اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
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
نمایش بیشتر📈 تحلیل کانال تلگرام Machinelearning
کانال Machinelearning (@ai_machinelearning_big_data) در بخش زبانی روسی بازیگری فعال است. در حال حاضر جامعه شامل 293 399 مشترک است و جایگاه 326 را در دسته فناوری و برنامهها و رتبه 1 283 را در منطقه روسيا دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 293 399 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 03 ژوئیه, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -6 469 و در ۲۴ ساعت گذشته برابر -218 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 7.32% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 5.77% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 21 487 بازدید دریافت میکند. در اولین روز معمولاً 16 937 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 169 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند openai, claude, api, gemini, контекст تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“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”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 04 ژوئیه, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامهها تبدیل کردهاند.
293 399
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آرشیو پست ها
293 404
From singing to musical scores: Estimating pitch with SPICE and Tensorflow Hub
Pitch is quantified by frequency, measured in Hertz (Hz), where one Hz corresponds to one cycle per second. The higher the frequency, the higher the note.
https://blog.tensorflow.org/2020/06/estimating-pitch-with-spice-and-tensorflow-hub.html
Model: https://tfhub.dev/google/spice/2
Colab code: https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/spice.ipynb
293 404
Using Multi-Scale Attention for Semantic Segmentation
https://devblogs.nvidia.com/using-multi-scale-attention-for-semantic-segmentation/
Paper: https://arxiv.org/abs/2005.10821
293 404
Rethinking the Truly Unsupervised Image-to-Image Translation - Official PyTorch Implementation
Here proposed the truly unsupervised image-to-image translation method (TUNIT) that simultaneously learns to separate image domains via an information-theoretic approach and generate corresponding images using the estimated domain labels.
Github: https://github.com/clovaai/tunit
Paper: https://arxiv.org/abs/2006.06500v1
293 404
Synthesizing High-Resolution Images with StyleGAN2
https://www.youtube.com/watch?v=9QuDh3W3lOY&feature=emb_logo
Article: https://news.developer.nvidia.com/synthesizing-high-resolution-images-with-stylegan2/
293 404
Хайп вокруг Big Data уже прошёл, но идеальное хранилище под большие данные — всегда актуальная тема: чтобы легко масштабировалось под любой объем и предоставляло расширенные возможности по обработке данных.
Современные базы бывают именно такими. Мы расскажем, почему DWH лучше строить в облаке и какие есть Best Practice для архитектуры.
👉 Регистрируйтесь на наш вебинар про управляемую СУБД на основе Greenplum, разработанную специально для решения аналитических задач — от BI до AI.
Встретимся в четверг 18 июня, онлайн. Начало в 17:00 по Москве, регистрация обязательна: https://events.webinar.ru/mcs/arenadatadb
293 404
Now Open STT Featured On Azure Datasets
https://azure.microsoft.com/en-us/services/open-datasets/catalog/open-speech-to-text/
293 404
VirTex: Learning Visual Representations from Textual Annotations
VirTex is a pretraining approach which uses semantically dense captions to learn visual representations.VirTex matches or outperforms models which use ImageNet for pretraining -- both supervised or unsupervised -- despite using up to 10x fewer images.
https://kdexd.github.io/virtex/
Github: https://github.com/kdexd/virtex
Paper: arxiv.org/abs/2006.06666
293 404
Deploy a Machine Learning Pipeline to the Cloud Using a Docker Container
https://www.kdnuggets.com/2020/06/deploy-machine-learning-pipeline-cloud-docker.html
293 404
YOLOv5 is Here: State-of-the-Art Object Detection at 140 FPS
Realtime object detection is improving quickly. The rate of improvement is improving even more quickly. The results are stunning.
https://blog.roboflow.ai/yolov5-is-here/
Github: https://github.com/ultralytics/yolov5
GCP Quickstart: https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart
293 404
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
Github: https://github.com/implus/GFocal
Paper: https://arxiv.org/abs/2006.04388v1
293 404
PEGASUS: A State-of-the-Art Model for Abstractive Text Summarization
https://ai.googleblog.com/2020/06/pegasus-state-of-art-model-for.html
Github: https://github.com/google-research/pegasus
Paper: https://arxiv.org/abs/1912.08777
293 404
Part 2: Fast, scalable and accurate NLP: Why TFX is a perfect match for deploying BERT
https://blog.tensorflow.org/2020/06/part-2-fast-scalable-and-accurate-nlp.html
Code: https://colab.research.google.com/github/tensorflow/workshops/blob/master/blog/TFX_Pipeline_for_Bert_Preprocessing.ipynb
293 404
Introducing Neuropod, Uber ATG’s Open Source Deep Learning Inference Engine
Neuropod makes it easy for researchers to build models in a framework of their choosing while also simplifying productionization of these models.
It currently supports TensorFlow, PyTorch, TorchScript, and Keras.
https://eng.uber.com/introducing-neuropod/
Github: https://github.com/uber/neuropod
Neuropod Tutorial: https://neuropod.ai/tutorial/
293 404
A Scalable and Cloud-Native Hyperparameter Tuning System
Katib is a Kubernetes-based system for Hyperparameter Tuning and Neural Architecture Search. Katib supports a number of ML frameworks, including TensorFlow, Apache MXNet, PyTorch, XGBoost, and others.
Github: https://github.com/kubeflow/katib
Getting started with Katib: https://www.kubeflow.org/docs/components/hyperparameter-tuning/hyperparameter/
Paper: https://arxiv.org/abs/2006.02085v1
293 404
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution
Recursive Feature Pyramid implements thinking twice at the macro level, where the outputs of FPN are brought back to each stage of the bottom-up backbone through feedback connections
Github: https://github.com/joe-siyuan-qiao/DetectoRS
Paper: https://arxiv.org/abs/2006.02334v1
293 404
Introduction to Convolutional Neural Networks
The article focuses on explaining key components in CNN and its implementation using Keras python library.
https://www.kdnuggets.com/2020/06/introduction-convolutional-neural-networks.html
293 404
A Smooth Representation of SO(3) for Deep Rotation Learning with Uncertainty
In this work presented a novel symmetric matrix representation of rotations that is singularity-free and requires marginal computational overhead
Website: https://papers.starslab.ca/bingham-rotation-learning/
Paper: https://arxiv.org/abs/2006.01031
Github: https://github.com/utiasSTARS/bingham-rotation-learn
293 404
Acme: A research framework for reinforcement learning
Acme strives to expose simple, efficient, and readable agents, that serve both as reference implementations of popular algorithms and as strong baselines, while still providing enough flexibility to do novel research
Github: https://github.com/deepmind/acme
Paper: https://arxiv.org/abs/2006.00979
293 404
Text Mining in Python: Steps and Examples
This blog summarizes text preprocessing and covers the NLTK steps including Tokenization, Stemming, Lemmatization, POS tagging, Named entity recognition and Chunking.
https://www.kdnuggets.com/2020/05/text-mining-python-steps-examples.html
