Arama Sonuçları: "#algorithmengineering"
20 sonuç
Added another layer for /ES 'absorption' CHoCH & BOS reading the tape. Large prints are short covering and legit buying. #Trading #AlgorithmEngineering
@AnthropicAI 👀
Brian Automates@TwitBotReferee
My own ladder via API (ticks), trade volume by price, calculates in 10sec increments, will read selling into liquidity, iceberg and tape bombs (Phase 2) Tonight 0.7t/s overnight session is much different than day/NYC session at 5 to 10t/s+ Currently running /MES -> Phase 1+
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Algorithm abeg blow me up
I’m sat for jobs like mad
#algo #AlgorithmEngineering
LAME AHH@coolguygimme
Meet the @VizoExchange mascot 🐛 Bright, friendly, and full of good vibes Designed to bring positivity, energy, and a little bit of fun wherever it goes. What do you think—would you rock with this character? 💚 #vizomascot
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Control the game… or get controlled by it.
Every move matters. Every decision counts.
Stay sharp. Stay focused. ♠️🔥#AlgorithmEngineering #algotrading #forextrading
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The hardest part of algorithm engineering isn’t writing code—it’s making it run reliably on real data. Once a model goes live, you see how different training data is from the re... @karpathy @dragonvcap10317 @DeepMind #AlgorithmEngineering #ModelDeployment #CodeReview #RealData
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An algorithm engineer’s daily life is mostly tuning parameters, checking curves, and testing again and again. It may look boring, but every small improvement can push the model a big step forward. #AlgorithmEngineering
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Algorithm engineers deal with data, parameters, and metrics every day. It may look boring, but it’s actually exciting. You’re always chasing a better answer, and that answer might be hiding in the next experiment. @ylecun #LabComputer #AlgorithmEngineering #ParameterExperiment
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In algorithm engineering, the most important thing isn’t getting it right once—it’s being able to find the problem fast. Is the data off, are the labels wrong, or is the model structure off? Break it down, and the... #DataQuality #TechnicalTroubleshooting #AlgorithmEngineering
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Algorithm engineering doesn't end when the code is written—it really starts after launch. Reading logs, watching metrics, checking anomalies, running regressions. Every day is about working with data, and details decide the outcome. @ylecun #AlgorithmEngineering
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Algorithm engineering sounds hardcore, but the core is really three things: clean data, solid features, and stable results. Change one parameter today, and you... @huggingface @kdnuggets @RetorS20974 #TrainingData #AlgorithmEngineering #FeatureEngineering #TuningPanel #TestSet
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Algorithm engineering isn’t magic. Most of the time, it’s about polishing a small problem again and again until it’s perfect. If the data is dirty, clean it; if the... @flux_e58062 @DeepLearningAI #AlgorithmEngineering #lab #DataCleaning #IterationOptimization #ModelRetraining
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A lot of people think algorithm engineering is just hyperparameter tuning, but it's much more than that. Data cleaning, feature design, online monitoring, rollback plans—every step matter... @awn_b75350 @ylecun #RollbackPlan #DataCleaning #AlgorithmEngineering #OnlineMonitoring
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In algorithm engineering, the real test is not whether the model runs. It’s whether production stays safe. Good offline metrics do not mean the real world will be stable. If you can handle testing, monitoring, and rollback we... @kdnuggets #TestEnvironment #AlgorithmEngineering
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One of the most important skills for an algorithm engineer is breaking complex problems down. Start with the data, then the features, then the model—step by step, ma... @karpathy @ErCiph62477 #FeatureProcessing #ProblemSolving #AlgorithmEngineering #ModelDebugging #DataAnalysis
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Whether algorithm engineering is good shows up in the user experience. Are recommendations accurate? Is response fast? Are results stable? On the surface it's UX, b... @LSihft30630 @ylecun #AlgorithmEngineering #UserExperience #StableResults #RecommendationSystem #ResponseSpeed
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The most valuable experience in algorithm engineering often isn't from papers—it's from mistakes. Knowing when overfitting happens, when drift starts, and when to roll back all comes f... @karpathy @summi_t34360 #AlgorithmEngineering #RealworldExperience #overfitting #DataDrift
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After working in algorithm engineering for a while, you realize the real experts don’t just train models—they get them running, keep them stable, and put them into business. Deployment is... @DeepMind #EngineeringPractice #ModelTraining #AlgorithmEngineering #BusinessDeployment
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In algorithm engineering, the goal isn’t just to get the highest score in training. It’s to make the model perform reliably in real-world settings. Pretty results in the lab don’t... @ylecun @VcetorF69717 @hardmaru #AlgorithmEngineering #LaunchValidation #LabTesting #ModelScore
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In algorithm engineering, what’s most valuable is often not the model itself, but your understanding of the business. Knowing what to optimize, what to drop, and what to do first—that’s the difference between experts and everyone... #AlgorithmEngineering #BusinessUnderstanding
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