Data Analytics
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Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making. Admin: @HusseinSheikho || @Hussein_Sheikho
Ko'proq ko'rsatish
Tarmoq:📈 Telegram kanali Data Analytics analitikasi
Data Analytics (@dataanalyticsx) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 30 560 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 4 280-o'rinni va Rossiya mintaqasida 20 902-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 30 560 obunachiga ega bo‘ldi.
05 Oktabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 582 ga, so‘nggi 24 soatda esa 24 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 5.28% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.16% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 1 612 marta ko‘riladi; birinchi sutkada odatda 355 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 3 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent sellerflash, buybox, buyer, chaos, effortless kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 06 Oktabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
30 560
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+2424 soatlar
+1597 kun
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| Sana | Obunachilarni jalb qilish | Esdaliklar | Kanallar | |
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Kanal postlari
| 2 | Exporting the model from PyTorch to the universal ONNX format for independent inference 🚀
Deploying PyTorch models in production often requires installing a large framework and relying on a Python environment. The ONNX (Open Neural Network Exchange) format converts the computation graph into an intermediate binary format suitable for running on any device and programming language. We will export the PyTorch neural network and run its inference using the lightweight ONNX Runtime. 🛠
To export and run the neural network, we will install the PyTorch framework, the ONNX library, and the cross-platform ONNX Runtime engine.
pip install torch onnx onnxruntime
The packages for converting and high-performance execution of graphs have been successfully installed. ✅
We will write a Python script that creates a test PyTorch model, exports it to a .onnx file, and immediately performs a verification of the output.
import torch, torch.nn as nn, onnxruntime as ort, numpy as np
model = nn.Sequential(nn.Linear(10, 5), nn.ReLU())
x = torch.randn(1, 10)
torch.onnx.export(model, x, "model.onnx", input_names=["input"], output_names=["output"])
session = ort.InferenceSession("model.onnx")
res = session.run(None, {"input": x.numpy()})
print("ONNX Output shape:", res[0].shape)
The model graph has been successfully serialized into a binary file, and the runtime performed the prediction without involving PyTorch. 📦
# verification (checks the correctness and structure of the saved ONNX model)
python3 -c "import onnx; model = onnx.load('model.onnx'); onnx.checker.check_model(model); print('ONNX Model Status: Valid')"
Expected output: ONNX Model Status: Valid
# cleanup (deletes the generated model file and cleans up binaries)
rm -f model.onnx
Converting neural networks to ONNX allows you to decouple inference from Python and run models in C++, Rust, Go, or directly in a web browser. Be sure to specify the names of the input and output tensors when exporting to simplify integration with the service. 💻
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