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Jousef Murad
Daten Augmentieren mit Keras
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What's inside
Syllabus
Datenaugmentierung mit Keras
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Vermittelt Fertigkeiten und Kenntnisse, die für die berufliche Weiterentwicklung unerlässlich sind
Entwickelt Kernkompetenzen in der Datenaufbereitung mit Keras
Bietet eine umfassende Untersuchung der Datenaufbereitungstechniken
Vermittelt die praktische Anwendung von Datenaufbereitung für die Verbesserung von Machine-Learning-Modellen
Bietet Einblicke in die neuesten Entwicklungen in der Datenaufbereitung mit Keras
Save this course
Save Bilddaten Augmentieren mit Keras to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Bilddaten Augmentieren mit Keras with these
activities:
Keras Tutorials durchgehen
Show steps
Tutorial-Durchführung erhöht das Verständnis für Keras-Konzepte und -Funktionen, was die Anwendung im Kurs erleichtert.
Browse courses on
Keras
Show steps
-
Finde relevante Tutorials zu Keras-Grundlagen.
-
Bearbeite die Tutorials in Reihenfolge.
-
Notiere dir unbekannte Konzepte für weitere Recherchen.
Buch über Datenaugmentierung lesen
Show steps
Das Buch bietet umfassende Informationen zur Datenaugmentierung und vertieft das im Kurs Gelernte.
View
Deep Learning mit R und Keras
on Amazon
Show steps
-
Lies das Buch im Abschnitt über Datenaugmentierung.
-
Markiere wichtige Konzepte und Techniken.
-
Erstelle Zusammenfassungen von Schlüsselpunkten.
Datenaugmentierungs-Tutorial erstellen
Show steps
Das Erstellen eines Tutorials zwingt zur Synthese von Wissen und vertieft das Verständnis der Datenaugmentierung.
Show steps
-
Wähle einen bestimmten Aspekt der Datenaugmentierung aus.
-
Erstelle eine Schritt-für-Schritt-Anleitung oder ein Video.
-
Erkläre die Konzepte klar und prägnant.
One other activity
Expand to see all activities and additional details
Show all four activities
Datenaugmentierungs-Pipeline entwickeln
Show steps
Die Entwicklung einer Pipeline automatisiert Datenaugmentierungsprozesse und verbessert das Verständnis ihrer Implementierung.
Browse courses on
Pipeline
Show steps
-
Entwirf die Architektur der Pipeline.
-
Implementiere die Pipeline mit Keras oder einem anderen geeigneten Framework.
-
Teste und optimiere die Pipeline.
Keras Tutorials durchgehen
Show steps
Tutorial-Durchführung erhöht das Verständnis für Keras-Konzepte und -Funktionen, was die Anwendung im Kurs erleichtert.
Browse courses on
Keras
Show steps
Show steps
Show steps
- Finde relevante Tutorials zu Keras-Grundlagen.
- Bearbeite die Tutorials in Reihenfolge.
- Notiere dir unbekannte Konzepte für weitere Recherchen.
Buch über Datenaugmentierung lesen
Show steps
Das Buch bietet umfassende Informationen zur Datenaugmentierung und vertieft das im Kurs Gelernte.
View
Deep Learning mit R und Keras
on Amazon
Show steps
Show steps
Show steps
- Lies das Buch im Abschnitt über Datenaugmentierung.
- Markiere wichtige Konzepte und Techniken.
- Erstelle Zusammenfassungen von Schlüsselpunkten.
Datenaugmentierungs-Tutorial erstellen
Show steps
Das Erstellen eines Tutorials zwingt zur Synthese von Wissen und vertieft das Verständnis der Datenaugmentierung.
Show steps
Show steps
Show steps
- Wähle einen bestimmten Aspekt der Datenaugmentierung aus.
- Erstelle eine Schritt-für-Schritt-Anleitung oder ein Video.
- Erkläre die Konzepte klar und prägnant.
One other activity
Expand to see all activities and additional details
Show all four activities
Datenaugmentierungs-Pipeline entwickeln
Show steps
Die Entwicklung einer Pipeline automatisiert Datenaugmentierungsprozesse und verbessert das Verständnis ihrer Implementierung.
Browse courses on
Pipeline
Show steps
Show steps
Show steps
- Entwirf die Architektur der Pipeline.
- Implementiere die Pipeline mit Keras oder einem anderen geeigneten Framework.
- Teste und optimiere die Pipeline.
Career center
Learners who complete Bilddaten Augmentieren mit Keras will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their knowledge of data analysis, machine learning, and statistics to solve business problems. They work with businesses to identify their data needs and then develop solutions that use data to improve decision-making.
Keras is a powerful tool for data analysis and machine learning. It can be used to preprocess data, build machine learning models, and evaluate model performance. This makes it an ideal tool for Data Scientists who want to use data to solve business problems.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning systems. These systems can be used for a variety of purposes, such as image recognition, natural language processing, and speech recognition. Machine Learning Engineers use their knowledge of machine learning algorithms and software to develop systems that can learn from data and make predictions.
Keras is a popular framework for building machine learning models. It is easy to use and has a wide range of features. This makes it a good choice for Machine Learning Engineers who want to build high-performance machine learning systems.
Data Analyst
Data Analyst
Data Analysts use specialized computer software to analyze large amounts of complex data. The data may come from a variety of sources, such as surveys, experiments, or business transactions. Data Analysts use their analytical skills to identify patterns and trends in the data, and then they develop reports and presentations that communicate their findings to decision-makers.
Keras is a powerful tool for data analysis. It can be used to preprocess data, build machine learning models, and evaluate model performance. This makes it an ideal tool for Data Analysts who want to use data to solve business problems.
Software Engineer
Software Engineer
Software Engineers design, build, and maintain software systems. They work with businesses to identify their software needs and then develop solutions that meet those needs. Software Engineers use their knowledge of programming languages and software development tools to build high-quality software systems.
Keras is a popular framework for building machine learning models. It is easy to use and has a wide range of features. This makes it a good choice for Software Engineers who want to build machine learning systems.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. These pipelines are used to collect, clean, and store data. Data Engineers use their knowledge of data engineering tools and technologies to build pipelines that can handle large volumes of data. They also work with businesses to ensure that data pipelines are meeting their needs.
Keras is a popular framework for building machine learning models. It is easy to use and has a wide range of features. This makes it a good choice for Data Engineers who want to build machine learning systems that can handle large volumes of data.
Business Analyst
Business Analyst
Business Analysts work with businesses to identify their business needs and then develop solutions that meet those needs. They use their knowledge of business analysis techniques and tools to analyze business processes and identify areas for improvement.
Keras is a powerful tool for data analysis. It can be used to preprocess data, build machine learning models, and evaluate model performance. This makes it an ideal tool for Business Analysts who want to use data to solve business problems.
Product Manager
Product Manager
Product Managers work with businesses to develop and launch new products. They work with engineers, designers, and marketing teams to ensure that products meet the needs of customers. Product Managers use their knowledge of product development and management techniques to launch successful products.
Keras is a popular framework for building machine learning models. It is easy to use and has a wide range of features. This makes it a good choice for Product Managers who want to build machine learning systems that can be used to improve products.
Statistician
Statistician
Statisticians use mathematical and statistical models to analyze data. They use their models to draw conclusions about the population from which the data was collected. Statisticians use their knowledge of statistics and data analysis to help businesses make informed decisions.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They use their models to make predictions about the future performance of financial markets. Quantitative Analysts use their knowledge of mathematics, statistics, and finance to develop models that can help investors make informed investment decisions.
Keras is a powerful tool for data analysis. It can be used to preprocess data, build machine learning models, and evaluate model performance. This makes it an ideal tool for Quantitative Analysts who want to use data to make predictions about the future performance of financial markets.
Risk Analyst
Risk Analyst
Risk Analysts use data to identify and assess risks. They use their assessments to help businesses make informed decisions about how to manage risk. Risk Analysts use their knowledge of risk management techniques and tools to develop models that can help businesses identify and assess risks.
Data Journalist
Data Journalist
Data Journalists use data to tell stories. They work with data to identify trends and patterns and then write articles and create visualizations that communicate their findings to the public.
Keras is a powerful tool for data analysis. It can be used to preprocess data, build machine learning models, and evaluate model performance. This makes it an ideal tool for Data Journalists who want to use data to tell stories.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. They use their models to optimize business processes and improve efficiency. Operations Research Analysts use their knowledge of operations research techniques and tools to develop models that can help businesses make better decisions.
Market Researcher
Market Researcher
Market Researchers use data to understand consumer behavior. They use their research to help businesses develop new products and marketing campaigns. Market Researchers use their knowledge of marketing research techniques and tools to conduct surveys, focus groups, and other research activities.
Keras is a powerful tool for data analysis. It can be used to preprocess data, build machine learning models, and evaluate model performance. This makes it an ideal tool for Market Researchers who want to use data to understand consumer behavior.
Data Architect
Data Architect
Data Architects design and build data systems. They work with businesses to identify their data needs and then develop systems that can meet those needs. Data Architects use their knowledge of data architecture techniques and tools to build systems that can store and manage large volumes of data.
Database Administrator
Database Administrator
Database Administrators maintain and manage database systems. They work with businesses to ensure that database systems are running smoothly and efficiently. Database Administrators use their knowledge of database administration techniques and tools to maintain databases and ensure that data is secure.
For more career information including salaries, visit:
OpenCourser.com/course/j93wk9/bilddaten
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Bilddaten Augmentieren mit Keras.
Dieses Buch bietet einen umfassenden Überblick über Deep Learning mit Python und behandelt dabei auch Datenaugmentation mit Keras.
Es ist besonders nützlich als Begleitlektüre zum Kurs, da es Hintergrundwissen und praktische Beispiele liefert.
Dieses Buch bietet einen umfassenden Überblick über Machine Learning mit TensorFlow 2 und Keras und behandelt dabei auch Datenaugmentation als wichtiges Thema.
Es ist besonders nützlich als Begleitlektüre zum Kurs, da es Hintergrundwissen und praktische Beispiele liefert.
Dieses Buch bietet einen Überblick über künstliche Intelligenz mit Python und behandelt dabei auch Datenaugmentation.
Es ist eine gute Wahl für Anfänger, die ihr Wissen über KI erweitern möchten.
Dieses Buch konzentriert sich auf Deep Learning für die medizinische Bildanalyse und behandelt dabei auch Datenaugmentation für medizinische Bilddaten.
Es ist eine wertvolle Ressource für diejenigen, die sich für medizinische Bildgebung interessieren.
Save
Dieses Buch ist ein Standardwerk in der Deep Learning-Community und deckt auch Generative Adversarial Networks (GANs) ab, die eng mit Datenaugmentation verbunden sind.
Es ist eine wertvolle Ressource für diejenigen, die ein tieferes Verständnis von GANs anstreben.
Dieses Buch bietet einen praktischen Überblick über Machine Learning mit Scikit-Learn, Keras und TensorFlow und behandelt dabei auch Datenaugmentation.
Es ist eine gute Wahl für Anfänger, die ihr Wissen über Machine Learning erweitern möchten.
Dieses Buch konzentriert sich auf Deep Learning mit Python und TensorFlow und behandelt dabei auch Datenaugmentation.
Es ist eine gute Wahl für Anfänger, die ihr Wissen über Deep Learning erweitern möchten.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/j93wk9/bilddaten
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Effort
1 Stunde
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Jousef Murad
Language
Deutsch
Good to know
Vermittelt Fertigkeiten und Kenntnisse, die für die berufliche Weiterentwicklung unerlässlich sind
Entwickelt Kernkompetenzen in der Datenaufbereitung mit Keras
Bietet eine umfassende Untersuchung der Datenaufbereitungstechniken
Vermittelt die praktische Anwendung von Datenaufbereitung für die Verbesserung von Machine-Learning-Modellen
Bietet Einblicke in die neuesten Entwicklungen in der Datenaufbereitung mit Keras
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