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Machinelearning (@ai_machinelearning_big_data) Rus til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 293 399 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 326-o'rinni va Rossiya mintaqasida 1 283-o'rinni egallagan.

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невідомо sanasidan buyon loyiha tez o‘sib, 293 399 obunachiga ega bo‘ldi.

03 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -6 469 ga, so‘nggi 24 soatda esa -218 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

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Post qamrovi: Har bir post o‘rtacha 21 487 marta ko‘riladi; birinchi sutkada odatda 16 937 ta ko‘rish yig‘iladi.
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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”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 04 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.

293 399

Obunachilar

-21824 soatlar

-1 5287 kunlar

-6 46930 kunlar

21 487

Post ko'rishlar

~ 16 93724 soatlar

~ 18 77448 soatlar

7.32%

Muloqot nisbati

~ 6

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Postlar arxiv

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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

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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

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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

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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/

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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

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Now Open STT Featured On Azure Datasets https://azure.microsoft.com/en-us/services/open-datasets/catalog/open-speech-to-text/

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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

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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

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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

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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

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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

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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

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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/

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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

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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

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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

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How to use pandas and get financial data https://morioh.com/p/43f5305ac2da

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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

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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

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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