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

Ko'proq ko'rsatish

Tarmoq:Machinelearning Rossiya1 290 Texnologiyalar & Aralashmalar328...

📈 Telegram kanali Machinelearning analitikasi

Machinelearning (@ai_machinelearning_big_data) Rus til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 292 388 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 328-o'rinni va Rossiya mintaqasida 1 290-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 292 388 obunachiga ega bo‘ldi.

08 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -6 274 ga, so‘nggi 24 soatda esa -221 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

Tasdiqlash holati: Tasdiqlanmagan
Jalb etish (ER): Auditoriya o‘rtacha 7.46% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 5.47% ini tashkil etuvchi reaksiyalarni to‘playdi.
Post qamrovi: Har bir post o‘rtacha 21 812 marta ko‘riladi; birinchi sutkada odatda 16 003 ta ko‘rish yig‘iladi.
Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 159 ta reaksiya keladi.
Tematik yo‘nalishlar: Kontent openai, claude, api, gemini, контекст kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“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”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 09 Iyul, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

292 388

Obunachilar

-22124 soatlar

-1 3547 kunlar

-6 27430 kunlar

21 812

Post ko'rishlar

~ 16 00324 soatlar

~ 17 78848 soatlar

7.46%

Muloqot nisbati

~ 6

Kuniga postlar

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Postlar arxiv

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

https://habr.com/company/piter/blog/429358/

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

Дорисовывание лиц с помощью машинного обучения https://habr.com/post/428756/

292 388

Building client routing / semantic search and clustering arbitrary external corpuses at Profi.ru https://habr.com/post/428674/

292 388

https://habr.com/company/yandex/blog/428700/

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

https://habr.com/company/hh/blog/427477/

292 388

BMW Machine Learning Weekly — Week 02 https://towardsdatascience.com/bmw-machine-learning-weekly-week-02-229404823b19