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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 8 036 in the Education category and 13 785 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 01 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -127 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 7.47%. 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 1 829 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 02 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
-207 days
-12730 days
Posts Archive
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Therapeutics
Github: https://github.com/mims-harvard/TDC
Paper: https://arxiv.org/abs/2102.09548
Datasets: https://tdcommons.ai/
@Machine_learn
Python for data science cheatsheet
#python #chearsheet
@Machine_learn
با عرض سلام ما پكيج ٣٦ پروژه عملي با يادگيري عميق همراه با داكيومنت فارسي را براي دوستاني كه مي خواهند در اين حوزه به صورت عملي كار كنند تهيه كرديم سرفصل هاي اين پكيج به ترتيب زير مي باشند:
1-Deep Learning Basic
-01_Introduction
--01_How_TensorFlow_Works
--02_Creating_and_Using_Tensors
--03_Implementing_Activation_Functions
-02_TensorFlow_Way
--01_Operations_as_a_Computational_Graph
--02_Implementing_Loss_Functions
--03_Implementing_Back_Propagation
--04_Working_with_Batch_and_Stochastic_Training
--05_Evaluating_Models
-03_Linear_Regression
--linear regression
--Logistic Regression
-04_Neural_Networks
--01_Introduction
--02_Single_Hidden_Layer_Network
--03_Using_Multiple_Layers
-05_Convolutional_Neural_Networks
--Convolution Neural Networks
--Convolutional Neural Networks Tensorflow
--TFRecord For Deep learning Models
-06_Recurrent_Neural_Networks
--Recurrent Neural Networks (RNN)
2-Classification apparel
-Classification apparel double capsule
-Classification apparel double cnn
3-ALZHEIMERS USING CNN(ResNet)
4-Fake News (Covid-19 dataset)
-Multi-channel
-3DCNN model
-Base line+ Char CNN
-Fake News Covid CapsuleNet
5-3DCNN Fake News
6-recommender systems
-GRU+LSTM MovieLens
7-Multi-Domain Sentiment Analysis
-Dranziera CapsuleNet
-Dranziera CNN Multi-channel
-Dranziera LSTM
8-Persian Multi-Domain SA
-Bi-GRU Capsule Net
-Multi-CNN
9-Recommendation system
-Factorization Recommender, Ranking Factorization Recommender, Item Similarity Recommender (turicreate)
-SVD, SVD++, NMF, Slope One, k-NN, Centered k-NN, k-NN Baseline, Co-Clustering(surprise)
10-NihX-Ray
-optimized CNN on FullDataset Nih-Xray
-MobileNet
-Transfer learning
-Capsule Network on FullDataset Nih-Xray
هزينه اين پكيج ٥٠٠هزارمیباشد(صرفا هزينه تهيه ديتاست هاست).
جهت خريد مي توانيد با ايدي بنده در ارتباط باشيد
@Raminmousa
Introducing Model Search: An Open Source Platform for Finding Optimal ML Models
http://ai.googleblog.com/2021/02/introducing-model-search-open-source.html
@Machine_learn
GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software
Github: https://github.com/EdisonLeeeee/GraphGallery
Paper: https://arxiv.org/abs/2102.07933v1
@Machine_learn
🔸لیستی از برترین کانالهای آموزشی در زمینه های هوشمصنوعی, پایتون و یادگیری ماشین
❯ هوش مصنوعی:
1️⃣ @Ai_Tv
2⃣ @AI_PYTHON
3⃣ @cvision
4⃣ @HomeAI
❯ یادگیری ماشین و یادگیری عمیق :
1️⃣ @Machine_learn
❯ علم داده:
1⃣ @dataanalysis
2⃣ @python4finance
3⃣ @mr_ie
❯ آموزش پایتون :
1⃣ @pythony
2⃣ @pythonchallenge
3⃣ @SQL_Server
4⃣ @Koolac_Org
5⃣ @Raspberry_Python
6⃣ @Programming4all_0to100
TI-Capsule: Capsule Network for Stock Exchange Prediction
#Paper
@Raminmousa
@Machine_learn
🧪 Alchemy: A structured task distribution for meta-reinforcement learning
Deepmind: https://deepmind.com/research/publications/alchemy
Github: https://github.com/deepmind/dm_alchemy
Paper: https://arxiv.org/abs/2102.02926
@Machine_learn
How to Speed up Scikit-Learn Model Training
https://www.kdnuggets.com/2021/02/speed-up-scikit-learn-model-training.html
@Machine_learn
📌کانالی مناسب برای علاقه مندان هوش مصنوعی، یادگیری ماشین، زبان برنامه نویسی پایتون و آموزش های رایگان
📝 این کانال توسط فارغ التحصیلان هوش مصنوعی دانشگاه صنعتی امیرکبیر ایجاد شده و جدیدترین اخبار حوزه هوش مصنوعی را اطلاع رسانی خواهد کرد.
https://t.me/joinchat/AAAAADweGusEx9ZAwC-N0g
📍متخصصین و اساتید زیادی در این کانال عضو هستند.
📖 مجله هوش مصنوعی
➖➖➖➖➖
🆔 : @HomeAI
Neural-Backed Decision Trees
Demo: https://research.alvinwan.com/neural-backed-decision-trees/
Github: https://github.com/alvinwan/neural-backed-decision-trees
Paper: https://arxiv.org/abs/2004.00221
Code: https://colab.research.google.com/github/alvinwan/neural-backed-decision-trees/blob/master/examples/load_pretrained_nbdts.ipynb
Dataset: https://pytorch.org/docs/stable/torchvision/datasets.html
@Machine_learn
Building Image Segmentation Faster Using Jupyter Notebooks from NGC
https://developer.nvidia.com/blog/building-image-segmentation-faster-using-jupyter-notebooks-from-ngc/
@Machine_learn
ML Algorithms Cheatsheet (python and R)
#code #python #R
@Machine_learn
Super VIP Cheatsheet: Machine Learning
Afshine Amidi and Shervine Amidi
#ML
@Machine_learn
Quantum Computing and Blockchain in Business (2020)
#book #2020 #Blockchain
@Machine_learn
Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning
http://ai.googleblog.com/2021/02/evaluating-design-trade-offs-in-visual.html
@Machine_learn
TracIn — A Simple Method to Estimate Training Data Influence
http://ai.googleblog.com/2021/02/tracin-simple-method-to-estimate.html
@Machine_learn
