Neural Networks & Advanced Analytics Blog
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The procedure used to carry out the learning process in a neural network is called training algorithm. The learning problem in neural networks is formulated in terms of minimization of a loss function, F. For that, there are many different algorithms in order to get the best possible results.
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The genetic algorithm is a stochastic method for function optimization based on the mechanics of natural genetics and biological evolution. In this article we show how genetic algorithms can be applied to optimize the performance of a predictive model, by selecting the most relevant features.
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Sales forecasting is an essential task for the management of a store. During this article we are going to use the information about the sales of a drug store from the last two years in order to predict the amount of sales that it is going to have one week in advance.
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In this article, we explain principal components analysis. It is a statistical technique that allows to identify underlying linear patterns in a data set so it can be expressed in terms of other data set of significatively lower dimension without much loss of information.
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In this article, our objective is to analyze a dataset from a grocery store in order to create a recommendation system. This system will be capable of generating accurate recommendations about products that the user may have an interest in.
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An outlier is a data point that is distant from other similar points. They may be due to variability in the measurement or may indicate experimental errors. Along this article, we are going to talk about 3 different methods of dealing with outliers.
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There are many different types of neural networks, from which the multilayer perceptron is the most important one. The characteristic neuron model in the multilayer perceptron is the so called perceptron. In this article we will explain the mathematics on this neuron model.
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Depending on the type of problem that we are analyzing, there are some specific methods that may help us to study in depth the performance of the predictive model. In this article, we will focus on the 6 most used testing analysis methods for binary classification problems.
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Time series forecasting is an important area of machine learning. There are so many prediction problems that involve a time component, however it is a hard task to deal with this kind of series due to the time factor. Here you can find a complete introduction that will allow you to solve this type of problems.
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Forecasting of power demand plays an essential role in the electric industry. It provides the basis for making decisions in power system planning and operation. The objective of this article is to explain all the factors that lead to demand change and to determine the underlying causes.
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Nowadays, the risk assessment process carried out by insurance companies has became obsolete. By analyzing the available information on the customers stored by the company, we can develop risk models that evaluate new customers in a faster and more accurate fashion.
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Advanced Analytics is the set of techniques used to discover intricate relationships, recognize complex patterns or predict current trends in your data. Its objective is to model data from internal and external variables in order to obtain useful insights that results in smarter decisions and better business results.
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Telemarketing is a form of direct marketing that is widely used by all types of companies. This technique can be extremely powerful at generating sales, but it requires a strict selection of potential clients. Advanced Analytics allows us to select individual targets, which results in increased profitability.
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PMML is an XML-based format that provides portability of predictive models among various software tools. It allows data scientists to design and deploy models in a more flexible way, so more software tools can make use of them.
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Predictive analytics extracts information from data sets in order to discover complex relationships, recognize unknown patterns, forecasting actual trends, find associations, etc. This allows us to anticipate the future and make the right decisions.
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Our tool Neural Designer provides innovative algorithms to automate the model selection process. In this post, we will explain some basic ideas about model selection and the algorithms implemented in this software.
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The data used for this challenge are simulated data provided by the ATLAS experiment at CERN. Physicists use them to optimize the analysis of the Higgs boson. The database has been downloaded from Kaggle.
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Nowadays, engine manufacturers are trying to implement strategies that provide the best efficiency and are as clean as possible. Deep learning can be used to model the behavior of an engine, based on the most important operational variables. That model can be used for different purposes, which include performance optimization or predictive maintenance.
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The R&D project "Advanced predictive analytics system with cloud computing (Neural Designer)", as part of the NEOTEC program for the consolidation of technology-based companies of the Center for Industrial Technological Development (CDTI), funded by the Government of Spain has concluded.
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One of the biggest difficulties for those patiens with Parkinson’s disease is the requisite physical visit to the clinic in order to monitoring and treatment. The main goal of this study is to predict the unified Parkinson’s disease rating scale (UPDRS) for classifying the stage of the disease, by remote telemonitoring of the patients.
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