lstm neural network

It took a week to learn about neural networks after soy sauce in the Knowledge Engineering Center. The teacher arranged a question and asked me to try it. I did a little simple. I conducted several groups of tests and wrote a summary report. I posted it here. After more than a week of experimentation, I have a simple understanding of this issue. The following is my thoughts on this issue. In the last two days, I suddenly felt that the problem was much

BP (Back Propagation) network is a multi-layer feed-forward Network trained by the error inverse propagation algorithm, which was proposed by a team of scientists led by Rumelhart and mccelland in 1986, it is one of the most widely used neural networks. The BP network can learn and store a large number of input/output

This paper is reproduced from http://blog.csdn.net/ironyoung/article/details/49455343 BP (backward propogation) neural networkSimple to understand, neural network is a high-end fitting technology. There are a lot of tutorials, but in fact, I think it is enough to look at Stanford's relevant learning materials, and there are better translations at home: Introdu

Generalized regression neural network GRNN (General Regression neural Network) Generalized regression Neural network is an improvement based on radial basis function neural

Series PrefaceReference documents: Rnnlm-recurrent Neural Network Language Modeling Toolkit (click here to read) Recurrent neural network based language model (click here to read) EXTENSIONS of recurrent neural NETWORK LAN

Recurrent neural network language modeling toolkit source code (8), recurrentneuralReferences: RNNLM-Recurrent Neural Network Language Modeling Toolkit (Click here to read) Recurrent neural network based language model (read he

Although the research and application of neural network has been very successful, but in the development and design of the network, there is still no perfect theory to guide the application of the main design method is to fully understand the problem to be solved on the basis of a combination of experience and temptation, through a number of improved test, finall

Deep learning "engine" contention: GPU acceleration or a proprietary neural network chip?Deep Learning (Deepin learning) has swept the world in the past two years, the driving role of big data and high-performance computing platform is very important, can be described as deep learning "fuel" and "engine", GPU is engine engine, basic all deep learning computing platform with GPU acceleration. At the same tim

, database storage of things more, a lot of things are known to know do not know what. Second, the database index is fast and complete, according to a thing can quickly associate with the principle of its occurrence. Third, the sensory ability is strong, palpation all sharp. That's what makes Sherlock Holmes.Because I know so much, so when I see a paper that blends decision-making forests with convolutional neural networks, I feelsomething is more clo

Objective From the understanding of convolution nerves to the realization of it, before and after spent one months, and now there are still some places do not understand thoroughly, CNN still has a certain difficulty, not to see which blog and one or two papers on the understanding, mainly by themselves to study, read the recommended list at the end of the reference. The current implementation of the CNN in the Minit data set effect is good, but there are some bugs, because the recent busy, the

This chapter does not involve too many neural network principles, but focuses on how to use the Torch7 neural networkFirst require (equivalent to the C language include) NN packet, the packet is a dependency of the neural network, remember to add ";" at the end of the statem

Deep Learning Neural Network pure C language basic Edition 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 computational workload rather t

Original page: Visualizing parts of convolutional neural Networks using Keras and CatsTranslation: convolutional neural network Combat (Visualization section)--using Keras to identify cats It is well known, that convolutional neural networks (CNNs or Convnets) has been the source of many major breakthroughs in The fiel

Series PrefaceReference documents: Rnnlm-recurrent Neural Network Language Modeling Toolkit (click here to read) Recurrent neural network based language model (click here to read) EXTENSIONS of recurrent neural NETWORK LAN

In the original: "Bi thing" Microsoft neural network algorithmThe Microsoft Neural Network is by far the most powerful and complex algorithm. To find out how complex it is, look at the SQL Server Books Online description of the algorithm: "This algorithm establishes a classification and regression mining model by estab

TravelseaLinks: https://zhuanlan.zhihu.com/p/22045213Source: KnowCopyright belongs to the author. Commercial reprint please contact the author for authorization, non-commercial reprint please specify the source.In recent years, the Deep convolutional Neural Network (DCNN) has been significantly improved in image classification and recognition. Looking back from 2014 to 2016 of these two years more time, has

This article is mainly for you to introduce the Python implementation of Neural Network (BP) algorithm and simple application, with a certain reference value, interested in small partners can refer to In this paper, we share the specific code of Python to realize the neural network algorithm and application, for your