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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 278 Texnologiyalar & Aralashmalar328...

📈 Telegram kanali Machinelearning analitikasi

Machinelearning (@ai_machinelearning_big_data) Rus til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 292 964 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 328-o'rinni va Rossiya mintaqasida 1 278-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 292 964 obunachiga ega bo‘ldi.

06 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -6 314 ga, so‘nggi 24 soatda esa -187 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

Tasdiqlash holati: Tasdiqlanmagan
Jalb etish (ER): Auditoriya o‘rtacha 7.37% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 5.45% ini tashkil etuvchi reaksiyalarni to‘playdi.
Post qamrovi: Har bir post o‘rtacha 21 579 marta ko‘riladi; birinchi sutkada odatda 15 979 ta ko‘rish yig‘iladi.
Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 159 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 07 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.

292 964

Obunachilar

-18724 soatlar

-1 3257 kunlar

-6 31430 kunlar

21 579

Post ko'rishlar

~ 15 97924 soatlar

~ 17 85348 soatlar

7.37%

Muloqot nisbati

~ 6

Kuniga postlar

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Postlar arxiv

292 956

A General Decoupled Learning Framework for Parameterized Image Operators https://arxiv.org/abs/1907.05852

292 956

New fast.ai course: A Code-First Introduction to Natural Language Processing https://www.fast.ai/2019/07/08/fastai-nlp/ Github: https://github.com/fastai/course-nlp Videos: https://www.youtube.com/playlist?list=PLtmWHNX-gukKocXQOkQjuVxglSDYWsSh9

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Postuf — продуктовая компания, которая занимается разработкой приложения, позволяющего узнать всю информацию о человеке из сети. Вводите имя, загружаете фото и получаете информацию об увлечениях, круге общения, последних совершённых действиях — всё, что только можно узнать о человеке из открытых источников. Сейчас они ищут разработчиков на Android, iOS и бэкенд (PHP, MySQL, Redis). Условия комфортные — офис в центре Москвы (ст.м. Чкаловская), Macbook Pro для работы, бесплатные обеды и ежегодный рост з/п на 20к. Подробнее о компании и вакансии: https://postuf.com/

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How to Implement Wasserstein Loss for Generative Adversarial Networks https://machinelearningmastery.com/how-to-implement-wasserstein-loss-for-generative-adversarial-networks/

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Simple Deep Q Network w/Pytorch: https://youtu.be/UlJzzLYgYoE Reinforcement Learning Crash Course: https://youtu.be/sOiNMW8k4T0 Policy Gradients w/Tensorflow: https://youtu.be/UT9pQjVhcaU Deep Q Learning w/Tensorflow https://youtu.be/3Ggq_zoRGP4 Code Your Own RL Environments https://youtu.be/vmrqpHldAQ0 How to Spec a Deep Learning PC: https://youtu.be/xsnVlMWQj8o Deep Q Learning w/ Pytorch: https://youtu.be/RfNxXlO6BiA Machine Learning Freelancing https://youtu.be/6M04ZTLE_O4 Code from video: https://github.com/philtabor/Youtube-Code-Repository

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Learning to learn with quantum neural networks via classical neural networks https://arxiv.org/abs/1907.05415

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An implementation of the BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer (Interspeech 2019) Article: https://arxiv.org/pdf/1907.03040.pdf Github: https://github.com/guanlinchao/bert-dst

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How to Code the GAN Training Algorithm and Loss Functions https://machinelearningmastery.com/how-to-code-the-generative-adversarial-network-training-algorithm-and-loss-functions/

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Multilingual Universal Sentence Encoder for Semantic Retrieval http://ai.googleblog.com/2019/07/multilingual-universal-sentence-encoder.html

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TRFL a library of reinforcement learning building blocks By the Research Engineering team at DeepMind: https://github.com/deepmind/trfl

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Advancing Semi-supervised Learning with Unsupervised Data Augmentation http://ai.googleblog.com/2019/07/advancing-semi-supervised-learning-with.html

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A Tour of Generative Adversarial Network Models https://machinelearningmastery.com/tour-of-generative-adversarial-network-models/

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Bayesian deep learning with hierarchical prior: Predictions from limited and noisy data Article: https://arxiv.org/abs/1907.04240 PDF: https://arxiv.org/pdf/1907.04240.pdf

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Predicting the Generalization Gap in Deep Neural Networks http://ai.googleblog.com/2019/07/predicting-generalization-gap-in-deep.html

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Neural Architecture Search at CVPR 2019 https://drsleep.github.io/NAS-at-CVPR-2019/

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How to Identify and Diagnose GAN Failure Modes https://machinelearningmastery.com/practical-guide-to-gan-failure-modes/

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Rank-consistent Ordinal Regression for Neural Networks Article: https://arxiv.org/abs/1901.07884 PyTorch: https://github.com/Raschka-research-group/coral-cnn

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Literature of Deep Learning for Graphs This is a paper list about deep learning for graphs. https://github.com/DeepGraphLearning/LiteratureDL4Graph

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Check the data science channel there you will find a lot of articles, links and advanced researches . Join and learn hot topics of data science @opendatascience

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How to Develop a Conditional GAN (cGAN) From Scratch Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images.