LLMs are good at in-context reasoning; given a bunch of information in context, they can perform complex reasoning tasks and solve problems. But can they perform latent multi-hop reasoning on pre-trained data? Simply put, can LLMs connect and traverse through implicit knowledge
MACHINE LEARNING
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Accelerating AI Development Data Pipeline for Autonomous Vehicles
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Watch this @PureStorage webinar "Accelerating the AI Development Data Pipeline for Autonomous, Software-Defined Vehicles" at https://
purestorage.com/video/webinars
/accelerating-the-ai-development-data-pipeline/6348011604112.html
… Tomorrow’s consumer vehicles will be electric, autonomous, software-defined, and powered by artificial intelligence (AI). The pace -
Exploring Statistical Phenomena and Common Mistakes in Data Science
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Explore surprising statistical phenomena and real-life common mistakes (like the base rate fallacy) in this new episode from the @_odsc Ai X Podcast. Start listening: https://
linktr.ee/odsc
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#ODSC #DataScientists #MachineLearning #Statistics #DataScience #StatisticalLiteracy -
Scheduled Sampling Technique in AI Model Training
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This is somewhat similar to scheduled sampling, right?
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Multi-Resolution Weak Supervision for Sequential Data Research Dissection
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Throwback to an enlightening session from 2021 when our talented team dissected the research paper 'Multi-Resolution Weak Supervision for Sequential Data' Watch the session here: https://
youtube.com/watch?v=G0sDtK
08330
… #snorkelai #machinelearning #neurips #AIeducation -
New Interpretable Machine Learning Python Book Second Edition Released
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I have this incredible 606-page new book from @PacktPublishing
: http://
amzn.to/463U79Y by @smasis "Interpretable #MachineLearning with #Python: Build high-performance models with hands-on, real-world examples" (2nd Ed.)
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#AI #XAI #ML #BigData #DataScience #DataScientists -
Develop High-Performance, Low-Bias, Explainable ML Models with Python
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Develop and Debug high-performance, low-bias, and explainable #MachineLearning and #DeepLearning Models with #Python: http://
amzn.to/3u2JiIB by @AliMLearning , published by @PacktPublishing —————
#DataScientists #ComputationalScience #Coding #DataScience #AI #XAI #PyTorch -
Python Feature Engineering Cookbook: 70+ Recipes for Machine Learning
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#Python #FeatureEngineering Cookbook with 70+ recipes for creating, engineering, & transforming features for #MachineLearning models (2nd Edition): http://
amzn.to/3Ssdh5X via @PacktPublishing ————
#AI #BigData #DataStrategy #DataLiteracy #DataScience #DataScientists #Coding #ML -
Machine Learning Model-Serving Patterns and Production Best Practices Guide
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#MachineLearning Model-Serving Patterns & Best Practices — Definitive guide to deploying, monitoring, & providing accessibility to #ML models in production: http://
amzn.to/3vNwKoT via @PacktPublishing ——
#AI #DeepLearning #DataScience #DataScientists #MLOps #CDO #DataEngineering -
Vector Search for Practitioners with Elastic NLP Toolkit
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Vector Search for Practitioners with Elastic — A toolkit for building #NLProc solutions for search, observability, and security: http://
amzn.to/3vEkWFe via @PacktPublishing ——
#DataScience #Analytics #DataScientists #MahineLearning #BigData #AI #VectorDB #RAG #LLMs #GenerativeAI