what activation function

activation functions of neural networks (Activation function) This blog is only for the author to record the use of notes, there are many details of the wrong place. Also hope that you crossing can forgive, welcome criticism correct. More related

A summary of lstm theory deduction Catalogue 1. The problem of traditional RNN: the disappearance and eruption of gradients 2. Lstm the solution to the problem 3. LSTM design of the model 4. Core ideas and derivation of lstm training 5. Recent

0 Monographs Lstm is a variant of RNN, which belongs to the category of feedback neural networks. 1. Problems of the traditional RNN model: disappearance and eruption of gradients When it comes to lstm, it's inevitable to first mention the

0-Background This paper introduces the deep convolution neural network based on residual network, residual Networks (resnets).Theoretically, the more neural network layers, the more complex model functions can be represented. CNN can extract the

In peacetime research, hope every night idle down when, all learn a machine learning algorithm, today see a few good genetic algorithm articles, summed up here.1 Neural network Fundamentals Figure 1. Artificial neural element modelThe X1~XN is an

Keras is a Theano and TensorFlow-compatible neural network Premium package that uses him to component a neural network more quickly, and several statements are done. and a wide range of compatibility allows Keras to run unhindered on Windows and

1. Data preprocessingbefore training the neural network, it is necessary to preprocess the data, and an important preprocessing method is normalization processing. The following is a brief introduction to the principle and method of normalization

ResNet (Residual neural Network), Microsoft Research Kaiming He and other 4 Chinese people proposed. Through Residual Unit training 152 layer Deep neural network, ILSVRC 2015 tournament champion, 3.57% top-5 error rate, the number of parameters is

1 Batch Processing TF requires both mean and variance data to be used in batch processing, the mean value and variance used in batch processing are not simple to use the mean and variance of the current batch data, but to find a new mean variance

4 activation function One of the things to be concerned about when building a neural network is what kind of activation function should be used in each separate layer. In logistic regression, the sigmoid function is always used as the activation

First, the main method of neural network performance tuning the technique of data augmented image preprocessing network initialization training The selection of activation function different regularization methods from the perspective of data

1. Introduction to Multilayer PerceptronA multilayer perceptron (MLP) can be seen as a logistic regression, but its input is preceded by a non-linear transformation, so that the data is mapped to a linearly divided space, which we call the hidden

Overview This demo is very suitable for beginners AI and deep learning students, from the most basic knowledge, as long as there is a little bit of advanced mathematics, statistics, matrix of relevant knowledge, I believe you can see clearly. The

Programmers who have turned to AI have followed this number ☝☝☝ Author: Lisa Song Microsoft Headquarters Cloud Intelligence Advanced data scientist, now lives in Seattle. With years of experience in machine learning and deep learning, we are

The structure of the classic convolutional neural network generally satisfies the following expressions: Output layer, (convolutional layer +--pooling layer?) ) +-Full connection layer + In the above formula, "+" means one or more, "? "represents

The cross-entropy cost function (cross-entropy) is a way to measure the predicted and actual values of an artificial neural network (ANN). Compared with the two-time cost function, it can promote the training of Ann more effectively. Before

Naive Bayes formulaHmm hidden MarkovDynamic planning:Linear regression:Logistic regression (sigmoid): A nonlinear activation function is added on the basis of linear combination to solve the problem of two classification and Softmax, which is used

The following is only my personal knowledge, not to mention please PAT.(At present, I only see some deep learning review and Tom Mitchell's book "Machine Learning" in the Neural network chapter, the understanding is limited. Feel 3\4 speak generally,

bp neural network in BP for back propagation shorthand, the earliest it was by Rumelhart, McCelland and other scientists in 1986, Rumelhart and in nature published a very famous article "Learning R Epresentations by back-propagating errors ". With

Time Series Model Time Series Prediction Analysis is to use the characteristics of an event time over a period of time to predict the characteristics of the event in the future. This is a kind of relatively complex prediction modeling problem, and