neural agility

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 neural network

first, the concept of BP neural networkBP Neural Network is a multilayer feedforward neural network, its basic characteristics are: the signal is forward propagation, and the error is the reverse propagation. in detail. For example, a neural network model with only one hidden layer, such as the following:(three-layer B

This article is from here, the content of this blog is Java Open source, distributed deep Learning Project deeplearning4j The introduction of learning documents. Introduction:in general, neural networks are often used for unsupervised learning, classification, and regression. That is, neural networks can help group unlabeled data, classify data, or output successive values after supervised training. Th

Tips: This article is a reference to the mechanical industry press "neural network Design" (Dai Qu, etc.) a book compiled by the relevant procedures, for beginners or want to learn more about the neural network kernel enthusiasts, this is the most reading value of the textbook. Perceptual machines and linear neural networks are the simplest and most basic types

Deep Learning Neural Network pure C language basic edition, deep Neural Network C Language Today, Deep Learning has become a field of fire, and the performance of Deep Learning Neural Networks (DNN) in the field of computer vision is remarkable. Of course, convolutional neural networks are used in engineering to reduce

1 Figure Neural Network (original version)Figure Neural Network now the power and the use of the more slowly I have seen from the most original and now slowly the latest paper constantly write my views and insights I was born in mathematics, so I prefer the mathematical deduction of the first article on the introduction of the idea of neural Network Diagram

3. Spark MLlib Deep Learning convolution neural network (depth learning-convolutional neural network) 3.3Http://blog.csdn.net/sunbow0Chapter III Convolution neural Network (convolutional neural Networks)3 Example3.1 test DataFollow the above example data, or create a new image recognition data.3.2 CNN Example??? //2 te

Learning Goals Understand the convolution operation Understand the pooling operation Remember the vocabulary used in convolutional neural network (padding, stride, filter, ...) Build a convolutional neural network for Image Multi-Class classification "Chinese Translation"Learning GoalsUnderstanding convolution OperationsUnderstanding pooling Operationsremember vocabulary used in co

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 integration of multiple depth networks 1. Data augmentation The generalization ability of the model can be improved by increasing the amount of data without changing the image category.The data augmentation metho

The use of neural networks to achieve autonomous driving, which means that the car through learning to drive themselves.It is a legend explaining how to realize automatic driving through neural network learning:The lower left corner is an image of the road ahead that the car sees. Left, you can see a horizontal menu bar (the direction indicated by the number 4), and the white section shows the direction the

Python implements simple neural network algorithms and python neural network algorithms Python implements simple neural network algorithms for your reference. The specific content is as follows: Python implements L2 Neural Networks Including the input layer and output layer import numpy as np #sigmoid function def non

I've been focusing on CNN implementations for a while, looking at Caffe's code and Convnet2 's code. At present, the content of the single-machine multi-card is more interested, so pay special attention to Convnet2 about MULTI-GPU support.where Cuda-convnet2 's project address is published in: Google Code:cuda-convnet2A more important paper on MULTI-GPU is: one weird trick for parallelizing convolutional neural NetworksThis article will also give an a

Part five The second model: convolutional neural NetworksDemonstrates the convolution operationLeNet-5-type convolutional neural network is the core of the great breakthrough in the field of computer vision recently. The convolution layer differs from the previous fully connected layer by using some techniques to avoid excessive number of parameters, but preserves the model's descriptive ability. These tips

Sample program download: http://files.cnblogs.com/gpcuster/ANN1.rarIf you have any questions, refer to the FAQ first.If you do not find a satisfactory answer, you can leave a message below :)1 IntroductionI still remember hearing from senior students about Ann (Artificial Neural Network) when I first came into contact with RoboCup two years ago. This is amazing, he can learn to solve some problems well. Just like our people, we can learn and learn new

Convolution neural Network (convolutional neural Network, CNN) is a feedforward neural network, which is widely used in computer vision and other fields. This article will briefly introduce its principles and analyze the examples provided by the TensorFlow official. I. Principle of work Convolution is a basic method in image processing. The convolution kernel is

First, you need to familiarize yourself with how to use pytorch to implement a feed-forward neural network. To facilitate understanding, we only use a feed-forward neural network with only one hidden layer as an example: The source code and comments of a feed-forward neural network are as follows: This is relatively simple and we will not discuss it here. 1 class

Neuron Model　　Neurons can be thought of as a computational unit that receives certain information from the input nerves, makes some calculations, and then transmits the results to other nodes or other neurons in the brain through axons.The neuron is modeled as a logical unit, as follows:　　In, the input unit is X1 X2 X3, sometimes can also be added x0 as offset units, the value of x0 is 1, whether to add bias units depends on whether it is advantageous to the example.The Orange small Circle in th

Data classification based on BP Neural network BP (back propagation) network is the 1986 by the Rumelhart and McCelland, led by the team of scientists, is an error inverse propagation algorithm training Multilayer Feedforward Network, is currently the most widely used neural network model. The BP network can learn and store a large number of input-output pattern mapping relationships without revealing the m

1. Some basic symbols2.COST function================backpropagation algorithm=============1. To calculate something 2. Forward vector graph, but in order to calculate the bias, it is necessary to use the backward transfer algorithm 3. Backward transfer Algorithm 4. Small topic ======== ======backpropagation intuition==============1. Forward calculation is similar to backward calculation 2. Consider only one example, cost function simplification 3. Theta =======implementation Note:unrolling param

LSTM unit.for the gradient explosion problem, it is usually a relatively simple strategy, such as Gradient clipping: in one iteration, the sum of the squares of each weighted gradient is greater than a certain threshold, and to avoid the weight matrix being updated too quickly, a scaling factor (the threshold divided by the sum of squares) is obtained, multiplying all the gradients by this factor. Resources:[1] The lecture notes on neural networks a