在台灣由講師進行實時指導的Spark NLP本地培訓課程。
客戶評論
我確實喜歡這些練習。
Office for National Statistics
課程: Natural Language Processing with Python
Machine Translated
培訓師很容易解釋困難和高級話題。
Leszek K
課程: Artificial Intelligence Overview
Machine Translated
這是我用過的最好的動手編程課程之一。
Laura Kahn
課程: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
Machine Translated
這是我13年職業生涯中最優秀的在線培訓之一。保持偉大的工作！
課程: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
Machine Translated
人間識別和電路板壞點檢測
王 春柱 - 中移物联网
課程: Deep Learning for NLP (Natural Language Processing)
Machine Translated
演示
中移物联网
課程: Deep Learning for NLP (Natural Language Processing)
Machine Translated
關於面部區域。
中移物联网
課程: Deep Learning for NLP (Natural Language Processing)
Machine Translated
最後一天。生成部分
Accenture Inc
課程: Python for Natural Language Generation
Machine Translated
關於NLG的主題。該團隊最終能夠通過有趣的主題學習新的東西，但這只是在最後一天。除了幻燈片之外，還有更多的動手活動。
Accenture Inc
課程: Python for Natural Language Generation
Machine Translated
我喜歡它更側重於不同文本摘要方法的方法
課程: Text Summarization with Python
Machine Translated
主題，演講者友好的態度
Fujitsu Technology Solutions Sp. z o.o.
課程: Artificial Intelligence Overview
Machine Translated
這是我13年職業生涯中最優秀的在線培訓之一。保持偉大的工作！
課程: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
Machine Translated
我喜歡它更側重於不同文本摘要方法的方法
課程: Text Summarization with Python
Machine Translated
Spark NLP課程大綱
課程名稱
課程時長
概觀
課程名稱
課程時長
概觀
14小時
This instructor-led, live training in 台灣 (online or onsite) is aimed at data scientists and developers who wish to use Spark NLP, built on top of Apache Spark, to develop, implement, and scale natural language text processing models and pipelines.
By the end of this training, participants will be able to:
- Set up the necessary development environment to start building NLP pipelines with Spark NLP.
- Understand the features, architecture, and benefits of using Spark NLP.
- Use the pre-trained models available in Spark NLP to implement text processing.
- Learn how to build, train, and scale Spark NLP models for production-grade projects.
- Apply classification, inference, and sentiment analysis on real-world use cases (clinical data, customer behavior insights, etc.).
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