Data Scientist Career Path: Tutorials → College → Deep Learning & Tools
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Data Scientist Career Path: Tutorials → College → Deep Learning & Tools
5K views2 weeks ago
TikTokai.data.tech
🤖 Machine Learning (ML) ဆိုတာ AI ရဲ့ အရေးပါတဲ့ အစိတ်အပိုင်းတစ်ခုဖြစ်ပြီး နယ်ပယ်ပေါင်းစုံရဲ့ အနာဂတ်ကို ပြောင်းလဲပေးနေတဲ့ နည်းပညာတစ်ခုပါ။ လူတစ်ယောက်က အတွေ့အကြုံတွေကနေ သင်ယူပြီး ဆုံးဖြတ်ချက်ချနိုင်သလို Machine Learning ကလည်း Data တွေကို လေ့လာသင်ယူပြီး Pattern တွေရှာဖွေကာ ခန့်မှန်းချက် (Prediction) နဲ့ ဆုံးဖြတ်ချက်တွေ ပြုလုပ်နိုင်ပါတယ်။ 🌍 Machine Learning ကို ဘယ်နယ်ပယ်တွေမှာ အသုံးပြုနေကြလဲ? ⚙️ Engineering – စက်ပစ္စည်းချို့ယွင်းမှု ကြိုတင်ခန့်မှန်းခြင်း၊ Design Optimization နှင့် Predictive Mainten
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🤖 Machine Learning (ML) ဆိုတာ AI ရဲ့ အရေးပါတဲ့ အစိတ်အပိုင်းတစ်ခုဖြစ်ပြီး နယ်ပယ်ပေါင်းစုံရဲ့ အနာဂတ်ကို ပြောင်းလဲပေးနေတဲ့ နည်းပညာတစ်ခုပါ။ လူတစ်ယောက်က အတွေ့အကြုံတွေကနေ သင်ယူပြီး ဆုံးဖြတ်ချက်ချနိုင်သလို Machine Learning ကလည်း Data တွေကို လေ့လာသင်ယူပြီး Pattern တွေရှာဖွေကာ ခန့်မှန်းချက် (Prediction) နဲ့ ဆုံးဖြတ်ချက်တွေ ပြုလုပ်နိုင်ပါတယ်။ 🌍 Machine Learning ကို ဘယ်နယ်ပယ်တွေမှာ အသုံးပြုနေကြလဲ? ⚙️ Engineering – စက်ပစ္စည်းချို့ယွင်းမှု ကြိုတင်ခန့်မှန်းခြင်း၊ Design Optimization နှင့် Predictive Mainten
224 views2 weeks ago
TikTokclicktopengineering
See how a neural network learns with TensorFlow Playground. This free browser experiment makes synthetic circle data, a learned decision boundary, and training results visible for an approachable introduction to machine learning. Education #TensorFlowPlayground #NeuralNetworks #MachineLearning #AI #30SecondAITools
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See how a neural network learns with TensorFlow Playground. This free browser experiment makes synthetic circle data, a learned decision boundary, and training results visible for an approachable introduction to machine learning. Education #TensorFlowPlayground #NeuralNetworks #MachineLearning #AI #30SecondAITools
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TikTok30secondideas
AI Engineer vs ML Engineer 🤖 What's the REAL difference? They both work with AI, but their skills, tools, and responsibilities can be very different! 🧠 Machine Learning Engineer: Focuses on data, model training, algorithms, optimization, PyTorch, and TensorFlow. ⚙️ AI Engineer: Focuses on building AI applications using LLMs, RAG, AI agents, APIs, and FastAPI. 🎯 The simple difference: ML Engineers focus on developing and improving models, while AI Engineers focus on building practical AI-power
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AI Engineer vs ML Engineer 🤖 What's the REAL difference? They both work with AI, but their skills, tools, and responsibilities can be very different! 🧠 Machine Learning Engineer: Focuses on data, model training, algorithms, optimization, PyTorch, and TensorFlow. ⚙️ AI Engineer: Focuses on building AI applications using LLMs, RAG, AI agents, APIs, and FastAPI. 🎯 The simple difference: ML Engineers focus on developing and improving models, while AI Engineers focus on building practical AI-power
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🔹 تربط الرسوم البيانية الحسابية في TensorFlow العمليات عبر الموترات لمعالجة البيانات وحساب التدرجات وتدريب الشبكات العصبية بكفاءة. ويتيح ‎@tf.function‎ تحويل دوال Python إلى رسوم بيانية محسّنة تقلّل من أعباء التنفيذ. كما يحسّن TensorFlow الأداء من خلال دمج العمليات وإعادة استخدام الذاكرة والتنفيذ المتوازي وتقنية XLA، خاصةً على وحدات GPU وTPU. ويساعد استخدام العمليات الأصلية على الاستفادة القصوى من هذه التحسينات. 👉 طوّر مهاراتك في تحسين النماذج وتدريب الشبكات العصبية من خلال برنامج الذكاء الاصط
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