Machine Learning with Python
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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Machine Learning with Python (@codeprogrammer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 67 813 obunachidan iborat bo'lib, Taʼlim toifasida 2 411-o'rinni va Hindiston mintaqasida 5 035-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 67 813 obunachiga ega bo‘ldi.
07 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 55 ga, so‘nggi 24 soatda esa -2 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
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- Jalb etish (ER): Auditoriya o‘rtacha 2.62% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.56% ini tashkil etuvchi reaksiyalarni to‘playdi.
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Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 08 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
▶️ What tensors are and why they are needed ▶️ Tensor initialization: zeros, ones, random, similar size ▶️ Type conversion and switching between NumPy and PyTorch ▶️ Arithmetic, logical operations, tensor comparison ▶️ Matrix multiplication and batch computations ▶️ Broadcasting, view(), reshape(), changing dimensions ▶️ Indexing and slicing: how to access parts of a tensor ▶️ Notebook with code examplesA good starting material to understand the mechanics of tensors before moving on to models and training. ⛓ GitHub link tags: #useful ➡ @codeprogrammer
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