WEEKLY DIGEST 2014, October 19 - 26 for Neural Networks in Trading & Everywhere
Inspired by the article about Neural Networks and made an EA based of that. The article on mql5.com
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Webinar "Trading with Artificial Neural Networks". The video
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Japanese scientists have managed to “bypass” inoperative neural pathways. Article.
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Computer scientists can predict the price of Bitcoin. mql5 artricle
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Neural networks draw on context to improve machine translation.
Dutch researchers have improved the output of a
statistical machine translation system by examining the context in which
words are found.
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Dimensionality Reduction using Principal Component Analysis and Self Organizing Maps.
The curse of dimensionality
is the phenomena whereby an increase in the dimensionality of a data
set results in exponentially more data being required to produce a
representative sample of that data set. This phenomena has significant
implications for all information gained through inference techniques such as those found in statistics, data mining, and artificial intelligence.
To combat the curse of dimensionality, numerous linear and non-linear dimensionality reduction techniques have been developed. These techniques aim to reduce the number of dimensions (variables) in a data set through either feature selection or feature extraction without significant loss of information. Feature extraction is the process of transforming the original data set into a data set with fewer dimensions. Two well known, and closely related, feature extraction techniques are Principal Component Analysis (PCA) and Self Organizing Maps (SOM).
