Machinelearning

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Погружаемся в машинное обучение и Data Science Показываем как запускать любые LLm на пальцах. По всем вопросам - @haarrp @itchannels_telegram -🔥best channels Реестр РКН: clck.ru/3Fmqri

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Tarmoq:Machinelearning Rossiya1 276 Texnologiyalar & Aralashmalar326...

📈 Telegram kanali Machinelearning analitikasi

Machinelearning (@ai_machinelearning_big_data) Rus til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 293 260 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 326-o'rinni va Rossiya mintaqasida 1 276-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 293 260 obunachiga ega bo‘ldi.

04 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -6 366 ga, so‘nggi 24 soatda esa -131 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

Tasdiqlash holati: Tasdiqlanmagan
Jalb etish (ER): Auditoriya o‘rtacha 7.35% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 5.62% ini tashkil etuvchi reaksiyalarni to‘playdi.
Post qamrovi: Har bir post o‘rtacha 21 569 marta ko‘riladi; birinchi sutkada odatda 16 480 ta ko‘rish yig‘iladi.
Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 168 ta reaksiya keladi.
Tematik yo‘nalishlar: Kontent openai, claude, api, gemini, контекст kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Погружаемся в машинное обучение и Data Science Показываем как запускать любые LLm на пальцах. По всем вопросам - @haarrp @itchannels_telegram -🔥best channels Реестр РКН: clck.ru/3Fmqri”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 05 Iyul, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

293 260

Obunachilar

-13124 soatlar

-1 4647 kunlar

-6 36630 kunlar

21 569

Post ko'rishlar

~ 16 48024 soatlar

~ 18 29048 soatlar

7.35%

Muloqot nisbati

~ 6

Kuniga postlar

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Postlar arxiv

293 272

Introducing Hypothesis GU Funcs, an Open Source Python Package for Unit Testing https://eng.uber.com/hypothesis-gu-funcs-unit-testing/ Hypothesis General Universal Function Documentation https://hypothesis-gufunc.readthedocs.io/en/latest/

293 272

RecSim: A Configurable Simulation Platform for Recommender Systems https://ai.googleblog.com/2019/11/recsim-configurable-simulation-platform.html article: https://arxiv.org/abs/1909.04847 github: https://github.com/google-research/recsim

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DeepFovea: Using deep learning for foveated reconstruction in AR/VR https://ai.facebook.com/blog/deepfovea-using-deep-learning-for-foveated-reconstruction-in-ar-vr/ code: https://github.com/facebookresearch/DeepFovea full paper: https://research.fb.com/publications/deepfovea-neural-reconstruction-for-foveated-rendering-and-video-compression-using-learned-statistics-of-natural-videos/ @ai_machinelearning_big_data

293 272

What Does Stochastic Mean in Machine Learning? https://machinelearningmastery.com/stochastic-in-machine-learning/

293 272

Machine Learning with Python Cookbook — Chris Albon (en) 2018 @datascienceiot

293 272

Stacked Capsule Autoencoders https://github.com/google-research/google-research/tree/master/stacked_capsule_autoencoders paper : https://arxiv.org/abs/1906.06818 http://akosiorek.github.io/ml/2019/06/23/stacked_capsule_autoencoders.html

293 272

How to Connect Model Input Data With Predictions for Machine Learning https://machinelearningmastery.com/how-to-connect-model-input-data-with-predictions-for-machine-learning/

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Sharing our Experience Upgrading OpenNMT to TensorFlow 2.0 https://blog.tensorflow.org/2019/11/our-experience-upgrading-OpenNMT-to-TensorFlow.html code: https://github.com/OpenNMT/OpenNMT-tf OpenNMT: http://opennmt.net/

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Sberbank's subsidiary Cloud Technologies (provides cloud services under the SberCloud brand) showed the most powerful russian supercomputer Christofari. Power of the supercomputer is 6.67 penaflops (about 6.7 quadrillion operations per second). So Christofari be in the TOP-30 of the world rating.Access will be available for all AI Cloud subscribers. The cost of usage per min on a full power - 5750 RUB (about $90).

293 272

Research Guide: Pruning Techniques for Neural Networks https://heartbeat.fritz.ai/research-guide-pruning-techniques-for-neural-networks-d9b8440ab10d

293 272

Introducing the Next Generation of On-Device Vision Models: MobileNetV3 and MobileNetEdgeTPU http://ai.googleblog.com/2019/11/introducing-next-generation-on-device.html

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Sparse Networks from Scratch: Faster Training without Losing Performance https://arxiv.org/abs/1907.04840 https://timdettmers.com/2019/07/11/sparse-networks-from-scratch/ Sparse Learning Library and Sparse Momentum Resources https://github.com/TimDettmers/sparse_learning

293 272

How to Save a NumPy Array to File for Machine Learning https://machinelearningmastery.com/how-to-save-a-numpy-array-to-file-for-machine-learning/

293 272

Green AI vs Red AI https://arxiv.org/abs/1907.10597 Tackling Climate Change with Machine Learning https://www.reddit.com/r/MachineLearning/comments/da30mv/r_tackling_climate_change_with_machine_learning/

293 272

A Multimodal Language Dataset for Understanding Humor article: https://arxiv.org/pdf/1904.06618.pdf dataset: https://github.com/ROC-HCI/UR-FUNNY

293 272

14 Different Types of Learning in Machine Learning https://machinelearningmastery.com/types-of-learning-in-machine-learning/

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Освойте самую востребованную технологию искусственного интеллекта! Хотите в сжатые сроки получить практические навыки по программированию глубоких нейронных сетей? В SkillFactory в ноябре стартует курс онлайн-курс "Deep Learning и нейронные сети" https://clc.to/_dCORw (при поддержке NVIDIA Corporation). Здесь вы: попробуете свои силы в создании нейронной сети для распознавания рукописных цифр, обучении рекурентной сети задачам прогнозирования временных рядов, разработке нейросетевого чат-бота, создании модели для идентификации лиц и др. Курс основан на практике. Фокус и упор мы делаем не на математическом фундаменте, а именно на понимании задач и практическом применении решений. Узнайте больше о возможностях: https://clc.to/_dCORw

293 272

A Gentle Introduction to Maximum a Posteriori (MAP) for Machine Learning https://machinelearningmastery.com/maximum-a-posteriori-estimation/

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Numerical Computing with Python @datascienceiot

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This project is adapted from the original Dive Into Deep Learning book https://github.com/dsgiitr/d2l-pytorch