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A report released by the Institute for Supply Management on Monday showed an unexpected increase by its reading on U.S. service sector activity in the month of April. The ISM said its services PMI rose to 51.6 in April from 50.8 in March, with a reading above 50 indicating growth. Economists had expected the index to edge down to 50.6.

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We have now expanded signal accessibility on our model, making it beneficial for all users. Additionally, we have motivated numerous upcoming developers on how to seamlessly integrate social networks for signals on our well-known MetaTrader 5 trading platform. Let's conclude by delving into WhatsApp integration details. Our goal is to automatically send signals generated by our custom MetaTrader 5 indicators to a WhatsApp number or group. Meta has introduced a new channel feature on WhatsApp,

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In our previous article, we laid the foundation by assembling the graphical elements of our MetaQuotes Language 5 (MQL5) graphical user interface (GUI) panel. If you recall, the iteration was a static assembly of GUI elements - a mere snapshot frozen in time, lacking responsiveness. It was static and unyielding. Now, let’s unfreeze that snapshot and infuse it with life. In this eagerly anticipated continuation, we’re taking our panel to the next level.

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We revisit a form of neural network we had considered in an earlier article by dwelling on one specific hyperparameter. The learning-rate. The Generative Adversarial Network is a neural network that operates in pairs, where one network is trained traditionally to discern the truth, while another is trained to discern the former’s projections from real occurrences. This duality does imply that the traditionally trained network (the former) is trying to fool the latter and this is true, however

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Let's get acquainted with a new model family: Ordinary Differential Equations. Instead of specifying a discrete sequence of hidden layers, they parameterize the derivative of the hidden state using a neural network. The results of the model are calculated using a "black box", that is, the Differential Equation Solver. These continuous-depth models use a constant amount of memory and adapt their estimation strategy to each input signal. Such models were first introduced in the paper "Neural Ordinary Differential Equations".

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