mql5

The idea is to take repeated steps in the opposite direction of the gradient (or approximate gradient) of the function at the current point, because this is the direction of steepest descent. Conversely, stepping in the direction of the gradient will lead to a local maximum of that function; the procedure is then known as gradient ascent.

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• Data Science and Machine Learning (Part 01): Linear Regression
• Data Science and Machine Learning (Part 02): Logistic Regression
• Data Science and Machine Learning part 03: Matrix Regressions
• Data Science and Machine Learning (Part 04): Predicting Current Stock Market Crash
• Data Science and Machine Learning (Part 05): Decision Trees
• Data Science and Machine Learning (Part 06): Gradient Descent

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