what activation function

Gradient Based Learning 1 Depth Feedforward network (Deep Feedforward Network), also known as feedforward neural network or multilayer perceptron (multilayer PERCEPTRON,MLP), Feedforward means that information in this neural network is only a

Tricks efficient BP (inverse propagation algorithm) in neural network trainingTricks efficient BP(inverse propagation algorithm) in neural network training[Email protected]Http://blog.csdn.net/zouxy09tricks! It's a word that's filled with mystery

The biggest problem with full-attached neural networks (Fully connected neural network) is that there are too many parameters for the full-connection layer. In addition to slowing down the calculation, it is easy to cause overfitting problems.

The previous blog introduced the use of the logistic regression to achieve kaggle handwriting recognition, this blog continues to introduce the use of multilayer perceptron to achieve handwriting recognition, and improve the accuracy rate. After I

1. Common activation functions The selection of activation function is an important link in the process of constructing neural network, the following is a brief introduction to the commonly used activation functions.(1) linear functions (Liner

If you have any questions, refer to the FAQ first.If you do not find a satisfactory answer, you can leave a message below :)First, I apologize to you for not updating it for a long time.Article. Too many chores recently. Sorry. 1 Introduction In

C ++ convolutional neural network example: tiny_cnn code explanation (10) -- layer_base and layer Class Structure Analysis In the previous blog posts, we have analyzed most of the layer structure classes. In this blog post, we plan to address the

Let's say we've installed the TensorFlow. Generally in the installation of good TensorFlow, will run its demo, and the most common demo is handwritten digit recognition of the demo, that is, mnist data set. However, we just ran its demo, maybe a lot

The biggest problem with full-attached neural networks (Fully connected neural network) is that there are too many parameters for the full-connection layer. In addition to slowing down the calculation, it is easy to cause overfitting problems.

This series of articles is about UFLDL Tutorial's learning notes.Neural Networks For a supervised learning problem, the training sample input form is (x (i), Y (i)). Using neural networks we can find a complex nonlinear hypothesis H (x (i))

TensorFlow implements AutoEncoder self-encoder, I. Overview AutoEncoder is a learning method that compresses and downgrades the high-dimensional features of data, and then undergoes the opposite decoding process. The final result obtained by

Learning notes TF032: Implementing Google Inception Net and tf032inception Google Inception Net, No. 1 in ILSVRC 2014. Control the calculation amount and parameter quantity, and the classification performance is very good. V1, top-5 Error Rate 6.67%,

The radial basis function (RBF) method of multivariable interpolation (Powell) was proposed in 1985. 1988 Moody and darken a neural network structure, RBF neural network, which belongs to the Feedforward neural network, can approximate any

Recently in the deep study of things, a beginning to see the Wunda of the UFLDL tutorial, there is a Chinese version of the direct look, and later found that some places are not very clear, and to see the English version, and then find some

catalog view Summary view Subscription   [Top] "convolutional neural network-evolutionary history" from Lenet to AlexnetTags: CNN convolutional neural Network Deep learningMay 17, 2016 23:20:3046038 people read Comments (4) favorite

In the Perceptron neural network model and the linear Neural network model learning algorithm, the difference between the ideal output and the actual output is used to estimate the neuron connection weight error. It is a difficult problem to

Machine Learning:neural NetworkA: PrefaceDefinition of the neural network on 1,wikipedia:InchMachine Learning, Artificial neural networks (anns) is a family of statistical learning algorithms inspired byBiological Neural Networks(TheCentral Nervous

This article by @ Star Shen Ge ice not to produce, reprint please indicate author and source.Article Link: http://blog.csdn.net/xingchenbingbuyu/article/details/53677630Weibo: http://weibo.com/xingchenbing In the previous blog Net class design and

I. Artificial neural element model1. Synaptic value (connection right)Each synapse is characterized by its weight, and the connection strength between each neuron is represented by the synaptic value. On synapses connected to neurons, the connected

Deep Learning paper notes (IV.) The derivation and implementation of CNN convolution neural network[Email protected]Http://blog.csdn.net/zouxy09 I usually read some papers, but the old feeling after reading will slowly fade, a day to pick up when it