neural agility

Deep neural Network, the problem of pattern recognition, has achieved very good results. But it is a time-consuming process to design a well-performing neural network that requires repeated attempts. This work [1] implements a visual analysis system for deep neural network design, Deepeyes. The system can extract data in Dnns training process, analyze the operati

Circular neural Network Tutorial-the first part RNN introduction Cyclic neural Network (RNN) is a very popular model, which shows great potential in many NLP tasks. Although it is popular, there are few articles detailing rnn and how to implement RNN. This tutorial is designed to address the above issues, and the tutorial is divided into 4 parts:1. Introduction to RNN (this tutorial)2. Realize RNN with Tens

This article by the @ Star Shen Pavilion Ice language production, reproduced please indicate the author and source. article link: http://blog.csdn.net/xingchenbingbuyu/article/details/53674544 Micro Blog: http://weibo.com/xingchenbing Gossip less and start straight. Since it is to be implemented in C + +, then we naturally think of designing a neural network class to represent the neural network, which I c

"Self-built Neural Networks" is an e-book. It is the first and only Neural Network book on the market that uses Java. What self-built Neural Networks teach you: Understand the principles and various design methods of neural networks, and make it easy to use ground gas; Understand the implementation of each component o

Neural NetworkIt is a system that can adapt to the new environment. It has the ability to analyze, predict, reason, and classify the past experience (information, it is a system that can emulate the human brain to solve complex problems. Compared with conventional systems (using statistical methods, pattern recognition, classification, linear or nonlinear methods, A Neural Network-based system has more powe

The linear neural network is similar to the perceptron, but the activation function of the linear neural network is linear rather than the hard transfer function, so the output of the linear neural network can be any value, and the output of the perceptron is not 0 or 1. Linear neural networks, like Perceptron, can onl

3. Model Representation I 1Neural networks are invented when mimicking neurons or neural networks in the brain. So, to explain how to represent a model hypothesis, let's start by looking at what individual neurons are like in the brain. Our brains are filled with neurons like the one shown here, which are cells in the brain. One of the two points worth noting is that neurons have cell bodies like this (Nucleus), and neurons have a certain number of i

Transfer from http://blog.csdn.net/xingzhedai/article/details/53144126More information: http://blog.csdn.net/mafeiyu80/article/details/51446558http://blog.csdn.net/caimouse/article/details/70225998http://kubicode.me/2017/05/15/Deep%20Learning/Understanding-about-RNN/RNN (recurrent Neuron) is a neural network for modeling sequence data. Following the bengio of the probabilistic language model based on neural

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 annotation of cyclic neural networksDeep Learning

"This paper presents a comprehensive overview of the depth of neural network compression methods, mainly divided into parameter pruning and sharing, low rank decomposition, migration/compression convolution filter and knowledge refining, this paper on the performance of each type of methods, related applications, advantages and shortcomings of the original analysis. ” Large-scale neural networks have a la

I. Convolutionconvolutional Neural Networks (convolutional neural Networks) are neural networks that share parameters spatially. Multiply by using a number of layers of convolution, rather than a matrix of layers. In the process of image processing, each picture can be regarded as a "pancake", which includes the height of the picture, width and depth (that is, co

This chapter is a total of two parts, this is the second part:14th-cyclic neural networks (recurrent neural Networks) (Part I) chapter 14th-Cyclic neural networks (recurrent neural Networks) (Part II)14.4 Depth RNNStacking a multilayer cell is very common, as shown in 14-12, which is a depth rnn.Figure 14-12 Depth Rnn

0. Statement It was a failed job, and I underestimated the role of scale/shift in batch normalization. Details in the fourth quarter, please take a warning. First, the preface There is an explanation for the function of the neural network: It is a universal function approximation. The BP algorithm adjusts the weights, in theory, the neural network can approximate any function.Of course, to approximate the

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. Therefore, a more reasonable neural network structure is needed to effectively reduce the number of parameters in the

Learning notes TF057: TensorFlow MNIST, convolutional neural network, recurrent neural network, unsupervised learning, tf057tensorflow MNIST convolutional neural network. Https://github.com/nlintz/TensorFlow-Tutorials/blob/master/05_convolutional_net.py.TensorFlow builds a CNN model to train the MNIST dataset. Build a model. Define input data and pre-process data

1. OverviewWe have already introduced the earliest neural network: Perceptron. A very deadly disadvantage of the perceptron is that its linear structure, which can only make linear predictions (even if it does not solve the regression problem), is a point that was widely criticized at the time.Although the perceptual machine can not solve the nonlinear problem, it provides a way to solve the nonlinear problem. The limitation of the perceptron comes fr

Recently, the Google deep Mind team put forward a machine learning model, and a particularly tall on the name: Neural network Turing machine, I translated this article for everyone, translation is not particularly good, some sentences did not read clearly, welcome everyone to criticize Original paper Source: Http://arxiv.org/pdf/1410.5401v1.pdf.All rights reserved, prohibited reprint. Neural netw

Reprint please indicate the Source: Bin column, Http://blog.csdn.net/xbinworldThis is the essence of the whole fifth chapter, will focus on the training method of neural networks-reverse propagation algorithm (BACKPROPAGATION,BP), the algorithm proposed to now nearly 30 years time has not changed, is extremely classic. It is also one of the cornerstones of deep learning. Still the same, the following basic reading notes (sentence translation + their o

This is the essence of the whole fifth chapter, will focus on the training method of neural networks-reverse propagation algorithm (BACKPROPAGATION,BP), the algorithm proposed to now nearly 30 years time has not changed, is extremely classic. It is also one of the cornerstones of deep learning. Still the same, the following basic reading notes (sentence translation + their own understanding), the contents of the book to comb over, and why the purpose,