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The NLP Cypher | 02.14.21

Towards AI

John on Patmos | Correggio NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER The NLP Cypher | 02.14.21 Their infrastructure is built on top of FastAPI and supports Python, Go and Ruby languages. Last Updated on July 19, 2023 by Editorial Team Author(s): Ricky Costa Originally published on Towards AI.

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NLP News Cypher | 07.26.20

Towards AI

Photo by Will Truettner on Unsplash NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER NLP News Cypher | 07.26.20 The cryptic book arrived on the internet in the mid 2010’s by the now wildly popular but mysterious internet group 3301. Primus The Liber Primus is unsolved to this day.

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The NLP Cypher | 02.14.21

Towards AI

John on Patmos | Correggio NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER The NLP Cypher | 02.14.21 Their infrastructure is built on top of FastAPI and supports Python, Go and Ruby languages. Last Updated on July 21, 2023 by Editorial Team Author(s): Ricky Costa Originally published on Towards AI.

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Unlock ML insights using the Amazon SageMaker Feature Store Feature Processor

AWS Machine Learning Blog

Engineers must manually write custom data preprocessing and aggregation logic in Python or Spark for each use case. For this post, we refer to the following notebook , which demonstrates how to get started with Feature Processor using the SageMaker Python SDK. Prior to joining AWS, Ninad worked as a software developer for 12+ years.

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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning Blog

Overview of RAG RAG solutions are inspired by representation learning and semantic search ideas that have been gradually adopted in ranking problems (for example, recommendation and search) and natural language processing (NLP) tasks since 2010. We use the following Python script to recreate tables as pandas DataFrames.

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A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

In terms of resulting speedups, the approximate order is programming hardware, then programming against PBA APIs, then programming in an unmanaged language such as C++, then a managed language such as Python. Analysis of publications containing accelerated compute workloads by Zeta-Alpha shows a breakdown of 91.5%

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