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

Network:Machinelearning Russia1 283 Technologies & Applications326...

📈 Analytical overview of Telegram channel Machinelearning

Channel Machinelearning (@ai_machinelearning_big_data) in the Russian language segment is an active participant. Currently, the community unites 293 399 subscribers, ranking 326 in the Technologies & Applications category and 1 283 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 293 399 subscribers.

According to the latest data from 03 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -6 469 over the last 30 days and by -218 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 7.32%. Within the first 24 hours after publication, content typically collects 5.77% reactions from the total number of subscribers.
Post reach: On average, each post receives 21 487 views. Within the first day, a publication typically gains 16 937 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 169.
Thematic interests: Content is focused on key topics such as openai, claude, api, gemini, контекст.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“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”

Thanks to the high frequency of updates (latest data received on 04 July, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

293 399

Subscribers

-21824 hours

-1 5287 days

-6 46930 days

21 487

Post views

~ 16 93724 hours

~ 18 77448 hours

7.32%

Engagement rate

~ 6

Posts per day

Ads index

beta

Posts Archive

293 340

Magenta: Music and Art Generation with Machine Intelligence Magenta is a research project exploring the role of machine learning in the process of creating art and music. Github: https://github.com/tensorflow/magenta Colab notebooks: https://colab.research.google.com/notebooks/magenta/hello_magenta/hello_magenta.ipynb Paper: https://arxiv.org/abs/1902.08710v2

293 340

Neural Networks are Function Approximation Algorithms https://machinelearningmastery.com/neural-networks-are-function-approximators/

293 340

FastText: stepping through the code fastText is a library for efficient learning of word representations and sentence classification. Article: https://medium.com/@mariamestre/fasttext-stepping-through-the-code-259996d6ebc4 Habr ru: https://habr.com/ru/post/492432/ Code: https://github.com/facebookresearch/fastText

293 340

🌐 Fast and Easy Infinitely Wide Networks with Neural Tangents Neural Tangents is a high-level neural network API for specifying complex, hierarchical, neural networks of both finite and infinite width. Neural Tangents allows researchers to define, train, and evaluate infinite networks as easily as finite ones. https://ai.googleblog.com/2020/03/fast-and-easy-infinitely-wide-networks.html Colab notebook: https://colab.research.google.com/github/google/neural-tangents/blob/master/notebooks/neural_tangents_cookbook.ipynb#scrollTo=Lt74vgCVNN2b Code: https://github.com/google/neural-tangents Paper: https://arxiv.org/abs/1912.02803

293 340

On the Texture Bias for Few-Shot CNN Segmentation This repository contains the code for deep auto-encoder-decoder network for few-shot semantic segmentation with state of the art results on FSS 1000 class dataset and Pascal 5i Code: https://github.com/rezazad68/fewshot-segmentation Paper: https://arxiv.org/abs/2003.04052v1 Download 1000-class dataset

293 340

Google just announced their new TensorFlow Developer Certificate, which is a great way to showcase your TF skills. Check it out https://www.tensorflow.org/certificate

293 340

Lagrangian Neural Networks In contrast to Hamiltonian Neural Networks, these models do not require canonical coordinates and perform well in situations where generalized momentum is difficult to compute Code: https://github.com/MilesCranmer/lagrangian_nns Paper: https://arxiv.org/abs/2003.04630v1

293 340

Big Data снова врывается в обычную жизнь. Персонализированная система рекомендаций, разработанная с использованием больших данных, увеличивает доход Amazon до 30% в год. Как добиваться впечатляющих результатов в работе с помощью data-driven подхода? Пройти курс по Data Science от SkillFactory! 12 месяцев обучения, за которые ты освоишь на практике: – Python, – Machine и deep learning, – Data engineering, – Менеджмент для Data Science. Менторы и персональные тьюторы всегда на связи и готовы прийти на помощь; по итогам обучения у каждого студента готово портфолио из 10 проектов, а лучшие студенты будут трудоустроены в топовые компании. 🌷Весенняя распродажа в SkillFactory! Не упусти курс со скидкой -40%: https://clc.to/5GM6GQ

293 340

🎇Announcing TensorFlow Quantum: An Open Source Library for Quantum Machine Learning https://ai.googleblog.com/2020/03/announcing-tensorflow-quantum-open.html

293 340

Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation (PyTorch) Code: https://github.com/cmhungsteve/SSTDA Paper: https://arxiv.org/abs/2003.02824

293 340

Step-By-Step Framework for Imbalanced Classification Projects https://machinelearningmastery.com/framework-for-imbalanced-classification-projects/

293 340

Measuring Compositional Generalization https://ai.googleblog.com/2020/03/measuring-compositional-generalization.html

293 340

Simulating the Universe in TensorFlow https://blog.tensorflow.org/2020/03/simulating-universe-in-tensorflow.html

293 340

A software toolkit for research on general-purpose text understanding models jiant is a software toolkit for natural language processing research, designed to facilitate work on multitask learning and transfer learning for sentence understanding tasks https://jiant.info/ Code: https://github.com/nyu-mll/jiant Paper: https://arxiv.org/pdf/2003.02249v1.pdf

293 340

MARKOV CHAIN MONTE CARLO (MCMC) SAMPLING https://www.tweag.io/posts/2019-10-25-mcmc-intro1.html Habr ru: https://habr.com/ru/company/piter/blog/491268/

293 340

ML HACK - a Machine Learning hackathon in Saint-Petersburg! On March 13-15, a large-scaled hackathon dedicated to Machine Learning will be held in St. Petersburg at the ITMO University. 48 hours 1 topic - ML 7 tasks 425k roubles $150,000 for acceleration Sign up here - http://mlhack.tech/ Join @mlhackitmo chat if you are looking for a team.

293 340

Sign Language Recognition with Deep Learning and PyTorch https://theaisummer.com/Sign-Language-Recognition-with-PyTorch/

293 340

Deep Image Spatial Transformation for Person Image Generation Pose-guided person image generation is to transform a source person image to a target pose. Github: https://github.com/RenYurui/Global-Flow-Local-Attention Paper: https://arxiv.org/abs/2003.00696v1

293 340

Better scalability with Cloud TPU pods and TensorFlow 2.1 https://cloud.google.com/blog/products/ai-machine-learning/better-scalability-with-cloud-tpu-pods-and-tensorflow-2-1 TensorFlow Official Models: https://github.com/tensorflow/models/tree/master/official

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5 причин стать специалистом в Data Science в 2020 году 1. Огромный спрос на профи Data Scientists анализируют данные и прокачивают искусственный интеллект. С 2012 года спрос на эту профессию увеличился на 650%. Компании больше не хотят полагаться на гипотезы — им нужны точные данные для принятия решений и спецы, готовые с ними работать. 2. Карьерные перспективы Мечтаешь работать в западной компании? Google, Amazon, Uber, Facebook — в этих корпорациях и в крупных российских компаниях открыто больше 2 000 вакансий для мастеров машинного обучения и работы с данными. 3. Высокая зарплата Потребность в Data Scientists растет — и им готовы много платить. 200 000 рублей — средняя зарплата специалиста по работе с данными в России. 4. Гарантированное трудоустройство Skillbox гарантирует трудоустройство всем студентам. Правда, специалиста в Data Science с готовыми проектами в портфолио и так с руками оторвут. 5. Полгода бесплатного обучения Первый платёж только через 6 месяцев обучения! На курсе «Профессия Data Scientist» от Skillbox тебя ждет много практики под руководством специалистов из NVIDIA, НИУ ВШЭ и ivi.ru. Сможешь освоить крутую профессию, даже если пока ничего не умеешь. Узнай подробности курса: https://clc.to/SVVAqg.