Remove Clean Data Remove Natural Language Processing Remove Supervised Learning
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NLP, Tools and Technologies and Career Opportunities

Women in Big Data

The Bay Area Chapter of Women in Big Data (WiBD) hosted its second successful episode on the NLP (Natural Language Processing), Tools, Technologies and Career opportunities. Computational Linguistics is rule based modeling of natural languages. The event was part of the chapter’s technical talk series 2023.

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Take advantage of AI and use it to make your business better

IBM Journey to AI blog

Building and training foundation models Creating foundations models starts with clean data. This includes building a process to integrate, cleanse, and catalog the full lifecycle of your AI data. A hybrid multicloud environment offers this, giving you choice and flexibility across your enterprise.

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Simplify data prep for generative AI with Amazon SageMaker Data Wrangler

AWS Machine Learning Blog

As AI adoption continues to accelerate, developing efficient mechanisms for digesting and learning from unstructured data becomes even more critical in the future. This could involve better preprocessing tools, semi-supervised learning techniques, and advances in natural language processing.

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Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

Data cleaning identifies and addresses these issues to ensure data quality and integrity. Data Analysis: This step involves applying statistical and Machine Learning techniques to analyse the cleaned data and uncover patterns, trends, and relationships.

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Retrieval augmented generation (RAG): a conversation with its creator

Snorkel AI

As humans, we learn a lot of general stuff through self-supervised learning by just experiencing the world. Maybe this is starting to change now, but for a long time, both in industry and academia, people didn’t have enough respect for data and how important it is and how much you can gain from thinking about the data.

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Retrieval augmented generation (RAG): a conversation with its creator

Snorkel AI

As humans, we learn a lot of general stuff through self-supervised learning by just experiencing the world. Maybe this is starting to change now, but for a long time, both in industry and academia, people didn’t have enough respect for data and how important it is and how much you can gain from thinking about the data.