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 292 388 名订阅者，在 技术与应用 类别中位列第 328，并在 俄罗斯 地区排名第 1 290 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 292 388 名订阅者。
根据 08 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 -6 274，过去 24 小时变化为 -221，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 7.46%。内容发布后 24 小时内通常能获得 5.47% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 21 812 次浏览，首日通常累积 16 003 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 159。
- 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 09 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。
292 388
订阅者
-22124 小时
-1 3547 天
-6 27430 天
帖子存档
292 388
Predicting Professional Players’ Chess Moves with Deep Learning
https://towardsdatascience.com/predicting-professional-players-chess-moves-with-deep-learning-9de6e305109e?source=collection_home---4------0---------------------
292 388
PySyft
PySyft is a Python library for secure, private Deep Learning. PySyft decouples private data from model training, using Multi-Party Computation (MPC) within PyTorch.
https://github.com/OpenMined/PySyft
292 388
How to train Neural Network faster with optimizers?
https://towardsdatascience.com/how-to-train-neural-network-faster-with-optimizers-d297730b3713
292 388
Driving Computer Vision with Deep Learning
https://wayve.ai/blog/2018/10/8/vision-for-driving-with-deep-learning
292 388
Kaggle Competition — Image Classification
How to build a CNN model that can predict the classification of the input images using transfer learning
https://towardsdatascience.com/kaggle-competition-image-classification-676dee6c0f23
292 388
Artificial Intelligence meets Art: Neural Transfer Style
https://towardsdatascience.com/artificial-intelligence-meets-art-neural-transfer-style-50e1c07aa7f7
292 388
Using 3D visualizations to tune hyperparameters in ML models
https://towardsdatascience.com/using-3d-visualizations-to-tune-hyperparameters-of-ml-models-with-python-ba2885eab2e9
292 388
PyTorch implementation of Google AI's BERT model with a script to load Google's pre-trained models
https://github.com/huggingface/pytorch-pretrained-BERT
292 388
Representation Power of Neural Networks
https://towardsdatascience.com/representation-power-of-neural-networks-8e99a383586
292 388
The MAME RL Algorithm Training Toolkit
This Python library has the to potential to train your reinforcement learning algorithm on almost any arcade game. It is currently available on Linux systems and works as a wrapper around MAME. The toolkit allows your algorithm to step through gameplay while recieving the frame data and internal memory address values for tracking the games state, along with sending actions to interact with the game.
https://github.com/M-J-Murray/MAMEToolkit
292 388
Building client routing / semantic search and clustering arbitrary external corpuses at Profi.ru
https://habr.com/post/428674/
292 388
Applying Machine Learning to classify an unsupervised text document
https://towardsdatascience.com/applying-machine-learning-to-classify-an-unsupervised-text-document-e7bb6265f52
292 388
Facebook open sourced Horizon, an end-to-end applied reinforcement learning platform built on #PyTorch 1.0. Horizon uses RL to optimize systems in large-scale production environments and we're excited to make it accessible to anyone using #RL at scale.
https://code.fb.com/ml-applications/horizon/
292 388
Semantic Segmentation of Seismic Reflection Images
https://nikolasent.github.io/deeplearning/competitions/2018/10/24/Semantic-Segmentation-of-Seismic-Reflection-Images.html
292 388
Introducing AdaNet: Fast and Flexible AutoML with Learning Guarantees
https://ai.googleblog.com/2018/10/introducing-adanet-fast-and-flexible.html
292 388
BMW Machine Learning Weekly — Week 02
https://towardsdatascience.com/bmw-machine-learning-weekly-week-02-229404823b19
