ru
Feedback
Machine Learning

Machine Learning

Открыть в Telegram

Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

Больше
Machine Learning with PythonСеть:Machine Learning with PythonСирия228Технологии и приложения3 344

📈 Аналитический обзор Telegram-канала Machine Learning

Канал Machine Learning (@machinelearning9) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 40 208 подписчиков, занимая 3 344 место в категории Технологии и приложения и 228 место в регионе Сирия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 40 208 подписчиков.

Согласно последним данным от 03 июля, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 338, а за последние 24 часа — 9, при этом общий охват остаётся высоким.

  • Статус верификации: Не верифицирован
  • Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 2.04%. В первые 24 часа после публикации контент обычно набирает 2.42% реакций от общего числа подписчиков.
  • Охват публикаций: В среднем каждый пост получает 822 просмотров. В течение первых суток публикация набирает 973 просмотров.
  • Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 3.
  • Тематические интересы: Контент сосредоточен на ключевых темах, таких как distance, insidead, gpu, learning, degree.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
“Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho”

Благодаря высокой частоте обновлений (последние данные получены 04 июля, 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.

40 208
Подписчики
+924 часа
+727 дней
+33830 день
Архив постов
Machine Learning
Machine Learningverified-telegram-icon
40 221
📌 Automatic Differentiation (AutoDiff): A Brief Intro with Examples 🗂 Category: DEEP LEARNING 🕒 Date: 2024-10-11 | ⏱️ Read
📌 Automatic Differentiation (AutoDiff): A Brief Intro with Examples 🗂 Category: DEEP LEARNING 🕒 Date: 2024-10-11 | ⏱️ Read time: 11 min read An introduction to the mechanics of AutoDiff, exploring its mathematical principles, implementation strategies, and applications

Machine Learning
Machine Learningverified-telegram-icon
40 221
📌 Topic Alignment for NLP Recommender Systems 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-11 | ⏱️ Read time: 18 mi
📌 Topic Alignment for NLP Recommender Systems 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-11 | ⏱️ Read time: 18 min read Leveraging topic modeling to align user queries with document themes, enhancing the relevance and contextual…

Machine Learning
Machine Learningverified-telegram-icon
40 221
📌 A Mixed-Methods Approach to Offline Evaluation of News Recommender Systems 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2
📌 A Mixed-Methods Approach to Offline Evaluation of News Recommender Systems 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-11 | ⏱️ Read time: 8 min read Combining reader feedback from surveys with behavioral click data to optimize content personalization.

Machine Learning
Machine Learningverified-telegram-icon
40 221
📌 Understanding Automatic Differentiation in JAX: A Deep Dive 🗂 Category: DEEP LEARNING 🕒 Date: 2024-10-11 | ⏱️ Read time:
📌 Understanding Automatic Differentiation in JAX: A Deep Dive 🗂 Category: DEEP LEARNING 🕒 Date: 2024-10-11 | ⏱️ Read time: 12 min read Unleashing the Gradient: How JAX Makes Automatic Differentiation Feel Like Magic

Machine Learning
Machine Learningverified-telegram-icon
40 221
📌 Common Misconceptions About Data Science 🗂 Category: CAREER ADVICE 🕒 Date: 2024-10-11 | ⏱️ Read time: 7 min read Data sc
📌 Common Misconceptions About Data Science 🗂 Category: CAREER ADVICE 🕒 Date: 2024-10-11 | ⏱️ Read time: 7 min read Data science advice that you should question

Machine Learning
Machine Learningverified-telegram-icon
40 221
📌 Bursting the AI Hype Bubble Once and for All 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-12 | ⏱️ Read time: 11 m
📌 Bursting the AI Hype Bubble Once and for All 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-12 | ⏱️ Read time: 11 min read Misinformation and poor research: a case study

Machine Learning
Machine Learningverified-telegram-icon
40 221
📌 Gaussian Naive Bayes, Explained: A Visual Guide with Code Examples for Beginners 🗂 Category: DATA SCIENCE 🕒 Date: 2024-1
📌 Gaussian Naive Bayes, Explained: A Visual Guide with Code Examples for Beginners 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-12 | ⏱️ Read time: 8 min read Bell-shaped assumptions for better predictions

Machine Learning
Machine Learningverified-telegram-icon
40 221
📌 Improve Your RAG Context Recall by 95% with an Adapted Embedding Model. 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-1
📌 Improve Your RAG Context Recall by 95% with an Adapted Embedding Model. 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-10-12 | ⏱️ Read time: 11 min read Step by Step Model Adaptation Code and Results Attached.

Machine Learning
Machine Learningverified-telegram-icon
40 221
📌 Why the 2024 Nobel Prize in (AI for) Chemistry Matters So Much 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-12 |
📌 Why the 2024 Nobel Prize in (AI for) Chemistry Matters So Much 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-12 | ⏱️ Read time: 6 min read To Demis Hassabis and John Jumper, from DeepMind, and to David Baker, leader of the…

Machine Learning
Machine Learningverified-telegram-icon
40 221
📌 Upgrading to Prefect Push Workers on AWS ECS 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-10-12 | ⏱️ Read time: 6 min read
📌 Upgrading to Prefect Push Workers on AWS ECS 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-10-12 | ⏱️ Read time: 6 min read Upgrade from Prefect 2.0 to 3.0 and use the new Push Work Pools that greatly…

Machine Learning
Machine Learningverified-telegram-icon
40 221
📌 Linear Discriminant Analysis (LDA) 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-12 | ⏱️ Read time: 13 min read Discover
📌 Linear Discriminant Analysis (LDA) 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-12 | ⏱️ Read time: 13 min read Discover how LDA helps identify critical data features

Machine Learning
Machine Learningverified-telegram-icon
40 221
📌 Top 5 Principles for Building User-Friendly Data Tables 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-10-13 | ⏱️ Read time:
📌 Top 5 Principles for Building User-Friendly Data Tables 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-10-13 | ⏱️ Read time: 9 min read Designing intuitive and reliable tables that your data team will love

Machine Learning
Machine Learningverified-telegram-icon
40 221
📌 Recruiting vs. Interviewing for Data Roles in Diverse Markets 🗂 Category: CAREER ADVICE 🕒 Date: 2024-10-13 | ⏱️ Read tim
📌 Recruiting vs. Interviewing for Data Roles in Diverse Markets 🗂 Category: CAREER ADVICE 🕒 Date: 2024-10-13 | ⏱️ Read time: 12 min read Factors of success in recruiting and interviewing after applying for 150+ positions and reviewing 500+…

Machine Learning
Machine Learningverified-telegram-icon
40 221
📌 How to Perform A/B Testing with Hypothesis Testing in Python: A Comprehensive Guide 🗂 Category: DATA SCIENCE 🕒 Date: 202
📌 How to Perform A/B Testing with Hypothesis Testing in Python: A Comprehensive Guide 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-13 | ⏱️ Read time: 11 min read A Step-by-Step Guide to Making Data-Driven Decisions with Practical Python Examples

Machine Learning
Machine Learningverified-telegram-icon
40 221
📌 Bringing Structure to Your Data 🗂 Category: 🕒 Date: 2024-10-14 | ⏱️ Read time: 13 min read Testing assumptions with path
📌 Bringing Structure to Your Data 🗂 Category: 🕒 Date: 2024-10-14 | ⏱️ Read time: 13 min read Testing assumptions with path models

Machine Learning
Machine Learningverified-telegram-icon
40 221
📌 lintsampler: a new way to quickly get random samples from any distribution 🗂 Category: PROBABILITY 🕒 Date: 2024-10-14 |
📌 lintsampler: a new way to quickly get random samples from any distribution 🗂 Category: PROBABILITY 🕒 Date: 2024-10-14 | ⏱️ Read time: 5 min read lintsampler is a pure Python package that can easily and efficiently generate random samples from…

Machine Learning
Machine Learningverified-telegram-icon
40 221
📌 Product-Oriented ML: A Guide for Data Scientists 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-14 | ⏱️ Read time:
📌 Product-Oriented ML: A Guide for Data Scientists 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-14 | ⏱️ Read time: 30 min read How to build ML products users love

Machine Learning
Machine Learningverified-telegram-icon
40 221
📌 How to Set Bid Guardrails in PPC Marketing 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-14 | ⏱️ Read time: 14 min read Witho
📌 How to Set Bid Guardrails in PPC Marketing 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-14 | ⏱️ Read time: 14 min read Without controls, bidding algorithms can be quite volatile. Learn how to protect performance through adding…

Machine Learning
Machine Learningverified-telegram-icon
40 221
📌 PyTorch Optimizers Aren’t Fast Enough. Try These Instead 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-14 | ⏱️ Read time: 12
📌 PyTorch Optimizers Aren’t Fast Enough. Try These Instead 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-14 | ⏱️ Read time: 12 min read These 4 advanced optimizers will open your mind.

Machine Learning
Machine Learningverified-telegram-icon
40 221
📌 Florence-2: Advancing Multiple Vision Tasks with a Single VLM Model 🗂 Category: 🕒 Date: 2024-10-14 | ⏱️ Read time: 8 min
📌 Florence-2: Advancing Multiple Vision Tasks with a Single VLM Model 🗂 Category: 🕒 Date: 2024-10-14 | ⏱️ Read time: 8 min read A Guided Exploration of Florence-2’s Zero-Shot Capabilities: Captioning, Object Detection, Segmentation and OCR.