Welcome to the Neural Designer blog! Here you will find posts about artificial intelligence, machine learning and neural networks written by professional data scientists.
This tutorial describes different techniques for improving the generalization performance of a neural network....
This tutorial describes different techniques for the deployment of neural network models....
The applications of neural networks are uncountable, but most of them can be formulated as regression or classification problems. They...
Neural Designer's components are explained in detail in this article....
This User�s Guide aims to help you get the most out of the Neural Designer....
This tutorial will teach how to use neural networks, the most powerful mehtod for machine learning and artificial intelligence, to...
This tutorial shows the main training strategies used by neural networks to learn....
Integrate a Neural Designer model in Power BI....
Neural Designer is a software aimed at data scientists and experts in different fields who wish to exploit the benefits...
Design a predictive model neural network by following this 7 steps using Neural Designer...
A machine learning dataset collects data needed to create and train an approximation, classification, or forecasting model....
Auto-Associative Neural Networks (AANNs) are feed-forward neural networks with the aim to capture the input-output model from known data samples....
An adequate treatment of outliers is essential to achieve models with good generalization properties....
This post describes some of the most widely used training algorithms for neural networks. They are implemented in Neural Designer....
Advanced Analytics is the set of techniques used to discover intricate relationships, recognize complex patterns or predict current trends in...
An adequate treatment of outliers is essential to achieve models with good generalization properties....
One of the main difficulties in applying neural networks to real-world problems is that the data set often needs to...
This blog contains the description of 6 of the most important testing methods used in binary classification problems....
Neural Designer includes an advanced framework for feature selection. In this post, we describe the algorithms it contains....
The mathematics of artificial intelligence: the learning problem in neural networks...
Neural Designer is a powerful tool for building and analyzing neural network models. However, when working with these models, it...
The samples are the rows in the data matrix. A sample contains one or more features and possibly a label....
One of the main difficulties in applying neural networks to real-world problems is that the data set often needs to...
Prediction of pathway status based on gene expression....
Artelnics create the data science and machine learning platform Neural Designer; a company specialized in the development and application of...
A variable is any characteristic, number, or quantity that can be measured or counted. It is an attribute that describes...
Neural networks are the most crucial technique for machine learning and artificial intelligence. Mathematically, we can formulate the modelling process...
An adequate treatment of outliers is essential to achieve models with good generalization properties....
An adequate treatment of outliers is essential to achieve models with good generalization properties....
Advanced Analytics is the set of techniques used to discover intricate relationships, recognize complex patterns or predict current trends in...
The perceptron is the most important neuron model in the neural networks field. This article explains how this neuron model works....
Principal component analysis allows us to reduce the size of a data set without much loss of information. Read this...
The samples are the rows in the data matrix. A sample contains one or more features and possibly a label....
When building a model, it is imperative to know the ranges of all the variables. Statistics provide precious information. Indeed,...
Genetic algorithms are inspired by nature to select the most relevant features for a machine learning model....
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