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 24 502 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

1 829

Post views

~ 50024 hours

~ 61348 hours

7.47%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

24 502

Deep Learning on Windows #deeplearning #2021 #book @Machine_learn

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Beginning Robotics with Raspberry Pi and Arduino #2021 #book @Machine_learn

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Topic : Sudoku solver (SolSudo) Abstract : SolSudo is a Sudoku solver made using Deep Learning. SolSudo can solve sudokus using images. This has an intelligent solution method. According to this method, the model predicts the blank digits, and when each level is completed, the filled blanks are placed one after another. Each time a digit is filled, new sudoku will be fed to the solver to determine the next digit. Again and again, until there is no blank left. One of the features of this project is detecting sudoku from an image and filling in the blanks. This requires tesseract-ocr, however, which may cause problems. Therefore, I devised a method, in which the Sudoku numbers are entered one by one, and 0 is used for the empty spaces. Below is an example of Sudoku, its detection, and its solution. Github Link : https://github.com/AryaKoureshi/SolSudo Linkedin Link : https://www.linkedin.com/posts/arya-koureshi_deeplearning-python-tensorflow-activity-6711641409658716160-kdSD @Machine_learn

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https://blog.tensorflow.org/2021/05/recap-of-tensorflow-at-google-io-2021.html @Machine_learn

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Neural-network-from-scratch Github: https://github.com/Joaomlg/neural-network-from-scratch Code: https://github.com/Joaomlg/neural-network-from-scratch/archive/refs/heads/main.zip @Machine_learn

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Project Guideline: Enabling Those with Low Vision to Run Independently http://ai.googleblog.com/2021/05/project-guideline-enabling-those-with.html @Machine_learn

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500 + 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗟𝗶𝘀𝘁 𝘄𝗶𝘁𝗵 𝗰𝗼𝗱𝗲 https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code @Machine_learn

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ALIGN: Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision http://ai.googleblog.com/2021/05/align-scaling-up-visual-and-vision.html @Machine_learn

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A Survey of Data Augmentation Approaches for NLP Data Augmentation has becoming more and more popular and important task in NLP. On the contrary to Computer Vision where all methods now are well-known and already pre-implemented in libraries, in NLP the situation is not so consistent. So, there has been published a nice paper that accumulated all known due today techniques, models and applications of data augmentation in texts: https://arxiv.org/abs/2105.03075 In the appendix you can find the list of open-source that may be useful for your task. @Machine_learn

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@Machine_learn

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@Machine_learn

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Deep_Reinforcement_Learning_in_Action_Manning_Publications_2020.pdf14.94 MB

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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 ٥٠٠هزار مي باشد و صرفا هزينه تهيه ديتاست هاست. جهت خريد مي توانيد با ايدي بنده در ارتباط باشيد @Raminmousa

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tf.keras.Model https://www.tensorflow.org/api_docs/python/tf/keras/Model Code: https://github.com/tensorflow/tensorflow/blob/v2.4.1/tensorflow/python/keras/engine/training.py#L138-L2675 @Machine_learn

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Google at ICLR 2021 http://ai.googleblog.com/2021/05/google-at-iclr-2021.html @Machine_learn

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How Machine Learning is Changing e-Government @Machine_learn

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Flexible, Scalable, Differentiable Simulation of Recommender Systems with RecSim NG http://ai.googleblog.com/2021/04/flexible-scalable-differentiable.html @Machine_learn

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📌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

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Monster Mash: A Sketch-Based Tool for Casual 3D Modeling and Animation http://ai.googleblog.com/2021/04/monster-mash-sketch-based-tool-for.html @Machine_learn

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Simple multi-dataset detection Github: https://github.com/xingyizhou/UniDet Paper: https://arxiv.org/abs/2102.13086v1 @Machine_learn