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 4 280 en la categoría Tecnologías y Aplicaciones y el puesto 20 902 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 05 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 582, y en las últimas 24 horas de 24, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 5.28%. 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 1 612 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 06 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
+2424 horas
+1597 días
+58230 días
Archivo de publicaciones
30 560
Repost from Machine Learning with Python
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30 560
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.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)python3 -c "import onnx; model = onnx.load('model.onnx'); onnx.checker.check_model(model); print('ONNX Model Status: Valid')"rm -f model.onnx30 560
Repost from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
✅ https://t.me/Codeprogrammer
30 560
Repost from Machine Learning with Python
+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
https://t.me/CodeProgrammer 🤩
30 560
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30 560
Repost from Machine Learning
Pandas vs Polars — 14-section course cheatshee
https://t.me/MachineLearning9
30 560
Repost from Machine Learning with Python
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.
This is not an advertisement: I personally used it and decided to share it with you.
https://deep-ml.com
https://t.me/CodeProgrammer
30 560
Repost from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
✅ https://t.me/Codeprogrammer
30 560
📊 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.
Using residential IPs from different locations can help when you need to test or collect location-specific data.
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30 560
Get Up to 500MB of Residential Proxy Traffic for Your Python Projects 🐍
Building a web scraper with Python?
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30 560
🚀Round 2 – 14-Day CCNA & CCNP Study Sprint!
Our first 21-Day Sprint was a huge success — we saw amazing check-ins, great discussions, and a community that truly learned together. 🙌
Now we're back with a faster, tighter 14-Day Sprint — same energy, same prizes, easier to finish! 💪
📅 Sprint Period: Sep 14 – Sep 27 (UTC+8)
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① DM admin: "I'M IN + cert name" (e.g., I'M IN CCNA 200-301)
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1️⃣ $10 SPOTO Universal Coupon ×2
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30 560
Repost from Machine Learning
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 🤩
