Machine learning books and papers

前往频道在 Telegram

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

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📈 Telegram 频道 Machine learning books and papers 的分析概览

频道 Machine learning books and papers (@machine_learn) 英语语言赛道中的是活跃参与者。目前社区聚集了 24 502 名订阅者，在教育类别中位列第 8 036，并在伊朗地区排名第 13 785 位。

📊 受众指标与增长动态

自 невідомо 创建以来，项目保持高速增长，吸引了 24 502 名订阅者。

根据 01 七月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 -127，过去 24 小时变化为 -5，整体触达仍然可观。

认证状态： 未认证
互动率 (ER)： 平均受众互动率为 7.47%。内容发布后 24 小时内通常能获得 2.04% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 1 829 次浏览，首日通常累积 500 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 1。
主题关注点： 内容集中在 disorder, psy, مقاله, framework, graph 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台：
“Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn”

凭借高频更新（最新数据采集于 02 七月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为教育类别中的关键影响点。

24 502

订阅者

-524 小时

-207 天

-12730 天

1 829

帖子浏览量

~ 50024 小时

~ 61348 小时

7.47%

参与率

无数据

每日帖子数

Ads index

beta

帖子存档

24 504

DENet: a deep architecture for audio surveillance applications GIthub: https://github.com/MiviaLab/DENet Paper: https://link.springer.com/article/10.1007/s00521-020-05572-5 @Machine_learn

24 504

👉Lecture Notes for Linear Algebra Featuring Python . GitHub link : https://github.com/MacroAnalyst/Linear_Algebra_With_Python @Machine_learn

24 504

#python #book @Machine_learn

24 504

TensorFlow Lite on GPU https://www.tensorflow.org/lite/performance/gpu_advanced @Machine_learn

24 504

🔇 Deep Noise Suppression Challenge – ICASSP 2021 Github: https://github.com/microsoft/DNS-Challenge Paper: https://arxiv.org/abs/2101.01902v1 Training and test datasets: https://github.com/microsoft/DNS-Challenge/tree/master/datasets Challenge: https://www.microsoft.com/en-us/research/academic-program/deep-noise-suppression-challenge-icassp-2021/ @Machine_learn

24 504

🚀 DALL·E is a 12-billion parameter version of GPT-3 trained to generate images from text descriptions Introduction: https://openai.com/blog/tags/multimodal/ Deepmind Blog: https://openai.com/blog/dall-e/ Github: https://github.com/openai/CLIP Paper: https://cdn.openai.com/papers/Learning_Transferable_Visual_Models_From_Natural_Language.pdf Colab: https://colab.research.google.com/github/openai/clip/blob/master/Interacting_with_CLIP.ipynb @Machine_learn

24 504

Not All Memories are Created Equal: Learning to Expire Github: https://github.com/lucidrains/learning-to-expire-pytorch Paper: https://openreview.net/forum?id=ZVBtN6B_6i7 @Machine_learn

24 504

How to Create Custom Real-time Plots in Deep Learning https://www.kdnuggets.com/2020/12/create-custom-real-time-plots-deep-learning.html @Machine_learn

24 504

180 Data Science and Machine Learning Projects with Python https://medium.com/coders-camp/180-data-science-and-machine-learning-projects-with-python-6191bc7b9db9 @Machine_learn

24 504

Generating Beautiful Neural Network Visualizations https://www.kdnuggets.com/2020/12/generating-beautiful-neural-network-visualizations.html @Machine_learn

24 504

💉 MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining Github: https://github.com/BruceWen120/medal Paper: https://arxiv.org/abs/2012.13978v1 Dataset: https://www.kaggle.com/xhlulu/medal-emnlp Pre-trained: https://huggingface.co/xhlu/electra-medal @Machine_learn

24 504

Feature Selection with Stochastic Optimization Algorithms https://machinelearningmastery.com/feature-selection-with-optimization/ @Machine_learning

24 504

Adversarial Examples in Deep Learning. https://blog.djsarkar.ai/adversarial-learning-attacks-1/ Github: https://github.com/dipanjanS/adversarial-learning-robustness @Machine_learn

24 504

Machine learning lecture 0 #slide @Raminmousa @Machine_learn

24 504

Rotated Binary Neural Network Github (Pytorch implementation): https://github.com/lmbxmu/RBNN Paper: https://arxiv.org/abs/2009.13055 @Machine_learn

24 504

Visualisation of the attention patterns of Vision Transformer link: https://epfml.github.io/attention-cnn/ @Machine_learn

24 504

🌐 Optimization Algorithms in Neural Networks https://datascience-enthusiast.com/DL/Optimization_methods.html Most used optimizers: https://www.theaidream.com/post/optimization-algorithms-in-neural-networks @Machine_learn

24 504

🤩 7 Awesome Open Source Machine Learning Project Repos👍 1. DeOldify - https://github.com/jantic/DeOldify 2. Real-Time Voice Cloning - https://github.com/CorentinJ/Real-Time-Voice-Cloning 3. Face Recognition - https://github.com/ageitgey/face_recognition 4. NeuralTalk2 - https://github.com/karpathy/neuraltalk2 5. U-GAT-IT - https://github.com/taki0112/UGATIT 6. Srez - https://github.com/david-gpu/srez 7. TecoGAN - https://github.com/thunil/TecoGAN @Machine_learn

24 504

MIT launched a New free Course on Machine learning : Click here @Machine_learn

24 504

📈 کارگاه Kernel Methods for Pattern Analysis 📅 تاریخ برگزاری: پنج شنبه ۲۷ آذر از ساعت ۱۳ الی ۱۶.۳۰ و جمعه ۲۸ آذر ۱۰ الی ۱۶.۳۰ 🖥 این دوره به صورت آنلاین برگزار خواهد شد. 🔖 ثبت نام زود هنگام این دوره را از دست ندهید! ⭕️ برای مشاهده جزئیات بیشتر و ثبت نام روی این لینک کلیک کنید. ❌ دوره دارای ظرفیت محدود می باشد. 🕙 مدت این دوره: 8 ساعت ➖➖➖➖➖➖➖➖ @LoopAcademy