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Encoder-Decoder Architecture - Deutsch

Google Cloud Training

Dieser Kurs vermittelt Ihnen eine Zusammenfassung der Encoder-Decoder-Architektur, einer leistungsstarken und gängigen Architektur, die bei Sequenz-zu-Sequenz-Tasks wie maschinellen Übersetzungen, Textzusammenfassungen und dem Question Answering eingesetzt wird. Sie lernen die Hauptkomponenten der Encoder-Decoder-Architektur kennen und erfahren, wie Sie diese Modelle trainieren und bereitstellen können. Im dazugehörigen Lab mit Schritt-für-Schritt-Anleitung können Sie in TensorFlow von Grund auf einen Code für eine einfache Implementierung einer Encoder-Decoder-Architektur erstellen, die zum Schreiben von Gedichten dient.

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Syllabus

Encoder-Decoder-Architektur: Überblick
Dieses Modul vermittelt Ihnen eine Zusammenfassung der Encoder-Decoder-Architektur, einer leistungsstarken und gängigen Architektur, die bei Sequenz-zu-Sequenz-Tasks wie maschinellen Übersetzungen, Textzusammenfassungen und dem Question Answering eingesetzt wird. Sie lernen die Hauptkomponenten der Encoder-Decoder-Architektur kennen und erfahren, wie Sie diese Modelle trainieren und bereitstellen können. Im dazugehörigen Lab mit Schritt-für-Schritt-Anleitung können Sie in TensorFlow von Grund auf einen Code für eine einfache Implementierung einer Encoder-Decoder-Architektur erstellen, die zum Schreiben von Gedichten dient.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Taught by Google Cloud Training, industry leaders in the field
Suitable for beginners looking to build a foundation in Encoder-Decoder Architectures
Covers the core concepts of Encoder-Decoder Architectures, a powerful technique used in natural language processing tasks like machine translation and text summarization
Provides hands-on practice with a step-by-step lab to implement an Encoder-Decoder Architecture for poetry generation in TensorFlow

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Review Encoder-Decoder Architectures
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  • Read the provided course materials on Encoder-Decoder Architectures.
  • Review online tutorials and articles on Encoder-Decoder Architectures.
  • Complete practice exercises to test your understanding of Encoder-Decoder Architectures.
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  • Conduct a thorough search for resources related to Encoder-Decoder Architectures.
  • Organize and categorize the resources in a logical manner.
  • Document the sources and provide proper citations.
  • Share the resource collection with others.
Participate in Peer Group Discussions
Engage in discussions with peers to exchange knowledge and perspectives on Encoder-Decoder Architectures.
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  • Join or create a study group with other learners in this course.
  • Regularly meet with the group to discuss course materials and concepts.
  • Share your understanding and insights with others.
  • Seek clarification and support from your peers.
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Practice Training Encoder-Decoder Models
Practice training Encoder-Decoder models in TensorFlow to enhance your practical skills.
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  • Set up a TensorFlow environment.
  • Load and prepare the necessary data for training.
  • Build an Encoder-Decoder model using TensorFlow.
  • Train the Encoder-Decoder model.
  • Evaluate the performance of the trained model.
Mentor Junior Learners
Reinforce your understanding by mentoring junior learners and sharing your knowledge of Encoder-Decoder Architectures.
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  • Identify opportunities to mentor junior learners, such as through online forums or study groups.
  • Provide guidance and support to learners who are struggling with Encoder-Decoder Architectures.
  • Share your insights and experiences to help learners develop a deeper understanding of the concepts.
Explore Advanced Techniques for Encoder-Decoder Architectures
Explore advanced techniques, such as attention mechanisms, to enhance your understanding of Encoder-Decoder Architectures.
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  • Identify advanced techniques for Encoder-Decoder Architectures.
  • Locate online tutorials and resources on these techniques.
  • Follow the tutorials and implement the techniques in your own projects.
Develop an Encoder-Decoder Model for a Specific Task
Create an Encoder-Decoder model for a specific task, such as machine translation or text summarization, to apply your knowledge.
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  • Identify a specific task for which you want to develop an Encoder-Decoder model.
  • Collect and prepare the necessary data for training.
  • Design and implement an Encoder-Decoder model for the task.
  • Train and evaluate the performance of the model.
  • Deploy the model for use.
Participate in Kaggle Competitions
Challenge yourself by participating in Kaggle competitions involving Encoder-Decoder Architectures to enhance your practical skills.
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  • Identify Kaggle competitions that involve Encoder-Decoder Architectures.
  • Form a team or participate individually.
  • Develop and train Encoder-Decoder models for the competition tasks.
  • Submit your models and track your progress.
  • Analyze the results and learn from the experience.

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Course image
Level
Advanced
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Session
August 19, 2024 - September 2, 2024
Language
Deutsch
Good to know
Taught by Google Cloud Training, industry leaders in the field
Suitable for beginners looking to build a foundation in Encoder-Decoder Architectures
Covers the core concepts of Encoder-Decoder Architectures, a powerful technique used in natural language processing tasks like machine translation and text summarization
Provides hands-on practice with a step-by-step lab to implement an Encoder-Decoder Architecture for poetry generation in TensorFlow
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