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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Сеть:📈 Аналитический обзор Telegram-канала Data Analytics
Канал Data Analytics (@dataanalyticsx) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 30 560 подписчиков, занимая 4 280 место в категории Технологии и приложения и 20 902 место в регионе Россия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 30 560 подписчиков.
Согласно последним данным от 05 октября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 582, а за последние 24 часа — 24, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 5.28%. В первые 24 часа после публикации контент обычно набирает 1.16% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 1 612 просмотров. В течение первых суток публикация набирает 355 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 3.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как sellerflash, buybox, buyer, chaos, effortless.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Благодаря высокой частоте обновлений (последние данные получены 06 октября, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.
30 560
Подписчики
+2424 часа
+1597 дней
+58230 дней
Архив постов
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
Когда нужна альтернатива биржам
Bitpapa — 1M+ Users
Крипторынок меняется, а вместе с ним меняются привычные способы покупки и обмена криптовалюты.
Bitpapa предлагает альтернативный формат: P2P-сделки, криптокошелёк и обмен BTC, USDT и других активов в одном месте.
Более 100 способов оплаты — чтобы выбрать удобный вариант для сделки.
Попробуй Bitpapa.
Ad. 18+
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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Use 711TRIAL to get 1GB of residential proxy traffic for testing.
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After signing up, contact 711Proxy support and mention “711TRIAL” to claim your trial.
For eligible new users only.
30 560
Get Up to 500MB of Residential Proxy Traffic for Your Python Projects 🐍
Building a web scraper with Python?
ThorData helps developers collect public web data while handling proxy rotation, geo-targeting, and access restrictions.
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Eligible new users can receive up to 500MB of residential proxy traffic for testing.
Use channel code: PYTHONSCRAPE
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Available to eligible new users. The actual trial traffic may vary, up to a maximum of 500MB.
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🚀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. 🙌
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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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JOIN 14 Days Study Sprint: https://chat.whatsapp.com/KZrAj2HZ3Y5K9UhhNhrApf
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 🤩
