Machine learning books and papers

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

📈 Análisis del canal de Telegram Machine learning books and papers

El canal Machine learning books and papers (@machine_learn) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 24 506 suscriptores, ocupando la posición 8 028 en la categoría Educación y el puesto 13 775 en la región Irán.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 24 506 suscriptores.

Según los últimos datos del 02 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -109, y en las últimas 24 horas de 5, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 6.29%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.04% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 1 541 visualizaciones. En el primer día suele acumular 500 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 1.
Intereses temáticos: El contenido se centra en temas clave como disorder, psy, مقاله, framework, graph.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 03 julio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.

24 506

Suscriptores

+524 horas

-147 días

-10930 días

1 541

Visitas de la publicación

~ 50024 horas

~ 61348 horas

6.29%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Archivo de publicaciones

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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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@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