machine learning and neural networks

First, prefaceThis convolutional neural network is the further depth of the multilayer neural network described above, which introduces the idea of deep learning into the neural network, and extracts different levels of images from the image by convolution operation, and uses the training process of

relationship scenarios. In recent years, the most popular neural network algorithm, which can deal with many problems in the field of machine learning. Neural network algorithms have the ability of linear and nonlinear learning algorithms.Neural

storage functions that are connected to form a system. In the artificial neural network, there are also a large number of neurons with local processing capability, and the information can be processed massively in parallel.Storage and Operation: both the human brain and the artificial neural network are capable of memory storage through the connection strength of neurons, and provide strong support for gen

, it can be seen that although the full The RBF effect may be better than K-means, but generally it is not often used due to computational complexity and overfitting risk.*************************************************************************************************************** **********************For radial basis function neural networks, just grasp the rustic representation of its hypothesis: a bunc

Reference booksDeep learningDeep learning is a new field in machine learning research, and its motive is to establish and simulate the neural network of human brain import analysis and learning, which imitates the mechanism of human brain to interpret the data.Examples of

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

This paper mainly records the cost function of neural network, the usage of gradient descent in neural network, the reverse propagation, the gradient test, the stochastic initialization and other theories, and attaches the MATLAB code and comments of the relevant parts of the course work. Concepts of neural networks,

)/∂ (θ (1) JK) is tested for gradients. After the partial derivative code does not have a problem, close the Gradient check section code.6. Use gradient descent or other advanced algorithms to perform reverse propagation to find the θ values for minimizing j (θ).This paper describes the gradient descent algorithm in neural networks: starting from the random initial point, descending step by step, until the

sentence The main task of pattern recognition is to design a classifier that is invariant to these transformations, with the following three techniques: Structural invariance: The design of the structure has taken into account the insensitivity to the transformation, and the disadvantage is that the number of network connections becomes large Training invariance: Different sample training parameters for the same target; disadvantage: It is not guaranteed that the tr

Translator Note : This article is translated from the Stanford cs231n Course Note convnet notes, which is authorized by the curriculum teacher Andrej Karpathy. This tutorial is completed by Duke and monkey translators, Kun kun and Li Yiying for proofreading and revision.The original text is as follows Content list: structure Overview A variety of layers used to build a convolution neural networkThe dimension setting regularity of the arrangement law l

In the first two sections, the logistic regression and classification algorithms were introduced, and the linear and nonlinear data sets were classified experimentally. Logistic uses a method of summation of vector weights to map, so it is only good for linear classification problem (experiment can be seen), its model is as follows (the detailed introduction can be viewed two times blog: linear and nonlinear experiments on logistic classification of machine