#MachineLearning on Streaming Data with #Python ————
#IIoT #IoT #TimeSeries #AI #BigData #DataScience #EdgeComputing #Edge #EdgeAI #PredictiveAnalytics #StreamAnalytics #AnomalyDetection #Forecasting #Industry40 ———— http://
amzn.to/3EFK3Ki from @PacktPublishing
RESEARCH
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Machine Learning on Streaming Data with Python Guide
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Data Labeling in Machine Learning with Python: Modern Preparation Techniques
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[New release from @PacktPublishing
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Data Labeling in #MachineLearning with #Python — Explore modern ways to prepare labeled data for training and fine-tuning #ML and #GenerativeAI models: http://
amzn.to/49Qm4Vc
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#DataScience #AI #GenAI #DeepLearning #DataStrategy -
New Third Edition: Bayesian Analysis with Python Practical Guide
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NEW, UPDATED Third Edition "Bayesian Analysis with #Python — Practical Guide to Probabilistic Modeling": http://
amzn.to/3w3tq9g from @PacktPublishing ————
#DataScience #DataScientists #AI #MachineLearning #Coding #Probability #Statistics #Inference #StatisticalLearning -
Mastering Kaggle Notebooks: New Guide to Becoming a Grandmaster
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Developing @Kaggle Notebooks, toward becoming a #Kaggle Notebooks Grandmaster: http://
amzn.to/48LAjKA by @PredaGabi New book from @PacktPublishing ———— #BigData #DataScience #MachineLearning #AI #ML #DataScientists #DeepLearning #Python #Coding -
Graph Machine Learning: Advanced Techniques for Graph Data Analysis
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"Graph #MachineLearning: Take graph data to the next level by applying machine learning techniques and algorithms" at http://
amzn.to/3xdhIEE from @PacktPublishing ————
#BigData #DataScience #AI #ML #GraphDB #LinkedData #GraphAnalytics #KnowledgeBases -
Develop High-Performance, Low-Bias, Explainable ML and DL Models
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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 -
New Interpretable Machine Learning Python Book 2nd 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 -
50 Essential Algorithms Every Programmer Must Know
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50 #Algorithms Every Programmer Should Know: http://
amzn.to/3Mi1hAk via @PacktPublishing —————
#DataScientist #DataScience #Coding #Mathematics #MachineLearning #Statistics #ComputerScience #AI #Cryptography #ComputationalScience -
Debate on NIAH difficulty and false statements for well-known subjects
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Great point — most NIAHs are too easy. But recalling false statements about well-known subjects seems harder a priori (see MemoTrap) than new facts about new subjects. Also it surely knows Wikipedia, so wouldn’t name edits stick out as odd just like too-easy Gatsby needles do?
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Proactive approaches to data transparency in AI systems
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Data transparency issues, such as how data is collected and labeled, typically arise after the fact. How can we be more proactive about addressing these problems? @StanfordHAI faculty fellow @HariSubramonyam shares some insights from their latest research: