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Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model.

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Demystifying Multimodal LLMs

Dataiku

This scenario is not science fiction but a glimpse into the capabilities of Multimodal Large Language Models (M-LLMs), where the convergence of various modalities extends the landscape of AI. Moreover, M-LLMs adeptly answer questions about visual content, aiding in tasks like image recognition and scene understanding.

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Enterprise Data Science Workflows with AMPs and Streamlit

Cloudera

Here in the virtual Fast Forward Lab at Cloudera , we do a lot of experimentation to support our applied machine learning research, and Cloudera Machine Learning product development. We believe the best way to learn what a technology is capable of is to build things with it.

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12 data science certifications that will pay off

CIO Business Intelligence

The exam covers everything from fundamental to advanced data science concepts such as big data best practices, business strategies for data, building cross-organizational support, machine learning, natural language processing, scholastic modeling, and more. and SAS Text Analytics, Time Series, Experimentation, and Optimization.

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Five open-source AI tools to know

IBM Big Data Hub

When AI algorithms, pre-trained models, and data sets are available for public use and experimentation, creative AI applications emerge as a community of volunteer enthusiasts builds upon existing work and accelerates the development of practical AI solutions.

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Ferrovial puts AI at the heart of its transformation

CIO Business Intelligence

With the aim to accelerate innovation and transform its digital infrastructures and services, Ferrovial created its Digital Hub to serve as a meeting point where research and experimentation with digital strategies could, for example, provide new sources of income and improve company operations.

IT 105
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Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

Beyond that, we recommend setting up the appropriate data management and engineering framework including infrastructure, harmonization, governance, toolset strategy, automation, and operating model. It is also important to have a strong test and learn culture to encourage rapid experimentation.

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