In the previous article Neural Network in Practice: Pseudoinverse (II), I discussed the importance of dedicated computational systems and the reasons behind their development. In this new article related to neural networks, we will delve deeper into the subject. Creating material for this stage is no simple task. Despite appearing straightforward, explaining something that often causes significant confusion can be quite challenging.
What will we cover at this stage? In this
more...The HiVT method begins by representing the road scene as a collection of vectorized elements. Based on this scene representation, the model hierarchically aggregates spatiotemporal information. The road scene consists of agents and map information. For structured scene representation, vectorized elements are first extracted, including trajectory segments of road agents and lane segments from map data.
We have released the most comprehensive guide to MQL5 programming, authored by experienced algorithmic trader Stanislav Korotky with MetaQuotes' support.
The book is intended for programmers of all levels. Beginners will learn the fundamentals as the book introduces key development tools and basic programming concepts. With this material, you can create, compile, and run your first application in the MetaTrader 5 trading platform. Users with experience in other programming languages can immediately
more...This article continues the series on how the MQL5 wizard is ideal for rapid testing and prototyping ideas for traders. For a lot of people developing expert advisers and trade systems, the need to keep learning and be abreast with trends in not just machine learning but trade & risk management in general is important. We therefore consider within these series how the MQL5 IDE is useful in this regard by not only saving time but also minimizing coding errors.
Principal Component Analysis (PCA) is the focusing on only the ‘principal components’ among the many dimensions of a data set, such that one is reducing the dimensions of that data set by ignoring the ‘non-principal’ parts.
PCA though, with eigen values & vectors, take on a slightly deeper approach. Typically, data sets that are handled under PCA are in a matrix format and the principal components, that are sought from a matrix would be a single vector column (or row) that is
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