Unlocking Multi-GPU Model Training with Dask XGBoost | NVIDIA Technical Blog https://
bit.ly/3ZkbIsF #AI #MachineLearning #DeepLearning #LLMs #DataScience
MACHINE LEARNING
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Multi-GPU Model Training with Dask XGBoost
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Evaluating AI Accuracy: How to Know When to Trust Results
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"Correctly identifying" And you evaluated this HOW? What % of the time is it actually right? And how do you know when NOT to trust it?
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Essential ML Deployment Techniques for Data Scientists
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Must learn ML deployment techniques as a data scientist 1. Model serving – REST APIs, Dedicated server
2. Containerization – Docker
3. Cloud deployment – AWS/GCP/Azure Learn more about cloud platforms here -
NER is Machine Learning, Not GenAI: Cut the Hype
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Are you doing ML for NER? That's. Not. "GenAI". Are you doing ML for NER? Have you carefully evaluated how well it works in your use case? If so, proceed with caution. Either way: cut the hype.
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Snowflake Transforms Data with Generative AI Synthetic Data
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Discover how cloud data giant #Snowflake is revolutionizing the use of #synthetic #data created through #generative #AI to overcome the challenges of real-world data collection, enhancing #privacy and reducing #bias while also leveraging AI.
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Compositional Foundation Models for Long-Horizon Goals
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8/ Compositional Foundation Models – proposes foundation models that leverage multiple expert foundation models trained on language, vision, and action data to solve long-horizon goals.
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LMSYS-Chat-1M: 1M Conversations Dataset with 25 LLMs
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6/ LMSYS-Chat-1M – a large-scale dataset containing 1 million real-world conversations with 25 state-of-the-art LLM; it is collected from 210K unique IP addresses on the Vincuna demo and Chatbot Arena website.
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Language Modeling as Compression: LLM Prediction Capabilities
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7/ Language Modeling is Compression – evaluates the compression capabilities of LLMs; investigates how and why compression and prediction are equivalent; shows that LLMs are powerful general-purpose compressors due to their in-context learning abilities.
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LLMs for Structured Data Generation: Fine-tuning Approach
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5/ LLMs for Generating Structured Data – studies the use of LLMs for generating complex structured data; proposes a structure-aware fine-tuning method, applied to Llama-7B, which significantly outperform other model like GPT-3.5/4 and Vicuna-13B.
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Contrastive Decoding Improves LLM Reasoning Performance
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3/ Contrastive Decoding Improves Reasoning in Large Language Models – shows that contrastive decoding leads Llama-65B to outperform Llama 2 and other models on commonsense reasoning and reasoning benchmarks.