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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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📈 نظرة تحليلية على قناة تيليجرام Machine Learning with Python

تُعد قناة Machine Learning with Python (@codeprogrammer) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 67 809 مشتركاً، محتلاً المرتبة 2 416 في فئة التعليم والمرتبة 5 038 في منطقة الهند.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 67 809 مشتركاً.

بحسب آخر البيانات بتاريخ 09 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 70، وفي آخر 24 ساعة بمقدار 10، مع بقاء الوصول العام مرتفعاً.

  • حالة التحقق: غير موثّقة
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  • الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل insidead, learning, degree, evaluation, algorithm.

📝 الوصف وسياسة المحتوى

يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 10 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التعليم.

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Repost from Machine Learning
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Repost from AI & ML Papers
Tired of endless job boards and low offers? Unlock access to exclusive remote jobs from top startups—some with salaries $100k
Tired of endless job boards and low offers? Unlock access to exclusive remote jobs from top startups—some with salaries $100k+ and early-bird roles at $50/h and above. New high-paying openings posted daily—tech, marketing, design, and more. Ready to upgrade your career from anywhere? Check today’s top jobs now before they’re gone! #إعلان InsideAds

Tired of endless job hunting? Unlock high-paying remote jobs from top startups – fresh roles posted daily. Want early access
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This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visua
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_rRW2scgfRhOTc0https://t.me/Codeprogrammer

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😉 A list of the best YouTube videos To learn data science 1️⃣ SQL language ⬅️ Learning 💰 4-hour SQL course from zero to one hundred 💰 Window functions tutorial ⬅️ Projects 📎 Starting your first SQL project 💰 Data cleansing project 💰 Restaurant order analysis ⬅️ Interview 💰 How to crack the SQL interview? ➖➖➖ 2️⃣ Python ⬅️ Learning 💰 12-hour Python for Data Science course ⬅️ Projects 💰 Python project for beginners 💰 Analyzing Corona Data with Python ⬅️ Interview 💰 Python interview golden tricks 💰 Python Interview Questions ➖➖➖ 3️⃣ Statistics and machine learning ⬅️ Learning 💰 7-hour course in applied statistics 💰 Machine Learning Training Playlist ⬅️ Projects 💰 Practical ML Project ⬅️ Interview 💰 ML Interview Questions and Answers 💰 How to pass a statistics interview? ➖➖➖ 4️⃣ Product and business case studies ⬅️ Learning 💰 Building strong product understanding 💰 Product Metric Definition ⬅️ Interview 💰 Case Study Analysis Framework 💰 How to shine in a business interview?
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What is torch.nn really? When I started working with PyTorch, my biggest question was: "What is torch.nn?". This article expl
What is torch.nn really?
When I started working with PyTorch, my biggest question was: "What is torch.nn?".
This article explains it quite well. 📌 Read

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GPU by hand ✍️ I drew this to show how a GPU speeds up an array operation of 8 elements in parallel over 4 threads in 2 clock cycles. Read more 👇 CPU • It has one core. • Its global memory has 120 locations (0-119). • To use the GPU, it needs to copy data from the global memory to the GPU. • After GPU is done, it will copy the results back. GPU • It has four cores to run four threads (0-3). • It has a register file of 28 locations (0-27) • This register file has four banks (0-3). • All threads share the same register file. • But they must read/write using the four banks. • Each bank allows 2 reads (Read 0, Read 1) and 1 write in a single clock cycle.
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This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visua
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_rRW2scgfRhOTc0https://t.me/Codeprogrammer

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Introduction to Deep Learning As we continue to push the boundaries of what's possible with artificial intelligence, I wanted to take a moment to share some insights on one of the most exciting fields in AI: Deep Learning. Deep Learning is a subset of machine learning that uses neural networks to analyze and interpret data. These neural networks are designed to mimic the human brain, with layers of interconnected nodes (neurons) that process and transmit information. What makes Deep Learning so powerful? Ability to learn from large datasets: Deep Learning algorithms can learn from vast amounts of data, including images, speech, and text. Improved accuracy: Deep Learning models can achieve state-of-the-art performance in tasks such as image recognition, natural language processing, and speech recognition. Ability to generalize: Deep Learning models can generalize well to new, unseen data, making them highly effective in real-world applications. Real-world applications of Deep Learning Computer Vision: Self-driving cars, facial recognition, object detection Natural Language Processing: Language translation, text summarization, sentiment analysis Speech Recognition: Virtual assistants, voice-controlled devices. #DeepLearning #AI #MachineLearning #NeuralNetworks #ArtificialIntelligence #DataScience #ComputerVision #NLP #SpeechRecognition #TechInnovation
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This GitHub Repo will be very helpful if you are preparing for a data science technical interview. This question bank covers:
This GitHub Repo will be very helpful if you are preparing for a data science technical interview. This question bank covers: 1️⃣ Machine Learning Interview Questions & Answers 2️⃣ Deep Learning Interview Questions & Answers 2.1. Deep learning basics 2.2. Deep learning for computer vision questions 2.3. Deep learning for NLP & LLMs 3️⃣ Probability Interview Questions & Answers 4️⃣ Statistics Interview Questions & Answers 5️⃣ SQL Interview Questions & Answers 6️⃣ Python Questions & Answers ⚡ You can find the repo link in the comments section!

Auto-Encoder & Backpropagation by hand ✍️ lecture video ~ 📺 https://byhand.ai/cv/10 It took me a few years to invent this me
Auto-Encoder & Backpropagation by hand ✍️ lecture video ~ 📺 https://byhand.ai/cv/10 It took me a few years to invent this method to show both forward and backward passes for a non-trivial case of a multi-layer perceptron over a batch of inputs, plus gradient descents over multiple epochs, while being able to hand calculate each step and code in Excel at the same time. = Chapters = • Encoder & Decoder (00:00) • Equation (10:09) • 4-2-4 AutoEncoder (16:38) • 6-4-2-4-6 AutoEncoder (18:39) • L2 Loss (20:49) • L2 Loss Gradient (27:31) • Backpropagation (30:12) • Implement Backpropagation (39:00) • Gradient Descent (44:30) • Summary (51:39)