The basic overview of neural networks and neural network models are not carefully introduced here. A detailed introduction to the introduction of the neural network and its model is presented in the details of Daniel Ng, Stanford University. This paper mainly introduces the

are two functions head () and tail (), the implementation mechanism is very simple, I believe you can understand:As for how to access the specified layer, TINY_CNN provides two means, one is to define the at function and type conversion through dynamic_cast:Another method is to overload the "[]" operation, and to access the array as a classThe above two methods of access are indexed (index) to complete, more convenient.OK, about the layer structure container layers class source first introduced

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 last two layers, it is also the two basic classes layer_base and layer th

Optimization algorithm is an important part of machine learning, BP Neural network is the foundation of deep Learning, BP neural network principle is very simple, almost can be understood as a logistic regression of a set way, in the previous blog post, I use r language to achieve several optimization algorithms, Based

C ++ convolutional neural network example: tiny_cnn code explanation (9) -- partial_connected_layer Structure Analysis (bottom)
In the previous blog, we focused on analyzing the structure of the member variables of the partial_connected_layer class. In this blog, we will continue to give a brief introduction to other m

Example of an artificial neural network algorithm implemented by Python [Based on the back propagation algorithm], python Artificial Neural Network
This example describes the artificial neural

is going when it is initialized, or we don't know where the driving direction is, only after the learning algorithm has been running long enough that the white section appears in the entire gray area, showing a specific direction of travel. This means that the neural network algorithm at this time has chosen a clear direction of travel, not like the beginning of the output of a faint light gray area, but t

Deep Learning Notes (i): Logistic classificationDeep learning Notes (ii): Simple neural network, back propagation algorithm and implementationDeep Learning Notes (iii): activating functions and loss functionsDeep Learning Notes: A Summary of optimization methods (Bgd,sgd,momentum,adagrad,rmsprop,adam)Deep Learning Notes (iv): The concept, structure and code annot

1 Introduction
An XOR operation is a commonly used calculation in a computer:
0 XOR 0 = 0
0 XOR 1 = 1
1 XOR 0 = 1
1 XOR 1 = 0
We can use the code in the first article to calculate this result Http://files.cnblogs.com/gpcuster/ANN1.rar (need to modify the training set), we can find that the results of learning does not satisfy us, because the single layer of neural

1 Introduction
Remember when I first contacted RoboCup 2 years ago, I heard from my seniors that Ann (artificial neural network), this thing can be magical, he can learn to do some problems well enough to deal with. Just like us, we can learn new knowledge by studying.
But for 2 years, I've always wanted to learn about Ann, but I haven't been successful. The main reason for this is that the introduction o

common theory of neural network structure and working principle, simple and good understanding, recommended to watch2, the mathematical derivation of the inverse propagation algorithm, if it is too complicated to temporarily skip3,matlab Code and Image Library(1) Plain English explain the traditional neural networkFir

This article mainly introduces Python based on numpy flexible definition of neural network structure, combined with examples of the principle of neural network structure and python implementation methods, involving Python using numpy extension for mathematical operations of the relevant operation skills, the need for f

code.After the completion of network training, only need to input the quality indicators of the network can be predicted data.The prediction result is: 2.20Matlab code:
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?P=[3.2 3.2 3 3.2 3.2 3.4 3.2 3 3.2 3.2 3.2 3.9 3.1 3.2;9.6 10.3 9 10.3 10.1 10 9.6 9 9.6 9.2 9.5 9 9.5 9.7;3.45 3.7

Constructing neural network with Keras
Keras is one of the most popular depth learning libraries, making great contributions to the commercialization of artificial intelligence. It's very simple to use, allowing you to build a powerful neural network with a few lines of code

Tutorial Content:"MATLAB Neural network principles and examples of fine solutions" accompanying the book with the source program. RAR9. Random Neural Networks-rar8. Feedback Neural Networks-rar7. Self-organizing competitive neural networks. RAR6. Radial basis function

Python-based three-layer BP neural network algorithm example, pythonbp
This example describes the three-layer BP neural network algorithm implemented by Python. We will share this with you for your reference. The details are as fo

Originally intended to begin the translation of the calculation of the part, the results of the last article just finished, mxnet upgraded the tutorial document (not hurt AH), updated the previous in the handwritten numeral recognition example of a detailed tutorial. Then this article on the Times, to the just updated this tutorial translated. Because the current picture can not upload to the blog, the relevant pictures can be viewed from the original

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