102.4K
S.Lipid profile. #lipid #lipidprofile #dyslipidemia
@s.s.kmedicare
#Lisp Macro System Explained
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AMCurious about what your lipid profile report really means? Let’s break down the numbers!🙌🏻
Understanding your lipid profile can be a game-changer for your health. No more confusing terms—just clear, simple insights to help you make smarter choices.
Watch this reel and gain the knowledge to keep your heart and health in check! ❤️
Don’t forget to share with friends and family!
[lipid profile explained, health simplified, know your numbers, heart health, cholesterol check, healthy habits]
@amitagadre
2.0K
RE💥Липидограмма — это один из самых простых, но при этом мощных инструментов профилактики сердечно-сосудистых заболеваний, включая инфаркт миокарда и инсульт. Но важно понимать: она не «защищает» сама по себе — она помогает вовремя выявить риск и принять меры
#холестерин
#здоровоесердце
#липидограмма
#атеросклероз
#профилактикаинсульта
@rezident_me
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PAGet Structured Outputs from LLM’s 👨💻
#tech #llms #google #developer
@parasmadan.in
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IMBlood tests normal? Don’t miss Lipoprotein(a) — the silent risk marker many ignore.
#LipoproteinA #HeartRisk #HiddenRisk #SilentKiller #HeartHealth CholesterolMyths PreventiveCardiology HealthAwareness DoctorReels MedicalReels
@immadi.n
56.4K
DRSerum Amylase & Lipase देखना सीखो Clinically🩺 ~ All in one Interpretation step-by-step - Must Know every Medico💊
- Follow for More ✅
- DM for Clinical Notes 📩
(Dr.Suryakamal sahu )
#medical #medicalstudent #medicalnotes #mbbs #bams #medico #neetpgpreparation #notes #medschool #doctor #medico #viral #instamood
@dr.suryaa___sahu
2.0K
GYLipid Profile एक बड़ा स्कैम🥱😱 #scienceandtechnology #upsc #education #i̇ran #israel
@gyrus.sulcus
39.9K
MA⭕ Lipoproteina(a) per prevedere il proprio rischio cardiovascolare?
👉 La Lp(a) è uno dei diversi metodi che abbiamo per stimare il rischio individuale di infarti e patologie cardiologiche.
Chiedi al tuo medico se è utile per te 💪
📍Bibliografia
Nordestgaard BG et al, Lancet 2024
Bibliografia completa sul canale 📺
#lipoproteinaa #infarto #cardiologia #prevenzione #mattiagarutti
@mattia.garutti.oncologo
25.6K
DRLp(a) tem ganhado cada vez mais espaço na estratificação de risco!
Quer saber mais? Digite plataforma e conheça a plataforma CardioEasy!
#cardioeasy
@dr.pedroschwartzmann
13.1K
TEPeople often say LLMOps is just MLOps applied to large language models.
But hey solve very different operational problems.
Here’s how I think about the difference.
1️⃣ Model lifecycle vs application lifecycle
MLOps focuses on managing the **model itself**
training → versioning → deployment → monitoring
LLMOps feels closer to managing an **AI application**
model → prompts → tools → workflows → responses
2️⃣ Data pipelines vs context pipelines
In MLOps, most work goes into building pipelines like
raw data → feature engineering → model training
In LLMOps, the pipeline shapes the model’s context
knowledge base → retrieval (RAG) → prompt template → model
3️⃣ Metrics vs evaluation frameworks
MLOps relies on clear metrics
accuracy → precision → recall → RMSE.
LLMOps needs layered evaluation
prompt testing → hallucination checks → LLM-as-judge → human feedback.
4️⃣ Model deployment vs AI system orchestration
MLOps manages
training pipeline → model deployment → performance monitoring.
LLMOps orchestrates the **entire AI system**
agents → tool calling → RAG pipelines → guardrails.
5️⃣ Data drift vs behavior drift
MLOps monitors
data drift → model performance decay.
LLMOps watches for
prompt failures → tool errors → hallucinations → safety issues.
Most real AI systems end up needing both.
Predictive systems like recommendation, forecasting, and fraud detection rely on **MLOps**.
AI assistants, copilots, and agent workflows rely heavily on **LLMOps**.
Different toolkits.
Same goal 🥅 Reliable AI systems.
💾 Save this if you’re trying to understand how modern AI systems are actually built
💬 Comment if you think LLMOps will eventually replace MLOps or live alongside it
🔁 Follow for more practical notes on building real AI systems
@techwithprateek
49.5K
NA👋 Hola futur@s cientific@s 😊 Este es un tutorial de clasificación de proteínas utilizando Python 💻. Puedes encontrar el código del tutorial original en Medium:
Protein Sequence Classification Using Bidirectional LSTM on UniProtKB
https://towardsdatascience.com/protein-sequence-classification-99c80d0ad2df 🔗🧬
Resumen del proceso:
💻 Preprocesamiento: Convertimos las secuencias de aminoácidos en una matriz numérica con codificación One-Hot.
🧬 Modelo LSTM bidireccional: Usamos una red neuronal que procesa las secuencias en ambas direcciones para capturar mejor los patrones.
📊 Entrenamiento: Ajustamos el modelo con parámetros como el optimizador y la función de pérdida.
🔍 Evaluación: Analizamos las gráficas para entender cómo el modelo mejora en precisión y exactitud durante las épocas. Pueden encontrar la animación del modelo en “Illustrated Guide to LSTM’s and GRU’s: A step by step explanation” en Towards Data Science 🔗.
Espero que este recurso les haya ayudado a entender cómo es el proceso de clasificación. Comenta si te gustaría una versión más larga paso a paso 😊.
Nos vemos en una próxima 🫶🔬
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#nath.biohack #genetifics #technology #research #science #investigation #student #job #learner #learning #engineering #education #tutorial #biotechnology #medicine #teaching #latam #spanish #statistics #bioinformatica
@nath.biohack
6.8K
LI🇮🇹 Dalla visione alla produzione🏭
Ogni gesto è controllo, cura e responsabilità. Lisap Milano produce internamente, unendo conoscenza e cultura italiana. È qui che la bellezza prende forma ✨
🇬🇧 From vision to production🏭
Every gesture is control, care and responsibility. Lisap Milano produces in-house, combining expertise with Italian culture 🇮🇹 It is here that beauty takes shape ✨
#lisapmilano #wedobeautifulhair #madeinitaly #hairprofessionalbeauty #whoweare
@lisapmilano.official
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