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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Red:📈 Análisis del canal de Telegram Data Analytics
El canal Data Analytics (@dataanalyticsx) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 30 560 suscriptores, ocupando la posición 4 272 en la categoría Tecnologías y Aplicaciones y el puesto 20 891 en la región Rusia.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 30 560 suscriptores.
Según los últimos datos del 06 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 587, y en las últimas 24 horas de 7, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 5.48%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.16% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 675 visualizaciones. En el primer día suele acumular 355 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
- Intereses temáticos: El contenido se centra en temas clave como sellerflash, buybox, buyer, chaos, effortless.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 07 octubre, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.
30 560
Suscriptores
+724 horas
+1407 días
+58730 días
Carga de datos en curso...
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| Fecha | Crecimiento de Suscriptores | Menciones | Canales | |
| 06 octubre | +7 | |||
| 05 octubre | +24 | |||
| 04 octubre | +51 | |||
| 03 octubre | +21 | |||
| 02 octubre | +5 | |||
| 01 octubre | +17 |
Publicaciones del Canal
Repost from Machine Learning with Python
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| 3 | 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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| 5 |  +1"How to Train a Neural Network" is a concise summary of the MIT course lectures on deep learning from 2024. It focuses on one of the fundamental questions in neural networks: how a model learns its weights.
The summary examines the training process from a mathematical perspective. It covers topics such as forward propagation, loss functions, gradients, backpropagation, and gradient-based optimization methods.
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.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/mit6_7960_f24_lec2.pdf
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| 11 | Pandas vs Polars — 14-section course cheatshee
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| 12 |  If you're just starting to learn machine learning and want to delve deeper into the mathematics required for machine learning and deep learning, I recommend trying this platform. It's something like LeetCode for machine learning.
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| 13 | This channels is for Programmers, Coders, Software Engineers.
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| 14 |  Sin texto... | 1 232 |
| 15 | 📊 Collecting Data Across Different Regions?
Data analysis often starts long before the dashboard or visualization.
When collecting public web data, regional differences can affect the content, prices, search results, or other information returned to your requests.
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| 16 | Sin texto... | 2 306 |
| 17 | Get Up to 500MB of Residential Proxy Traffic for Your Python Projects 🐍
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| 18 |  Sin texto... | 2 211 |
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| 20 |  This repository contains Jupyter notebooks for the O'Reilly book "Transformers: The Definitive Guide."
It includes code for computer vision tasks, time series analysis, audio processing, and reinforcement learning.
https://github.com/Nicolepcx/transformers-the-definitive-guide
https://t.me/MachineLearning9 🤩 | 1 764 |
