Machine learning books and papers

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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn

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📈 Аналитический обзор Telegram-канала Machine learning books and papers

Канал Machine learning books and papers (@machine_learn) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 24 506 подписчиков, занимая 8 028 место в категории Образование и 13 775 место в регионе Иран.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 24 506 подписчиков.

Согласно последним данным от 02 июля, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -109, а за последние 24 часа — 5, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 6.29%. В первые 24 часа после публикации контент обычно набирает 2.04% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 1 541 просмотров. В течение первых суток публикация набирает 500 просмотров.
Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 1.
Тематические интересы: Контент сосредоточен на ключевых темах, таких как disorder, psy, مقاله, framework, graph.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
“Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn”

Благодаря высокой частоте обновлений (последние данные получены 03 июля, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.

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Подписчики

+524 часа

-147 дней

-10930 день

1 541

Просмотры поста

~ 50024 часа

~ 61348 часов

6.29%

Коэффициент вовлеченности

Нет данных

Постов в день

Ads index

beta

Архив постов

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@Machine_learn Deep learning of dynamical attractors from time series measurements Code: https://github.com/williamgilpin/fnn Paper: https://arxiv.org/abs/2002.05909

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🤝We strongly believe 💯 that education is to be free. 🤝We spend lots of money on our studies from school to graduation. 🤝Why you are still spending thousands on online courses. 🤝From now you can get any online course for free. 🤝Join our channel. 🔑@freeonlinecourses_paid_to_free🔑

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@Machine_learn 160+ Data Science Interview Questions https://hackernoon.com/160-data-science-interview-questions-415s3y2a

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Learn Keras for Deep Neural Networks — Jojo Moolayil (en) 2019. #middle #book #keras @Machine_learn

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Learn Keras for Deep Neural Networks — Jojo Moolayil (en) 2019. #middle #book #keras @Machine_learn

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@Machine_learn Fresh picks from ArXiv This week is full of CVPR and AISTATS 20 accepted papers, new surveys, more submissions to ICML and KDD, and new GNN models 📚 @Machine_learn CVPR 20 * Unbiased Scene Graph Generation from Biased Training * Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction * 4D Association Graph for Realtime Multi-person Motion Capture Using Multiple Video Cameras * Representations, Metrics and Statistics For Shape Analysis of Elastic Graphs * Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs * Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning * SketchGCN: Semantic Sketch Segmentation with Graph Convolutional Networks @Machine_learn Survey * Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks * Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective * Adversarial Attacks and Defenses on Graphs: A Review and Empirical Study * Knowledge Graphs on the Web -- an Overview @Machine_learn GNN * Infinitely Wide Graph Convolutional Networks: Semi-supervised Learning via Gaussian Processes * Can graph neural networks count substructures? by group of Joan Bruna * Heterogeneous Graph Neural Networks for Malicious Account Detection by group of Le Song @Machine_learn AISTATS 20 * Permutation Invariant Graph Generation via Score-Based Generative Modeling @Machine_learn KDD 20 * PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting @Machine_learn ICML 20 * Semi-supervised Anomaly Detection on Attributed Graphs * Inverse Graphics GAN: Learning to Generate 3D Shapes from Unstructured 2D Data * Permutohedral-GCN: Graph Convolutional Networks with Global Attention @Machine_learn Graph Theory * Finding large matchings in 1-planar graphs of minimum degree 3 * Trapping problem on star-type graphs with applications * On Fast Computation of Directed Graph Laplacian Pseudo-Inverse

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@Machine_learn Fresh picks from ArXiv ICML 20 submissions, AISTATS 20, graphs in math, and Stephen Hawking 👨‍🔬 ICML 2020 submissions Fast Detection of Maximum Common Subgraph via Deep Q-Learning (https://arxiv.org/abs/2002.03129) Random Features Strengthen Graph Neural Networks (https://arxiv.org/abs/2002.03155) Hierarchical Generation of Molecular Graphs using Structural Motifs (https://arxiv.org/pdf/2002.03230.pdf) Graph Neural Distance Metric Learning with Graph-Bert (https://arxiv.org/abs/2002.03427) Segmented Graph-Bert for Graph Instance Modeling (https://arxiv.org/abs/2002.03283) Haar Graph Pooling (https://arxiv.org/abs/1909.11580) Constant Time Graph Neural Networks (https://arxiv.org/abs/1901.07868) @Machine_learn AISTATS 20 Laplacian-Regularized Graph Bandits: Algorithms and Theoretical Analysis (https://arxiv.org/abs/1907.05632) @Machine_learn Math Some arithmetical problems that are obtained by analyzing proofs and infinite graphs (https://arxiv.org/abs/2002.03075) Extra pearls in graph theory (https://arxiv.org/abs/1812.06627) Distance Metric Learning for Graph Structured Data (https://arxiv.org/abs/2002.00727) @Machine_learn Surveys Generalized metric spaces. Relations with graphs, ordered sets and automata : A survey (https://arxiv.org/abs/2002.03019) @Machine_learn Stephen Hawking 👨‍🔬 Stephen William Hawking: A Biographical Memoir (https://arxiv.org/abs/2002.03185)

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@Machine_learn Graph ML Surveys A good way to start in this domain is to read what people already have done. Videos * Learning on Non-Euclidean Domains * Stanford Course CS 224w @Machine_learn GNN * Graph Neural Networks: A Review of Methods and Applications 2018 * A Comprehensive Survey on Graph Neural Networks 2019 * A Gentle Introduction to Deep Learning for Graphs 2019 * Deep Learning on Graphs: A Survey 2018 * Relational inductive biases, deep learning, and graph networks 2018 * Geometric deep learning: going beyond Euclidean data 2016 * Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview 2019 @Machine_learn Graph kernels * A Survey on Graph Kernels 2019 * Graph Kernels: A Survey 2019 @Machine_learn Adversarial Attacks * Adversarial Attack and Defense on Graph Data: A Survey 2018 @Machine_learn Representation Learning * Learning Representations of Graph Data -- A Survey 2019 * Representation Learning on Graphs: Methods and Applications 2017 @Machine_learn

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https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset #Corona_virus dataset

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@Machine_learn Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html Code: https://colab.research.google.com/github/google-research/text-to-text-transfer-transformer/blob/master/notebooks/t5-trivia.ipynb Github: https://github.com/google-research/text-to-text-transfer-transformer

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@Machine_learn IBM Data Science and AI Programs Free for 30 Days https://onlinecoursesgalore.com/ibm-data-science-ai-coursera/ Coursera: https://www.coursera.org/promo/ibmdscommunity?ranMID=40328&ranEAID

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#Coronavirus

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Deep Reinforcement Learning with Guar Performance #DL #book #RL @Machine_learn

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Deep learning of dynamical attractors from time series measurements Embed complex time series using autoencoders and a loss function based on penalizing false-nearest-neighbors. Code: https://github.com/williamgilpin/fnn Paper: https://arxiv.org/abs/2002.05909

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GANILLA: Generative Adversarial Networks for Image to Illustration Translation. Github: https://github.com/giddyyupp/ganilla Dataset: https://github.com/giddyyupp/ganilla/blob/master/docs/datasets.md Paper: https://arxiv.org/abs/2002.05638v1

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با عرض سلام و ادب خدمت کلیه کسانی که در این گروه و کانال شرف حضور دارند و با عرض خسته نباشید خدمت گردانندگان گروه ♦️مردمی بودن به حرف و شعار نیست در دل مردم جای گرفتن با عکس و پوستر و تبلیغات میلیونی میسر نیست، باید یک کاندیدای واقعی را به عمل شناخت نه به زبان. ♦️همچنان که مستحضر هستید کاندیدای مردمی و همرنگ ملت محمدرحیم حاتمی در پاسخ به دعوت جمعی از جوانان یکی از روستاهای دور افتاده بخش سیلوانا( اورسی) امروز عصر(یکشنبه 98.11.27) در شرایط بد آب و هوایی راهی آنجا شدند و پس از حضور در جمع آنها و پاسخ به سوالاتشان در مسیر برگشت به دلیل بارش برف و لغزندگی راه روستایی خودروی حامل آقای حاتمی و همراهانش دچار سانحه شده و از جاده خارج و چپ می‌شود و سپس به کمک اهالی محترم کاندیدای مردمی و همراهانش از خودروی تصادفی خارج و به شهر منتقل شدند، بنابه خواست کاندیدای محترم و جهت همراهی و همدلی با مردم علی رغم کوفتگی جسمی و اثرات ناشی از تصادف در بین مردم در ستاد خویش حضور یافتند که با حضور به موقع اورژانس در محل ستاد معاینه سرپایی صورت گرفت که خوشبختانه مشکل جدی و حادی برای وی و همراهانش وجود نداشت و قرار شد برای معاینات تکمیلی به مراکز درمانی مراجعه نمایند♦️ صدیق عباسی رئیس ستاد مرکزی انتخاباتی محمد رحیم حاتمی لینک کانال↙️ https://t.me/joinchat/AAAAAFkTvNIdZPqaS79Tgw لینک گروه حامیان↙️↙️ https://t.me/joinchat/FKK-PkyqX-cqCyl6OHcM0A

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https://t.me/Machine_learn

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Reinforcement learning #deeplearning #reinforcement #python @Machine_learn

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@Machine_learn Learning to See Transparent Objects ClearGrasp uses 3 neural networks: a network to estimate surface normals, one for occlusion boundaries (depth discontinuities), and one that masks transparent objects Google research: https://ai.googleblog.com/2020/02/learning-to-see-transparent-objects.html Code: https://github.com/Shreeyak/cleargrasp Dataset: https://sites.google.com/view/transparent-objects 3D Shape Estimation of Transparent Objects for Manipulation: https://sites.google.com/view/cleargrasp

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Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with Python @Machine_learn