Practical Aspects of Forming Training/Test Samples for Convolutional Neural Networks

Abstract

The most common approaches to assessing the quality of training neural networks in the context of the problem of "small training sets" are analyzed. A review of the code implementation of the most universal approaches and ways of extending training/testing samples is carried out. The logic of the work of STN-module is analyzed. It can be inserted into existing convolutional architectures, giving neural networks the ability to actively spatially transform feature maps, conditional on the feature map itself, without any extra training supervision or modification to the optimization process.

Modeling and forecasting of stock market processes

“…Methods of approximating and modeling stock indices using a Wiener random process serve as the basis for intelligent data analysis using machine learning. The main goal of this analysis is to predict the price of the test data depending on real quotes over time, taking into account the errors of real prediction [6,7], [8,9], [10,11], [12,13], [14,15], [16,17], [18,19], [20].…”

“…With this in mind, the Python library Keras stands out as a powerful tool for building neural networks, in particular multilayer architectures such as LSTM (Long Short-Term Memory) [9, 11], [21,22], [23,24], [25]. LSTMs are one of the most efficient types of recurrent neural networks that are specifically designed for analyzing sequential data, such as time series, which is typical for market data [19,20], [21,22], [23,24], [25,26], [27,28], [29,30]. Therefore, for more efficient analysis, it is necessary to have an LSTM neural network architecture consisting of at least several layers, especially if the amount of information is large.…”

Information system for analyzing publicsentiment in web platforms based on machine learning

“…In some cases, statistical characteristics such as variance, centrality, and histograms of the distribution of node degrees are also important. Patterns of interconnections in a "self-centered network" can reflect the position, social or professional activities of an actor [16]. For example, managers and administrators may share a "bridge" pattern that connects subgroups in an organization.…”