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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
Show more📈 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
Posts Archive
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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