Machinelearning

前往频道在 Telegram

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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网络:Machinelearning 俄罗斯1 281 技术与应用326...

📈 Telegram 频道 Machinelearning 的分析概览

频道 Machinelearning (@ai_machinelearning_big_data) 俄语语言赛道中的是活跃参与者。目前社区聚集了 293 457 名订阅者，在 技术与应用 类别中位列第 326，并在 俄罗斯 地区排名第 1 281 位。

📊 受众指标与增长动态

自 невідомо 创建以来，项目保持高速增长，吸引了 293 457 名订阅者。

根据 02 七月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 -6 464，过去 24 小时变化为 -249，整体触达仍然可观。

认证状态： 未认证
互动率 (ER)： 平均受众互动率为 7.49%。内容发布后 24 小时内通常能获得 5.71% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 21 989 次浏览，首日通常累积 16 765 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 173。
主题关注点： 内容集中在 openai, claude, api, gemini, контекст 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台：
“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”

凭借高频更新（最新数据采集于 03 七月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。

293 457

订阅者

-24924 小时

-1 5267 天

-6 46430 天

21 989

帖子浏览量

~ 16 76524 小时

~ 18 67648 小时

7.49%

参与率

~ 6

每日帖子数

Ads index

beta

帖子存档

293 404

From singing to musical scores: Estimating pitch with SPICE and Tensorflow Hub Pitch is quantified by frequency, measured in Hertz (Hz), where one Hz corresponds to one cycle per second. The higher the frequency, the higher the note. https://blog.tensorflow.org/2020/06/estimating-pitch-with-spice-and-tensorflow-hub.html Model: https://tfhub.dev/google/spice/2 Colab code: https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/spice.ipynb

293 404

Using Multi-Scale Attention for Semantic Segmentation https://devblogs.nvidia.com/using-multi-scale-attention-for-semantic-segmentation/ Paper: https://arxiv.org/abs/2005.10821

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Rethinking the Truly Unsupervised Image-to-Image Translation - Official PyTorch Implementation Here proposed the truly unsupervised image-to-image translation method (TUNIT) that simultaneously learns to separate image domains via an information-theoretic approach and generate corresponding images using the estimated domain labels. Github: https://github.com/clovaai/tunit Paper: https://arxiv.org/abs/2006.06500v1

293 404

Synthesizing High-Resolution Images with StyleGAN2 https://www.youtube.com/watch?v=9QuDh3W3lOY&feature=emb_logo Article: https://news.developer.nvidia.com/synthesizing-high-resolution-images-with-stylegan2/

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Хайп вокруг Big Data уже прошёл, но идеальное хранилище под большие данные — всегда актуальная тема: чтобы легко масштабировалось под любой объем и предоставляло расширенные возможности по обработке данных. Современные базы бывают именно такими. Мы расскажем, почему DWH лучше строить в облаке и какие есть Best Practice для архитектуры. 👉 Регистрируйтесь на наш вебинар про управляемую СУБД на основе Greenplum, разработанную специально для решения аналитических задач — от BI до AI. Встретимся в четверг 18 июня, онлайн. Начало в 17:00 по Москве, регистрация обязательна: https://events.webinar.ru/mcs/arenadatadb

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Now Open STT Featured On Azure Datasets https://azure.microsoft.com/en-us/services/open-datasets/catalog/open-speech-to-text/

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VirTex: Learning Visual Representations from Textual Annotations VirTex is a pretraining approach which uses semantically dense captions to learn visual representations.VirTex matches or outperforms models which use ImageNet for pretraining -- both supervised or unsupervised -- despite using up to 10x fewer images. https://kdexd.github.io/virtex/ Github: https://github.com/kdexd/virtex Paper: arxiv.org/abs/2006.06666

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Deploy a Machine Learning Pipeline to the Cloud Using a Docker Container https://www.kdnuggets.com/2020/06/deploy-machine-learning-pipeline-cloud-docker.html

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YOLOv5 is Here: State-of-the-Art Object Detection at 140 FPS Realtime object detection is improving quickly. The rate of improvement is improving even more quickly. The results are stunning. https://blog.roboflow.ai/yolov5-is-here/ Github: https://github.com/ultralytics/yolov5 GCP Quickstart: https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart

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Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection Github: https://github.com/implus/GFocal Paper: https://arxiv.org/abs/2006.04388v1

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PEGASUS: A State-of-the-Art Model for Abstractive Text Summarization https://ai.googleblog.com/2020/06/pegasus-state-of-art-model-for.html Github: https://github.com/google-research/pegasus Paper: https://arxiv.org/abs/1912.08777

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Part 2: Fast, scalable and accurate NLP: Why TFX is a perfect match for deploying BERT https://blog.tensorflow.org/2020/06/part-2-fast-scalable-and-accurate-nlp.html Code: https://colab.research.google.com/github/tensorflow/workshops/blob/master/blog/TFX_Pipeline_for_Bert_Preprocessing.ipynb

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Introducing Neuropod, Uber ATG’s Open Source Deep Learning Inference Engine Neuropod makes it easy for researchers to build models in a framework of their choosing while also simplifying productionization of these models. It currently supports TensorFlow, PyTorch, TorchScript, and Keras. https://eng.uber.com/introducing-neuropod/ Github: https://github.com/uber/neuropod Neuropod Tutorial: https://neuropod.ai/tutorial/

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A Scalable and Cloud-Native Hyperparameter Tuning System Katib is a Kubernetes-based system for Hyperparameter Tuning and Neural Architecture Search. Katib supports a number of ML frameworks, including TensorFlow, Apache MXNet, PyTorch, XGBoost, and others. Github: https://github.com/kubeflow/katib Getting started with Katib: https://www.kubeflow.org/docs/components/hyperparameter-tuning/hyperparameter/ Paper: https://arxiv.org/abs/2006.02085v1

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DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution Recursive Feature Pyramid implements thinking twice at the macro level, where the outputs of FPN are brought back to each stage of the bottom-up backbone through feedback connections Github: https://github.com/joe-siyuan-qiao/DetectoRS Paper: https://arxiv.org/abs/2006.02334v1

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Introduction to Convolutional Neural Networks The article focuses on explaining key components in CNN and its implementation using Keras python library. https://www.kdnuggets.com/2020/06/introduction-convolutional-neural-networks.html

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How to use pandas and get financial data https://morioh.com/p/43f5305ac2da

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A Smooth Representation of SO(3) for Deep Rotation Learning with Uncertainty In this work presented a novel symmetric matrix representation of rotations that is singularity-free and requires marginal computational overhead Website: https://papers.starslab.ca/bingham-rotation-learning/ Paper: https://arxiv.org/abs/2006.01031 Github: https://github.com/utiasSTARS/bingham-rotation-learn

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Acme: A research framework for reinforcement learning Acme strives to expose simple, efficient, and readable agents, that serve both as reference implementations of popular algorithms and as strong baselines, while still providing enough flexibility to do novel research Github: https://github.com/deepmind/acme Paper: https://arxiv.org/abs/2006.00979

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Text Mining in Python: Steps and Examples This blog summarizes text preprocessing and covers the NLTK steps including Tokenization, Stemming, Lemmatization, POS tagging, Named entity recognition and Chunking. https://www.kdnuggets.com/2020/05/text-mining-python-steps-examples.html