Machine learning books and papers

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

📈 Analytical overview of Telegram channel Machine learning books and papers

Channel Machine learning books and papers (@machine_learn) in the English language segment is an active participant. Currently, the community unites 24 506 subscribers, ranking 8 028 in the Education category and 13 775 in the Iran region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 506 subscribers.

According to the latest data from 02 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -109 over the last 30 days and by 5 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 6.29%. Within the first 24 hours after publication, content typically collects 2.04% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 541 views. Within the first day, a publication typically gains 500 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 1.
Thematic interests: Content is focused on key topics such as disorder, psy, مقاله, framework, graph.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn”

Thanks to the high frequency of updates (latest data received on 03 July, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

24 506

Subscribers

+524 hours

-147 days

-10930 days

1 541

Post views

~ 50024 hours

~ 61348 hours

6.29%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

24 506

@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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@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

24 506

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

24 506

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

24 506

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

24 506

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

24 506

https://t.me/Machine_learn

24 506

Reinforcement learning #deeplearning #reinforcement #python @Machine_learn

24 506

@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