Book a demo of Snorkel Flow and learn how to accelerate your AI development >> 100x faster with the power of programmatic labeling. http://
snkl.ai/bookdemo We will be at #GartnerDA, be sure to stop by booth #1544 and register to win an Apple Vision Pro!
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Snorkel Flow Accelerates AI Development with Programmatic Labeling
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Data Science Toolkit: From Machine Learning to Deployment Guide
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Unpack the world of Data Science with this concise infographic by Gina Acosta Gutiérrez! It's a toolkit to navigate from machine learning to deployment. Elevate your expertise with "The Digital Edge" https://
bit.ly/3u4pILl by @ingliguori
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Essential Steps for Successful AI Project Implementation
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Great article for #AIConsultants discussing the steps for #AI projects: Understand the Problem Data Collection & Prep Model Building Testing & Validation Deployment Monitoring & Maintenance
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Hugging Face Becomes Critical Platform for AI Leaderboards
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Very cool to see that in the past year, @huggingface has become the platform for leaderboards, benchmark and evaluations, which are very critical pieces of the AI building process. You can find a lot of these like the chatbot Arena, the openLLM leaderboard, the bigcode
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Google AI Secrets Stolen: Top AI Stories Today
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Top stories in AI today: -Google engineer steals AI secrets
-Inflection upgrade nears GPT-4
-Generate an AI song with just a prompt
-Researchers create self-spreading AI malware
-6 new AI tools & 4 new AI jobs Read more: http://
therundown.ai/p/googles-ai-s
ecrets-stolen
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Abacus AI Anomaly Detection Using Deep Learning Models
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#AnomalyDetection from @AbacusAI uses leading-edge #DeepLearning models to spot anomalies in your data & act on them to increase revenue, decrease costs, reduce risk: https://
abacus.ai/anomalydetecti
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#AI #MachineLearning #ML #BigData #Analytics #DataScience #DataScientists #DataQuality -
Keras and PyTorch: Prototype Once, Deploy Anywhere with JAX
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I know some companies like to prototype in PyTorch then rewrite in JAX for performance. Did you know you could just… prototype in Keras + PyTorch, then switch to JAX *while keeping all of your model code*? At most you'll have to rewrite the train_step or the training loop.
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Essential Keras Tips for Debugging and Prototyping with Different Backends
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Essential Keras tips: 1. Debug/prototype with eager execution. You can prototype with the PyTorch backend, or even with the NumPy backend if you're only looking at a layer or a forward pass (the NumPy doesn't support gradients/training). With JAX or TF, make sure to use
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Availability and performance comparison of AI inference tools
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Would love to give it a try here in EU. Seems like it is about to catch up with groq at some point
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Find LLM leaderboards: Aymeric Roucher shares ultimate collection by C. Le Fourrier
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Are you trying to find good leaderboards to compare LLMs? @clefourrier is building the ultimate collection here: