http://
Ludwig.ai now supports gradient-boosted tree #models. Just in time for the holidays! Check out this blog and tutorial to see how they compare to #neuralnetworks and get started building in < 10 lines of code. https://
pbase.ai/3HTgPJK #opensource #machinelearning
MACHINE LEARNING
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Ludwig.ai Now Supports Gradient-Boosted Tree Models
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Integrating datasets with Hugging Face model evaluator tools
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cool work @EthanJPerez & team! Would be awesome to add the datasets to http://
hf.co/datasets or some sort of integration with https://
huggingface.co/spaces/autoeva
luate/model-evaluator?dataset=acronym_identification
… if that makes sense! -
Software Stack Enables ML Training and Inference Across Frameworks
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"Software is one of the hardest obstacles in cracking the ML training and inference market. Our software stack is capable of running both training and inference from all the different popular machine learning frameworks and architecture design." @TMLS_TO
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Flamingo AI Advances Computer Vision Beyond Past Predictions
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10 yrs ago @karpathy wrote a blog post on the outlook of AI: https://
karpathy.github.io/2012/10/22/sta
te-of-computer-vision/
… in which he describes how difficult it would be for an AI to understand a given photo, concluding "we are very, very far and this depresses me."
Today, our Flamingo steps up to the challenge. -
Featuretools-SQL: Automated Feature Engineering for Databases
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Introducing Featuretools-SQL: The easy way to import data from your #RDBMS and perform automated #FeatureEngineering. Streamline your #MachineLearning process with our new library. Learn more in our blog: http://
ow.ly/kFhA50M69ww #Python #databases #postgres #Snowflake #mySQL -
AI Inference Hardware Optimization for Machine Learning Workloads
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Thank you for your continued support as we work to help companies run AI inference workloads faster, cooler, and more cost-effectively using #atmemorycomputation. We look forward to the innovations and advancements that 2023 will hold! #ML #deeplearning #neuralnetworks #AI
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Fine-tuning vs. In-context Learning: Exploring Alternative Approaches
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this is great question! i think its an unexplored space. finetuning is much more heavy weight (error prone, hard to do, more data required, etc), so it will be interesting to see if you can achieve similar performance just by using examples
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AlphaCode and AlphaTensor Named Top 10 Science Breakthroughs
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Awesome to see #AlphaCode (AI for competitive programming) and #AlphaTensor (AI for efficient matrix multiplication) both feature in Science's Top 10 Breakthroughs of the Year! Congrats to the teams!!
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Stantec Launches Flood Predictor Engine with Databricks
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Stantec set out to develop the first-ever rapid flood estimation product Learn about their Flood Predictor Engine, the challenges the data team encountered implementing production-grade geospatial feature engineering pipelines & how Databricks helps! https://
dbricks.co/3BlfBmi -
Scaling Natural Language Instructions to 240K Diverse Examples
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New paper — In ‘Unnatural Instructions’ we propose a new method to automatically generate natural language instructions, allowing us to scale up to 240K diverse instructions & train models that rival the performance of contemporary instruction-tuned models. Read on ArXiv