Synthetic Data RL: Task Definition Is All You Need Guo et al.: https://
arxiv.org/abs/2505.17063 #ArtificialIntelligence #DeepLearning #MachineLearning
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Synthetic Data RL: Task Definition Is All You Need
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Handling Missing Values in Data Science and Machine Learning
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After some time off, I am back at XGBlogging. In today's and the next two posts I'll be dealing with the topic of missing values in data science and machine learning. Link to the blog post in the first comment.
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Batch Size Impact on Model Training Updates and Results
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We covered this in some of our earlier courses – lower batch sizes provide more updates, which should give better results for a fixed # epochs.
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Claude 4 prompt to map Gmail customer journeys
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Steal my Claude 4 prompt to analyze Gmail customer interactions and turn it into comprehensive journey maps with emotional insights ———————————————-
CLAUDE 4 CUSTOMER JOURNEY MAPPER
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Prompt for AI customer journey mapping with emotional insights
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Steal my o3 prompt to turn customer interview data into comprehensive journey maps with emotional insights ————————————-
o3 CUSTOMER JOURNEY MAPPER
————————————- You are an expert customer experience analyst who transforms raw -
RAG System Webinar: Finance Model Evaluation Demo
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Unlock your RAG system! Weekly demo + live Q&A on finance model evaluation. May 27 | 10–10:30 AM PT Register https://
snorkel.ai/webinar/weekly
-demo-with-qa/
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Advanced telemetry systems built for next-generation technology
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Some of the things we're building at (coming soon) are so advanced that we've had to set up entirely new kinds of telemetry systems, each of which could easily be its own company lol
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Productionizing Machine Learning: Pipeline, Inference and Workflow Challenges
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Productionizing machine learning is the hack of a task. It can be extremely complicated, from the model pipeline to inferences and automating workflows across multiple servers. chasing some bits of it lately
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Comparative Bias Analysis Datasets for Popular Chatbots
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Would be useful to have similar datasets for GPT, Claude, Grok, and most of the popular chatbots to better analyze how biased they are
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AI Data Collection Scale Compared to 2010s Tech Giants
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the scale of data collection in the AI labs pales in comparison to 2010s google it’s mostly web scraping and data-labeling. compare that to diligently photographing streets of every country, mapping earth via satellite, scanning every book known to man.. now *that* was ambitious