Neural Network Training Data

In this article, I want to share with you 8 neural network architectures. I believe that any machine learning researcher should be familiar with this process to promote their work.

Some tasks are more complicated to code directly. We can't handle all the nuances and simple coding. Therefore, machine learning is necessary. Instead, we provide a large amount of data to machine learning algorithms, allowing the algorithm to continuously explore the data and build models to solve the problem.

"Editor's note" Deep convolution neural network has a wide range of application scenarios, in this paper, the deep convolution neural network deep CNNs multi-GPU model parallel and data parallel framework for the detailed sharing, through a number of worker group to achieve data parallelism, the same worker Multiple worker implementation models in a group are parallel. In the framework, the three-stage parallel pipelined I/O and CPU processing time are implemented, and the model parallel engine is designed and implemented, which improves the execution efficiency of the model parallel computation, and solves the data by transmits layer ...

Learning methods depending on the type of data, there are different ways to model a problem. In the field of machine learning or artificial intelligence, people first consider the way of learning algorithms. In the field of machine learning, there are several main ways of learning. 　　It is a good idea to classify the algorithm according to the learning style, so that people can choose the most suitable algorithm according to the input data to get the best results when modeling and algorithm selection. Supervised learning: Under supervised learning, input data is called "training data", each group training number ...

Absrtact: The neural network model makes the data more potential we all know that in the mass data age, deep learning has brought new opportunities for artificial intelligence. These opportunities are concentrated in three places: text, pictures, and speech recognition. Wunda mentioned that artificial intelligence has a neural network model to make the data more potential. We all know that in the age of mass data, deep learning offers new opportunities for artificial intelligence. These opportunities are concentrated in three places: text, pictures, and speech recognition. Wunda mentioned that artificial intelligence has a positive cyclic chain. Have a good product, but ...

Similarity Chinese character recognition based on deep neural network in large data Charles Tau Zhang Shuye Jin Lianwen The traditional handwritten Chinese character recognition system (SHCCR) is limited by the feature extraction method, and a deep neural network (DNN) is used to identify the automatic learning of similar Chinese characters. This paper introduces the method of similar character set generation and the specific structure of deep neural network for similar Chinese character recognition, and studies the influence of different training data scale on recognition performance. Experiments show that DNN can effectively carry out feature learning, avoid the lack of artificial design features, and traditional based on gradient features ...

The research of BP Neural network genetic algorithm based on MapReduce in nonlinear system identification Chen Chunping Chaya champion Shandandan often encounter complex s nonlinear systems in engineering applications, which are complex and difficult to model accurately by mathematical methods. The three-layer BP neural network can approximate continuous function with arbitrary precision. But the BP network has the disadvantage of falling into the local optimal value, and then the genetic algorithm is added. The traditional training method of the serial BP neural network has great problem when processing the massive data.

Now we are in an era of big data, but I think everyone is very clear now that this big data does not mean really great value. To get the value in the data, we must conduct effective data analysis. Today, we have to use computer to analyze data, we must have machine learning.

Luoma classification of water quality by parallel BP Neural network under Hadoop Shungua Shao Xiaogen Bao Xu Delan Wang Hai Study The advantages of cloud computing to data migration mechanism and mapreduce parallel processing of massive data, to solve the problem of BP neural network in processing large sample data, The bottleneck problem of the network training time is long. This paper constructs a network model of multiple pollution factors affecting the Luoma water quality, and uses parallel BP network algorithm under Hadoop to realize the classification of Luoma water quality and the results of mining analysis have decision support for Luoma water quality optimization and ecological restoration.

C + + computer vision ccv-based on C language/provides cache/core machine Vision Library, novel Machine Vision Library opencv-It provides C + +, C, Python, Java and MATLAB interfaces, and supports Windows, Linux, Android and Mac OS operating system. General machine learning Mlpack dlib Ecogg Shark Closure Universal machine learning Closure Toolbox-cloj ...