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
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Network:📈 Analytical overview of Telegram channel Data Analytics
Channel Data Analytics (@dataanalyticsx) in the English language segment is an active participant. Currently, the community unites 30 560 subscribers, ranking 4 280 in the Technologies & Applications category and 20 902 in the Russia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 30 560 subscribers.
According to the latest data from 05 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 582 over the last 30 days and by 24 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 5.28%. Within the first 24 hours after publication, content typically collects 1.16% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 612 views. Within the first day, a publication typically gains 355 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
- Thematic interests: Content is focused on key topics such as sellerflash, buybox, buyer, chaos, effortless.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Thanks to the high frequency of updates (latest data received on 06 October, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
30 560
Subscribers
+2424 hours
+1597 days
+58230 days
Data loading in progress...
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| Date | Subscriber Growth | Mentions | Channels | |
| 06 October | +3 | |||
| 05 October | +24 | |||
| 04 October | +51 | |||
| 03 October | +21 | |||
| 02 October | +5 | |||
| 01 October | +17 |
Channel Posts
| 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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I believe this is an interesting resource for those who want to go beyond a general, intuitive understanding of neural networks and begin to delve into the mathematics that underlies their training.
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| 12 | This channels is for Programmers, Coders, Software Engineers.
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| 14 | 📊 Collecting Data Across Different Regions?
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| 19 |  This repository contains Jupyter notebooks for the O'Reilly book "Transformers: The Definitive Guide."
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