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 276 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 293 167 subscribers, ranking 326 in the Technologies & Applications category and 1 276 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 293 167 subscribers.

According to the latest data from 04 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -6 366 over the last 30 days and by -131 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 7.35%. Within the first 24 hours after publication, content typically collects 5.62% reactions from the total number of subscribers.
Post reach: On average, each post receives 21 569 views. Within the first day, a publication typically gains 16 480 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 168.
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 05 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.

293 167

Subscribers

-13124 hours

-1 4647 days

-6 36630 days

21 569

Post views

~ 16 48024 hours

~ 18 29048 hours

7.35%

Engagement rate

~ 6

Posts per day

Ads index

beta

Posts Archive

293 155

Synthetic Celebrity Faces at 128x128 Resolution After Tuning Generated by the Progressive Growing GAN How to Train a Progressive Growing GAN in Keras for Synthesizing Faces https://machinelearningmastery.com/how-to-train-a-progressive-growing-gan-in-keras-for-synthesizing-faces/

293 155

Accelerated CNN Training Through Gradient Approximation https://arxiv.org/abs/1908.05460

293 155

Освойте самую востребованную технологию искусственного интеллекта! У вас уже есть базовое понимание машинного обучения и знание языка Python? Повысьте компетенции и получите практические навыки по программированию глубоких нейронных сетей! В SkillFactory открыт новый набор на курс "Deep Learning и нейронные сети" https://clc.to/6onwXA разработанный при поддержке NVIDIA Corporation. Проекты, над которыми вы будете работать, включают: ● создание нейронной сети для распознавания рукописных цифр; ● обучение рекурентной сети задачам прогнозирования временных рядов; ● разработку нейросетевого чат-бота; ● создание модели для идентификации лиц и генерации лиц на основе архитектуры GAN; ● разработку агента для игры на основе DQN алгоритма. Вы познакомитесь с основными библиотеками для Deep Learning, такими как TensorFlow, Keras и другими. 🔥 Жаркая новость: супер-скидки Августа -30%, узнайте подробности: https://clc.to/6onwXA

293 155

Awesome Machine Learning A curated list of awesome machine learning frameworks, libraries and software (by language). libraries: https://github.com/josephmisiti/awesome-machine-learning books: https://github.com/josephmisiti/awesome-machine-learning/blob/master/books.md

293 155

How technology impacts the way of producing and consuming food at AGRO & TECH conference by Sistema_VC Founders of startups from the UK, the Netherlands and Russia will talk about how AI provides people with better nutrition and globally helps fighting hunger. You will learn how agriculture and tech interact and network with startup founders, innovation managers, investors and the media in the industry. Place: Moscow, Kollektiv Space, Bol'shoy Znamenskiy 2s3, metro Kropotkinskaya Date: August 21st, 6 pm Register free: https://is.gd/JgvmUA

293 155

How vFlat used the TFLite GPU delegate for real time inference to scan books https://medium.com/tensorflow/how-vflat-used-the-tflite-gpu-delegate-for-real-time-inference-to-scan-books-df3bb86a4eb7

293 155

New advances in natural language processing to better connect people https://ai.facebook.com/blog/new-advances-in-natural-language-processing-to-better-connect-people

293 155

VisualBERT: A Simple and Performant Baseline for Vision and Language https://arxiv.org/abs/1908.03557

293 155

How to Implement Progressive Growing GAN Models in Keras https://machinelearningmastery.com/how-to-implement-progressive-growing-gan-models-in-keras/

293 155

Project Euphonia’s Personalized Speech Recognition for Non-Standard Speech http://ai.googleblog.com/2019/08/project-euphonias-personalized-speech.html

293 155

Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning https://eng.uber.com/research/collaborative-multi-agent-dialogue-model-training-via-reinforcement-learning/ article: https://arxiv.org/pdf/1907.05507.pdf

293 155

The Best of AI: New Articles Published This Month (July 2019) https://blog.sicara.com/07-2019-best-ai-new-articles-this-month-3e1fa3f6c321

293 155

A Finnish News Corpus for Named Entity Recognition https://arxiv.org/abs/1908.04212

293 155

New paper on training with pseudo-labels for semantic segmentation Semi-Supervised Segmentation of Salt Bodies in Seismic Images: SOTA (1st place) at TGS Salt Identification Challenge. Github: https://github.com/ybabakhin/kaggle_salt_bes_phalanx ArXiV: https://arxiv.org/abs/1904.04445 #GCPR2019 #Segmentation #CV

293 155

A Gentle Introduction to the Progressive Growing GAN https://machinelearningmastery.com/introduction-to-progressive-growing-generative-adversarial-networks/

293 155

LVIS: A Dataset for Large Vocabulary Instance Segmentation https://arxiv.org/abs/1908.03195

293 155

A new model for word embeddings that are resilient to misspellings https://ai.facebook.com/blog/-a-new-model-for-word-embeddings-that-are-resilient-to-misspellings-/ https://github.com/facebookresearch/moe?fbclid=IwAR3pCHx4-8oWTqgYqUnKHxcVWdDzPuOVTL0sTidyDBX9J7UPt2HcWxRG9AA

293 155

NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Data https://arxiv.org/abs/1908.03190

293 155

How to Develop a CycleGAN for Image-to-Image Translation with Keras https://machinelearningmastery.com/cyclegan-tutorial-with-keras/

293 155

Interpreting Latent Space of GANs for Semantic Face Editing https://shenyujun.github.io/InterFaceGAN/ code: https://github.com/ShenYujun/InterFaceGAN.git