related information, you should also extract the features from the image and sample them. CNN combines features, down-sampling and traditional

programming principle and construct a dynamic sequence model. This requires recurrent neural Network (RNN) to achieve.RNN is usually translated into cyclic neural networks, and its similar dynamic programming principles can also be translated into sequential recurrent neural networks.Of course there are structural rec

Now that the "neural network" and "Deep neural network" are mentioned, there is no difference between the two, the neural network can not be "deep"? Our usual logistic regression can be thought of as a neural network with sigmoid (logistic) for output layer activation functions without hidden layers, and it is clear th

Transfer from http://blog.csdn.net/xingzhedai/article/details/53144126More information: http://blog.csdn.net/mafeiyu80/article/details/51446558http://blog.csdn.net/caimouse/article/details/70225998http://kubicode.me/2017/05/15/Deep%20Learning/Understanding-about-RNN/RNN (recurrent Neuron) is a neural network for modeling sequence data. Following the bengio of the probabilistic language model based on neural

parameter random initialization is introduced, we can combine my previous a neural network to get started knowledge http://blog.csdn.net/u012328159/article/details/ 51143536 See, believe can have a basic understanding of neural network. Note: Provide some reference material to everyone, can better help you understand the neural network better. Talk abo

Just entered the lab and was called to see CNN. Read some of the predecessors of the blog and paper, learned a lot of things, but I think some blog there are some errors, I try to correct here, but also added their own thinking and deduction. After all, the theory of CNN has been put forward, I just want to be able to objectively describe it. If you feel that there is something wrong with this article, be sure to tell me in the comments below.convolutional n