Remove 2023 Remove Clean Data Remove Natural Language Processing
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Innovations in Analytics: Elevating Data Quality with GenAI

Towards AI

Hype Cycle for Emerging Technologies 2023 (source: Gartner) Despite AI’s potential, the quality of input data remains crucial. Inaccurate or incomplete data can distort results and undermine AI-driven initiatives, emphasizing the need for clean data. Clean data through GenAI!

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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. The event was part of the chapter’s technical talk series 2023. Computational Linguistics is rule based modeling of natural languages.

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Introduction to Autoencoders

Flipboard

Figure 3: Latent space visualization of the closet (source: Kumar, “Autoencoder vs Variational Autoencoder (VAE): Differences,” Data Analytics , 2023 ). Figure 5: Architecture of Convolutional Autoencoder for Image Segmentation (source: Bandyopadhyay, “Autoencoders in Deep Learning: Tutorial & Use Cases [2023],” V7Labs , 2023 ).

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Top 15 Data Analytics Projects in 2023 for beginners to Experienced

Pickl AI

Top 15 Data Analytics Projects in 2023 for Beginners to Experienced Levels: Data Analytics Projects allow aspirants in the field to display their proficiency to employers and acquire job roles. NLP techniques help extract insights, sentiment analysis, and topic modeling from text data.

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Poster presenters compete to win desktop GPU

Snorkel AI

We asked the community to bring its best and most recent research on how to further the field of data-centric AI, and our accepted applicants have delivered. Those approved so far cover a broad range of themes—including data cleaning, data labeling, and data integration.

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Poster presenters compete to win desktop GPU

Snorkel AI

We asked the community to bring its best and most recent research on how to further the field of data-centric AI, and our accepted applicants have delivered. Those approved so far cover a broad range of themes—including data cleaning, data labeling, and data integration.

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Turn the face of your business from chaos to clarity

Dataconomy

Data preprocessing is a fundamental and essential step in the field of sentiment analysis, a prominent branch of natural language processing (NLP). Data scientists must decide on appropriate strategies to handle missing values, such as imputation with mean or median values or removing instances with missing data.