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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
Mostrar más📈 Análisis del canal de Telegram Machine learning books and papers
El canal Machine learning books and papers (@machine_learn) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 24 499 suscriptores, ocupando la posición 8 036 en la categoría Educación y el puesto 13 785 en la región Irán.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 24 499 suscriptores.
Según los últimos datos del 01 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -127, y en las últimas 24 horas de -5, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 7.47%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.04% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 829 visualizaciones. En el primer día suele acumular 500 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 1.
- Intereses temáticos: El contenido se centra en temas clave como disorder, psy, مقاله, framework, graph.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Admin: @Raminmousa
ID: @Machine_learn
link: https://t.me/Machine_learn”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 02 julio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.
24 499
Suscriptores
-524 horas
-207 días
-12730 días
Archivo de publicaciones
Deep Learning
on Windows #deeplearning #2021 #book @Machine_learn
Beginning Robotics with Raspberry Pi and Arduino #2021 #book @Machine_learn
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
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
Project Guideline: Enabling Those with Low Vision to Run Independently
http://ai.googleblog.com/2021/05/project-guideline-enabling-those-with.html
@Machine_learn
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
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
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
Deep_Reinforcement_Learning_in_Action_Manning_Publications_2020.pdf14.94 MB
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
Google at ICLR 2021
http://ai.googleblog.com/2021/05/google-at-iclr-2021.html
@Machine_learn
Flexible, Scalable, Differentiable Simulation of Recommender Systems with RecSim NG
http://ai.googleblog.com/2021/04/flexible-scalable-differentiable.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
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
Simple multi-dataset detection
Github: https://github.com/xingyizhou/UniDet
Paper: https://arxiv.org/abs/2102.13086v1
@Machine_learn
