Unterschiede

Hier werden die Unterschiede zwischen zwei Versionen angezeigt.

--- p:ki:fische4 [2024/05/02 08:57] – Tscherter Vincent
+++ p:ki:fische4 [2026/03/29 11:28] (aktuell) – Ralf Kretzschmar
@@ Zeile 1: / Zeile 1: @@
-Navigation: [[:p:ki:fische1|🐟 Teil 1]] - [[:p:ki:fische2|🐟 Teil 2]] - [[:p:ki:fische3|🐟 Teil 3]] - [[:p:ki:fische4|🐟 Teil 4]] {{gem/mgr}}{{ gem/pageinfo}}
+Navigation: [[:p:ki:fische1|🐟 Teil 1]] - [[:p:ki:fische2|🐟 Teil 2]] - [[:p:ki:fische3|🐟 Teil 3]] - [[:p:ki:fische4|🐟 Teil 4]] - [[:p:ki:fische_ki|🐟 Fische für ChatGPT?]] {{gem/mgr}}{{ gem/pageinfo}}
 ====== 🐟 Künstliche Intelligenz für echte Fische 4 ======
@@ Zeile 5: / Zeile 5: @@
 🎯  In dieser Reihe erfährst du, wie eine künstliche Intelligenz automatisiert Daten auswertet und beurteilt. Dazu begibst du dich auf raue See.
+\\
 ~~INTOC~~
@@ Zeile 10: / Zeile 11: @@
 ===== -#1 Was bisher geschah... =====
-👩‍🦰 Du hilfst Sigrún beim Bau eines Fischsortierapparats der Hering von Lodde unterscheiden kann. Als Herzstück hast du dafür ein neuronalen Netzes trainiert. Das Neuronale Netz erkennt rund 90% der Fische richtig, jedoch ist das immer noch zu schlecht um Sigrún die Arbeit abnehmen zu können.
+👩‍🦰 Du hilfst Sigrún beim Bau eines Fischsortierapparats der Hering von Lodde unterscheiden kann. Als Herzstück hast du dafür ein neuronalen Netzes trainiert. Das Neuronale Netz erkennt rund 90% der Fische richtig, jedoch ist das immer noch zu schlecht, um Sigrún die Arbeit abnehmen zu können.
 😢 Musst du aufgeben?
 \\
-[{{ p:pasted:classoverlap.png?185px|Überlappende Klassen((eigene Darstellung, [[https://creativecommons.org/publicdomain/zero/1.0/deed.de|CC0 1.0]])) }}]
+<figure right>{{p:pasted:classoverlap.png?185px}}<caption>Überlappende Klassen((eigene Darstellung, [[https://creativecommons.org/publicdomain/zero/1.0/deed.de|CC0 1.0]])) </caption></figure>
 ===== - Das Problem der überlappenden Klassen =====
@@ Zeile 40: / Zeile 41: @@
 🤔 Aber wie bringen wir unser neuronales Netz dazu zu erkennen, ob ein Sample in einer Region überlappender Klassen liegt, oder nicht?
-Zur Erinnerung, unser Neuronales Netz sollte den Ausgangswert ''0'' für Heringe (blaue Kreuze) und ''1'' für Lodde (grüne Kreise) produzieren. Unser Threshold war 0.5. Ein Fisch mit Ausgangswert < 0.5 wurde als Hering, ein Fisch mit Ausgangswert ≥ 0.5 wurde als Lodde klassifiziert.
+Zur Erinnerung, unser Neuronales Netz sollte den Ausgangswert ''0'' für Heringe (blaue Kreuze) und ''1'' für Lodde (grüne Kreise) produzieren. Unser Grenzwert war 0.5. Ein Fisch mit Ausgangswert < 0.5 wurde als Hering, ein Fisch mit Ausgangswert ≥ 0.5 wurde als Lodde klassifiziert.
-💡 Neu machst du Folgendes. Du wählst einen anderen Threshold (im Programm 0.3). Wenn nun der Ausgangswert kleiner als dieser Threshold ist (< 0.3), dann landet der Fisch im Hering-Korb. Wenn der Ausgangswert grösser als Eins minus dieser Threshold ist (also > 1 - 0.3, d.h. > 0.7) dann landet der Fisch im Lodde-Korb. Alle anderen Fische (Ausgangswerte 0.3 - 0.7) werden verworfen und landen somit in einem dritten Korb, der entweder von Hand sortiert oder als "gemischter Fisch" verkauft wird.
+💡 Neu machst du Folgendes. Du wählst einen anderen Grenzwert (im Programm 0.3). Wenn nun der Ausgangswert kleiner als dieser Grenzwert ist (< 0.3), dann landet der Fisch im Hering-Korb. Wenn der Ausgangswert grösser als Eins minus dieser Grenzwert ist (also > 1 - 0.3, d.h. > 0.7) dann landet der Fisch im Lodde-Korb. Alle anderen Fische (Ausgangswerte 0.3 - 0.7) werden verworfen und landen somit in einem dritten Korb, der entweder von Hand sortiert oder als "gemischter Fisch" verkauft wird.
 <WRAP center round box >
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 🚨 Training manuell stoppen: In die Grafik klicken und ''ESC'' drücken.
   - Starte mehrfach das Programm. Das Ziel ist es, möglichst wenige Fische zu verwerfen, aber gleichzeitig die anderen Fische zu 100 % korrekt zu trennen. Der Prozentwert bei ''🗑️'' gibt dir an, welcher Anteil der Fische verworfen wird. Der Prozentwert bei ''✔️'' gibt dir an, wie viele der anderen Fische korrekt klassifiziert werden.
-  - Spiele mit den Parametern ''LERNRATE'', ''ANZAHL_HIDDEN_NEURONS'', ''ANZAHL_EPOCHEN'' und dem ''THRESHOLD''. Trage dein bestes Ergebnis (%-verworfen bei 100 % korrekt) in das nachfolgende Textfeld ein. Was bedeutet dein Ergebnis für Sigrún? {{gem/plain?0=N4XyA#4029d46d442013a7}}
+  - Spiele mit den Parametern ''LERNRATE'', ''ANZAHL_HIDDEN_NEURONS'', ''ANZAHL_EPOCHEN'' und dem ''GRENZWERT''. Trage dein bestes Ergebnis (%-verworfen bei 100 % korrekt) in das nachfolgende Textfeld ein. Was bedeutet dein Ergebnis für Sigrún? {{gem/plain?0=N4XyA#4029d46d442013a7}}
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 </WRAP>
@@ Zeile 64: / Zeile 65: @@
 💡 Die Klassifikation von Fischen ist nicht nur für den Fischfang von Bedeutung. Immer mehr Regierungen fühlen sich dazu verpflichtet, ein Gleichgewicht zwischen Ökosystem und Fischfang aufrechtzuerhalten. Dazu werden auch Unterwasserroboter eingesetzt, welche Fische und Fischschwärme fotografieren, um Rückschlüsse bezüglich Fischvorkommen und insbesondere gefährdeten Fischarten ziehen zu können. Basierend auf diesen Informationen können Fangkontingente ausgesprochen werden.
-🐟 Einen Überblick über die aktuelle Forschung zur Fischklassifizierung gibt z.B. die englischsprachige Publikation [[https://www.sciencedirect.com/science/article/pii/S1319157820304195|A survey on fish classification techniques]] von Mutasem K. Alsmadi und Ibrahim Almarashdeh((Mutasem K. Alsmadi, Ibrahim Almarashdeh, "A survey on fish classification techniques", Journal of King Saud University - Computer and Information Sciences, Volume 24, Issue 5, Pages 1625-1638, May 2022 https://doi.org/10.1016/j.jksuci.2020.07.005 [aufgerufen am 14.12.2023].)).
+🐟 Einen Überblick über die Forschung zur Fischklassifizierung gibt z.B. die englischsprachige Publikation [[https://www.sciencedirect.com/science/article/pii/S1319157820304195|A survey on fish classification techniques]] von Mutasem K. Alsmadi und Ibrahim Almarashdeh((Mutasem K. Alsmadi, Ibrahim Almarashdeh, "A survey on fish classification techniques", Journal of King Saud University - Computer and Information Sciences, Volume 24, Issue 5, Pages 1625-1638, May 2022 https://doi.org/10.1016/j.jksuci.2020.07.005 [aufgerufen am 14.12.2023].)).
+\\
+===== - Die Fortsetzung ======
+Im Kapitel [[:p:ki:fische_ki|🐟 Fische für ChatGPT?]] vergleichst du das neuronale Netz für die Fische mit modernen KI-Chatbots und überlegst dir, was du daraus für KI im Allgemeinen lernen kannst.
 \\
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