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Machine learning books and papers
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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
Show more📈 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 24 502 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 24 502 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.
24 502
Subscribers
+524 hours
-147 days
-10930 days
Posts Archive
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
👉Lecture Notes for Linear Algebra Featuring Python
.
GitHub link : https://github.com/MacroAnalyst/Linear_Algebra_With_Python
@Machine_learn
TensorFlow Lite on GPU
https://www.tensorflow.org/lite/performance/gpu_advanced
@Machine_learn
🔇 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
🚀 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
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
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
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
Generating Beautiful Neural Network Visualizations
https://www.kdnuggets.com/2020/12/generating-beautiful-neural-network-visualizations.html
@Machine_learn
💉 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
Feature Selection with Stochastic Optimization Algorithms
https://machinelearningmastery.com/feature-selection-with-optimization/
@Machine_learning
Adversarial Examples in Deep Learning.
https://blog.djsarkar.ai/adversarial-learning-attacks-1/
Github: https://github.com/dipanjanS/adversarial-learning-robustness
@Machine_learn
Rotated Binary Neural Network
Github (Pytorch implementation): https://github.com/lmbxmu/RBNN
Paper: https://arxiv.org/abs/2009.13055
@Machine_learn
Visualisation of the attention patterns of Vision Transformer
link: https://epfml.github.io/attention-cnn/
@Machine_learn
🌐 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
🤩 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
MIT launched a New free Course on Machine learning : Click here
@Machine_learn
📈 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
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