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 291 Texnologiyalar & Aralashmalar328...

📈 Telegram kanali Machinelearning analitikasi

Machinelearning (@ai_machinelearning_big_data) Rus til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 292 747 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 328-o'rinni va Rossiya mintaqasida 1 291-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 292 747 obunachiga ega bo‘ldi.

07 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -6 317 ga, so‘nggi 24 soatda esa -209 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

Tasdiqlash holati: Tasdiqlanmagan
Jalb etish (ER): Auditoriya o‘rtacha 7.45% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 5.46% ini tashkil etuvchi reaksiyalarni to‘playdi.
Post qamrovi: Har bir post o‘rtacha 21 817 marta ko‘riladi; birinchi sutkada odatda 15 977 ta ko‘rish yig‘iladi.
Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 160 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 08 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.

292 747

Obunachilar

-20924 soatlar

-1 3687 kunlar

-6 31730 kunlar

21 817

Post ko'rishlar

~ 15 97724 soatlar

~ 17 79548 soatlar

7.45%

Muloqot nisbati

~ 6

Kuniga postlar

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Postlar arxiv

292 713

PlotNeuralNet https://github.com/HarisIqbal88/PlotNeuralNet

292 713

DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning https://www.nature.com/articles/s41598-018-38343-3

292 713

Реализация моделей seq2seq в Tensorflow https://habr.com/ru/post/440472/

292 713

Convolutional Neural Nets in PyTorch https://blog.algorithmia.com/convolutional-neural-nets-in-pytorch/

292 713

GANimation: Anatomically-aware Facial Animation from a Single Image https://github.com/albertpumarola/GANimation

292 713

Introducing PlaNet: A Deep Planning Network for Reinforcement Learning https://ai.googleblog.com/2019/02/introducing-planet-deep-planning.html

292 713

How Should Self-Driving Cars Choose Who Not to Kill? https://medium.com/@MORGANMEAKER/how-should-self-driving-cars-choose-who-not-to-kill-442f2a5a1b59?source=topic_page---------------------------20

292 713

Russian AI Cup 2018, история 9 места https://habr.com/ru/post/440574/

292 713

Image Segmentation using Python’s scikit-image module https://towardsdatascience.com/image-segmentation-using-pythons-scikit-image-module-533a61ecc980

292 713

Box Convolution Layer for ConvNets This is a PyTorch implementation of the box convolution layer as introduced in the 2018 NeurIPS paper: https://github.com/shrubb/box-convolutions

292 713

What To Optimize for? Loss Function Cheat Sheet https://medium.com/@urimerhav/what-to-optimize-for-loss-function-cheat-sheet-5fc8b1339939

292 713

Nature Machine Intelligence

292 713

Introduction to gradient boosting on decision trees with Catboost Today I would like to share my experience with open source machine learning library, based on gradient boosting on decision trees, developed by Russian search engine company — Yandex. https://towardsdatascience.com/introduction-to-gradient-boosting-on-decision-trees-with-catboost-d511a9ccbd14

292 713

The Ancient Secrets of Computer Vision University of Washington. Free course This class is a general introduction to computer vision. It covers standard techniques in image processing like filtering, edge detection, stereo, flow, etc. , as well as newer, machine-learning based computer vision. https://pjreddie.com/courses/computer-vision/

292 713

Predict malignancy in cancer tumors with your own neural network https://towardsdatascience.com/predict-malignancy-in-breast-cancer-tumors-with-your-own-neural-network-and-the-wisconsin-dataset-76271a05e941?source=collection_home---4------0---------------------

292 713

A Simple Baseline for Bayesian Deep Learning https://github.com/wjmaddox/swa_gaussian

292 713

Deep Generative Modeling https://www.youtube.com/watch?v=yFBFl1cLYx8

292 713

A Comprehensive Introduction to Different Types of Convolutions in Deep Learning https://towardsdatascience.com/a-comprehensive-introduction-to-different-types-of-convolutions-in-deep-learning-669281e58215

292 713

Автономная езда по тротуару посредством OpenCV и Tensorflow https://habr.com/ru/post/439928/

292 713

Reflections on the State of AI: 2018 https://medium.com/evolution-one/reflections-on-the-state-of-ai-2018-46504e25263d?source=topic_page---------4------------------1