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Machine Learning with Python

Machine Learning with Python

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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

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📈 Аналитический обзор Telegram-канала Machine Learning with Python

Канал Machine Learning with Python (@codeprogrammer) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 67 829 подписчиков, занимая 2 404 место в категории Образование и 5 049 место в регионе Индия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 67 829 подписчиков.

Согласно последним данным от 05 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 77, а за последние 24 часа — 9, при этом общий охват остаётся высоким.

  • Статус верификации: Не верифицирован
  • Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 2.60%. В первые 24 часа после публикации контент обычно набирает 2.50% реакций от общего числа подписчиков.
  • Охват публикаций: В среднем каждый пост получает 1 767 просмотров. В течение первых суток публикация набирает 1 695 просмотров.
  • Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 6.
  • Тематические интересы: Контент сосредоточен на ключевых темах, таких как insidead, learning, degree, evaluation, algorithm.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

Благодаря высокой частоте обновлений (последние данные получены 06 июня, 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 Образование.

67 829
Подписчики
+924 часа
+587 дней
+7730 день
Архив постов
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📱 Python enthusiasts, this is for you — 15 BEST REPOSITORIES on GitHub for learning Python ▶️ Awesome Python — https://github.com/vinta/awesome-python — the largest and most authoritative collection of frameworks, libraries, and resources for Python — a must-save ▶️ TheAlgorithms/Python — https://github.com/TheAlgorithms/Python — a huge collection of algorithms and data structures written in Python ▶️ Project-Based-Learning — https://github.com/practical-tutorials/project-based-learning — learning Python (and not only) through real projects ▶️ Real Python Guide — https://github.com/realpython/python-guide — a high-quality guide to the Python ecosystem, tools, and best practices ▶️ Materials from Real Python — https://github.com/realpython/materials — a collection of code and projects for Real Python articles and courses ▶️ Learn Python — https://github.com/trekhleb/learn-python — a reference with explanations, examples, and exercises ▶️ Learn Python 3 — https://github.com/jerry-git/learn-python3 — a convenient guide to modern Python 3 with tasks ▶️ Python Reference — https://github.com/rasbt/python_reference — cheat sheets, scripts, and useful tips from one of the most respected Python authors ▶️ 30-Days-Of-Python — https://github.com/Asabeneh/30-Days-Of-Python — a 30-day challenge: from syntax to more complex topics ▶️ Python Programming Exercises — https://github.com/zhiwehu/Python-programming-exercises — 100+ Python tasks with answers ▶️ Coding Problems — https://github.com/MTrajK/coding-problems — tasks on algorithms and data structures, including for preparation for interviews ▶️ Projects — https://github.com/karan/Projects — a list of ideas for pet projects (not just Python). Great for practice ▶️ 100-Days-Of-ML-Code — https://github.com/Avik-Jain/100-Days-Of-ML-Code — machine learning in Python in the format of a challenge ▶️ 30-Seconds-of-Python — https://github.com/30-seconds/30-seconds-of-python — useful snippets and tricks for everyday tasks ▶️ Geekcomputers/Python — https://github.com/geekcomputers/Python — various scripts: from working with the network to automation tasks React ♥️ for more posts like this 💛

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A huge cheat sheet for Python, Django, Plotly, Matplotlib, P.pdf7.41 KB

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git lfs install
git clone https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt models/stable-video-diffusion-img2vid-xt
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--config config/infer.yaml \
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𝐕𝐢𝐬𝐮𝐚𝐥 𝐛𝐥𝐨𝐠 on Vision Transformers is live. https://vizuaranewsletter.com/p/vision-transformers?r=5b5pyd&utm_campaign=post&utm_medium=web Learn how ViT works from the ground up, and fine-tune one on a real classification dataset.
CNNs process images through small sliding filters. Each filter only sees a tiny local region, and the model has to stack many layers before distant parts of an image can even talk to each other. Vision Transformers threw that whole approach out. ViT chops an image into patches, treats each patch like a token, and runs self-attention across the full sequence. Every patch can attend to every other patch from the very first layer. No stacking required. That global view from layer one is what made ViT surpass CNNs on large-scale benchmarks. 𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐛𝐥𝐨𝐠 𝐜𝐨𝐯𝐞𝐫𝐬: - Introduction to Vision Transformers and comparison with CNNs - Adapting transformers to images: patch embeddings and flattening - Positional encodings in Vision Transformers - Encoder-only structure for classification - Benefits and drawbacks of ViT - Real-world applications of Vision Transformers - Hands-on: fine-tuning ViT for image classification The Image below shows Self-attention connects every pixel to every other pixel at once. Convolution only sees a small local window. That's why ViT captures things CNNs miss, like the optical illusion painting where distant patches form a hidden face. The architecture is simple. Split image into patches, flatten them into embeddings (like words in a sentence), run them through a Transformer encoder, and the class token collects info from all patches for the final prediction. Patch in, class out. Inside attention: each patch (query) compares itself to all other patches (keys), softmax gives attention weights, and the weighted sum of values produces a new representation aware of the full image, visualizes what the CLS token actually attends to through attention heatmaps. The second half of the blog is hands-on code. I fine-tuned ViT-Base from google (86M params) on the Oxford-IIIT Pet dataset, 37 breeds, ~7,400 images. 𝐁𝐥𝐨𝐠 𝐋𝐢𝐧𝐤 https://vizuaranewsletter.com/p/vision-transformers?r=5b5pyd&utm_campaign=post&utm_medium=web
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