- Level Expert
- المدة 1 ساعات hours
- الطبع بواسطة Google Cloud
-
Offered by
عن
Questo corso ti offre un riepilogo dell'architettura encoder-decoder, che è un'architettura di machine learning potente e diffusa per attività da sequenza a sequenza come traduzione automatica, riassunto del testo e risposta alle domande. Apprenderai i componenti principali dell'architettura encoder-decoder e come addestrare e fornire questi modelli. Nella procedura dettagliata del lab corrispondente, implementerai in TensorFlow dall'inizio un semplice codice dell'architettura encoder-decoder per la generazione di poesie da zero.الوحدات
Architettura encoder-decoder: panoramica
1
Assignment
- Architettura encoder-decoder: Quiz
2
Videos
- Architettura encoder-decoder: Panoramica
- Architettura encoder-decoder: Procedura dettagliata del lab
1
Readings
- Architettura encoder-decoder: Risorse del lab
Auto Summary
Delve into the fascinating world of machine learning with our comprehensive course on Encoder-Decoder Architecture, presented in Italian. This advanced program is meticulously designed for those with a keen interest in IT and Computer Science. The course focuses on the powerful and widely-used encoder-decoder architecture, essential for sequence-to-sequence tasks such as automatic translation, text summarization, and question answering. Guided by experienced instructors from Coursera, you will explore the fundamental components of the encoder-decoder architecture, gaining insights into training and deploying these models effectively. A hands-on lab session is included, where you will implement a basic encoder-decoder model using TensorFlow, culminating in the creation of poetry from scratch. The course spans a concise duration of one month, offering flexible subscription options tailored to your needs, including Starter and Professional plans. Ideal for expert-level learners, this program promises to equip you with the skills to harness the full potential of encoder-decoder architectures in various machine learning applications.

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