September 2024
Intermediate to advanced
294 pages
9h 48m
German
Content preview from Skalierung von Machine Learning mit Spark
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Kapitel 6. Modelle mit Spark MLlib trainieren
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Nachdem du nun gelernt hast, wie man Experimente zum maschinellen Lernen verwaltet, ein Gefühl für die Daten bekommt und Features entwickelt, ist es an der Zeit, einige Modelle zu trainieren.
Was bedeutet das genau? Beim Trainieren eines Modells werden die Modellparameter so angepasst oder verändert, dass sich seine Leistung verbessert. Die Idee dabei ist, dein maschinelles Lernmodell mit Trainingsdaten zu füttern, die ihm beibringen, wie es eine bestimmte Aufgabe lösen kann - zum Beispiel ein Objekt auf einem Foto als Katze zu klassifizieren, indem es seine "Katzen"-Eigenschaften identifiziert.
In diesem Kapitel erfährst du, wie Algorithmen für maschinelles Lernen funktionieren, wann du welches Tool verwenden solltest, wie du dein Modell validierst und vor allem, wie du den Prozess mit der Spark MLlib Pipelines API automatisieren kannst.
In diesem Kapitel geht es im Wesentlichen um Folgendes:
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Grundlegende Algorithmen für maschinelles Lernen in Spark
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Überwachtes maschinelles Lernen mit Spark Machine Learning
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Unüberwachtes maschinelles Lernen mit Spark Machine Learning
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Dein Modell evaluieren und testen
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Hyperparameter und Abstimmung deines Modells
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Verwendung von Spark-Pipelines für maschinelles Lernen
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Persistieren von Modellen und Pipelines auf der Festplatte
Algorithmen
Beginnen wir ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.