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 24 505 subscribers, ranking 8 033 in the Education category and 13 749 in the Iran region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 505 subscribers.

According to the latest data from 03 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -99 over the last 30 days and by 2 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 6.54%. Within the first 24 hours after publication, content typically collects 2.24% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 603 views. Within the first day, a publication typically gains 549 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 04 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.

24 505

Subscribers

+224 hours

-107 days

-9930 days

1 603

Post views

~ 54924 hours

~ 67448 hours

6.54%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

24 505

#Deep Set Prediction Networks #paper #DL @Machine_learn

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#Deep Set Prediction Networks #paper #DL @Machine_learn

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#Deep Set Prediction Networks #paper #DL @Machine_learn

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discriminative : 1:#Regression 2:#Logistic regression 3:#decision tree(Hunt) 4:#neural network(traditional network, deep network) 5:#Support Vector Machine(SVM) Generative: 1:#Hidden Markov model 2:#Naive bayes 3:#K-nearest neighbor(KNN) 4:#Generative adversarial networks(GANs) Deep learning: 1:CNN R_CNN Fast-RCNN Mask-RCNN 2:RNN 3:LSTM 4:CapsuleNet 5:Siamese: siamese cnn siamese lstm siamese bi-lstm siamese CapsuleNet 6:time series data SVR DT(cart) Random Forest linear Bagging Boosting جهت درخواست و راهنمایی در رابطه با پیاده سازی مقالات و پایان نامه ها در رابطه با مباحث deep learning و machine learning با ایدی زیر در ارتباط باشید @Raminmousa

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@Machine_learn Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift How normalization applied to layers helps to reach faster convergence. ArXiV: https://arxiv.org/abs/1502.03167 #NeuralNetwork #nn #normalization #DL

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@Machine_learn The largest publicly available language model: CTRL has 1.6B parameters and can be guided by control codes for style, content, and task-specific behavior. code: https://github.com/salesforce/ctrl article: https://einstein.ai/presentations/ctrl.pdf C-write:ai_machinelearning_big_data https://blog.einstein.ai/introducing-a-conditional-transformer-language-model-for-controllable-generation/

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Deep Learning with Python The ultimate beginners guide to Learn Deep Learning with Python Step by Step #book #DL #python @Machine_learn

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Deep Learning with Python The ultimate beginners guide to Learn Deep Learning with Python Step by Step #book #DL #python @Machine_learn

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@Machine_learn DeepMind's OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. code: https://github.com/deepmind/open_spiel article: https://arxiv.org/abs/1908.09453

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@Machine_learn 🚀 Introducing TF-GAN: A lightweight GAN library for TensorFlow 2.0 Tensorflow blog: https://medium.com/tensorflow/introducing-tf-gan-a-lightweight-gan-library-for-tensorflow-2-0-36d767e1abae Code: https://github.com/tensorflow/gan Free course: https://developers.google.com/machine-learning/gan/ Paper: https://arxiv.org/abs/1805.08318

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@Machine_learn Memory-Efficient Adaptive Optimization Source: https://arxiv.org/abs/1901.11150 Code: https://github.com/google-research/google-research/tree/master/sm3

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@Machine_leaen ai ,machine learning #code #datasets #paper • 1146 leaderboards • 1223 tasks • 1105 datasets • 14779 papers with code https://paperswithcode.com/sota

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@Machine_learn Rank-consistent Ordinal Regression for Neural Networks Article: https://arxiv.org/abs/1901.07884 PyTorch: https://github.com/Raschka-research-group/coral-cnn

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Unsupervised learning with python,2019 #book @Machine_learn

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Learning_Tenserflow_building_deep #book @Machine_learn

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@Machine_learn The HSIC Bottleneck: Deep Learning without Back-Propagation🥺 An alternative to conventional backpropagation, that has a number of distinct advantages. Link: https://arxiv.org/abs/1908.01580 #backpropagation #DL

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#deeplearning ⬇⬇⬇ @Machine_learn

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@Machine_learn How to Implement Progressive Growing GAN Models in Keras https://machinelearningmastery.com/how-to-implement-progressive-growing-gan-models-in-keras/

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@Machine_learn Interpreting Latent Space of GANs for Semantic Face Editing https://shenyujun.github.io/InterFaceGAN/ code: https://github.com/ShenYujun/InterFaceGAN.git