现已上线！2025 年 Telegram 研究 — 年度关键洞察 
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
显示更多📈 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 293 260 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 1 276 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 293 260 名订阅者。
根据 04 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 -6 366，过去 24 小时变化为 -131，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 7.35%。内容发布后 24 小时内通常能获得 5.62% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 21 569 次浏览，首日通常累积 16 480 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 168。
- 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 05 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。
293 260
订阅者
-13124 小时
-1 4647 天
-6 36630 天
帖子存档
293 187
Depth Hints are complementary depth suggestions which improve monocular depth estimation algorithms trained from stereo pairs
code: https://github.com/nianticlabs/depth-hints
paper: https://arxiv.org/abs/1909.09051
dataset : https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html
293 187
Neural networks in NLP are vulnerable to adversarially crafted inputs.
We show that they can be trained to become certifiably robust against input perturbations such as typos and synonym substitution in text classification:
https://arxiv.org/abs/1909.01492
293 187
If you are programmer or a student / graduate or PHD. IF you have basic knowledge of higher mathematics, probability theory and python? If you dream to try yourself in Data Science?
MegaFon announces a competition for participation in the five-day intensive BigDataCamp!
You could become a participant in the training, just go through testing and write a motivation letter.
All details on the website: http://bigdatacamp.megafon.ru/
293 187
🗣 Using AI-generated questions to train NLP systems
https://ai.facebook.com/blog/research-in-brief-unsupervised-question-answering-by-cloze-translation/
code: https://github.com/facebookresearch/UnsupervisedQA
paper: https://research.fb.com/publications/unsupervised-question-answering-by-cloze-translation/
293 187
🎲 Discrete Probability Distributions for Machine Learning
https://machinelearningmastery.com/discrete-probability-distributions-for-machine-learning/
293 187
🔍 DeepPavlov: An open-source library for end-to-end dialogue systems and chatbots
article: https://medium.com/tensorflow/deeppavlov-an-open-source-library-for-end-to-end-dialog-systems-and-chatbots-31cf26849e37
research: https://colab.research.google.com/github/deepmipt/dp_notebooks/blob/master/DP_tf.ipynb
code: https://github.com/deepmipt/DeepPavlov
293 187
NVIDIA Announces TensorRT 6; Breaks 10 millisecond barrier for BERT-Large
https://news.developer.nvidia.com/tensorrt6-breaks-bert-record/
293 187
A Gentle Introduction to Probability Distributions
https://machinelearningmastery.com/what-are-probability-distributions/
293 187
This AI Clears Up Your Hazy Photos
Double-DIP: Unsupervised Image Decomposition via Coupled Deep-Image-Priors
article: http://www.wisdom.weizmann.ac.il/~vision/DoubleDIP/
code: https://github.com/yossigandelsman/DoubleDIP
video: https://www.youtube.com/watch?v=qkHK1QdQ2Fk
293 187
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models
https://arxiv.org/abs/1909.03186v1
293 187
The largest publicly available language model: CTRL has 1.6B parameters and can be guided by control codes for style, content, and task-specific behavior.
code: https://github.com/salesforce/ctrl
article: https://einstein.ai/presentations/ctrl.pdf
https://blog.einstein.ai/introducing-a-conditional-transformer-language-model-for-controllable-generation/
293 187
Facebook Research at Interspeech 2019
https://ai.facebook.com/blog/facebook-research-at-interspeech-2019/
Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions
https://research.fb.com/publications/sequence-to-sequence-speech-recognition-with-time-depth-separable-convolutions/
Unsupervised Singing Voice Conversion
https://research.fb.com/publications/unsupervised-singing-voice-conversion/
293 187
A Gentle Introduction to Uncertainty in Machine Learning
https://machinelearningmastery.com/uncertainty-in-machine-learning/
293 187
Using Deep Learning to Inform Differential Diagnoses of Skin Diseases
http://ai.googleblog.com/2019/09/using-deep-learning-to-inform.html
293 187
PyTorch Meta-learning Framework for Researchers
https://github.com/learnables/learn2learn
learn2learn is a PyTorch library for meta-learning implementations
http://learn2learn.net
293 187
5 Reasons to Learn Probability for Machine Learning
https://machinelearningmastery.com/why-learn-probability-for-machine-learning/
293 187
Machine Learning for Physics and the Physics of Learning Tutorials
overview: https://www.ipam.ucla.edu/programs/workshops/machine-learning-for-physics-and-the-physics-of-learning-tutorials/
videos: https://www.ipam.ucla.edu/videos/
293 187
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