🔖 Learning Data Science through interactive examples

One of the most useful repositories for those who want to better understand machine learning.

It transforms complex concepts into visual experiments: you can study models, change parameters, and immediately see the results.

⛓ Link to GitHub
https://github.com/GeostatsGuy/DataScienceInteractivePython

@Machine_learn

هر هفته با یک موضوع تحقیقی

موضوع :تولید داده های سری زمانی با استفاده از شبکه های عصبی تخاصمی در شبکه های هوشمند
#Thesis #proposed_research

@Raminmousa1

@Machine_learn

با عرض سلام در مقاله زیر جهت سابمیت نیاز به نفر دوم داریم

Title: FFChurn: Fusion Former for Customer Churn Classification Based on Transformer, FEDformer, and Informer

Abstract: Customer churn prediction is a key issue in customer relationship management that directly impacts organizational profitability and has created challenges for researchers and organizations. Machine learning (ML), Ensemble Learning (EL), and Deep Learning (DL) models have achieved comparable results on this problem. In this study, Fusion Former was introduced, integrating the FEDformer, Informer, and Transformer architectures to simultaneously extract local features and long-term dependencies. The pipeline for this model includes denoising with a wavelet transform, Min-Max normalization, and hybrid adaptive feature selection based on mutual information (MI), recursive feature elimination (RFE), and the Boruta algorithm. Four different versions of the model, including binary and ternary object fusion, were evaluated on two datasets. The results showed that the Fusion Former (FED+INF+Transformer) model, with F1 scores of 0.9876 on the Dataset 1 and 0.8887 on the Dataset 2, outperformed classical machine learning models, multilayer neural networks, and other binary combinations. Also, the sensitivity analysis of hyperparameters, which included changes in the cost function, batch size, and dropout size, methods for dealing with data imbalance, which included Smote, TMG-GAN, Ib-gan, T-SMOTE approaches, and the effect of feature selection, which included four methods: MI, RFE, Boruta, and Adaptive FS (Boruta+MI+RFE), confirmed the superiority and relative stability of the proposed model.

Price:250$

@Raminmousa1

@Machine_learn

اخرین زمان سابمیت این مقاله امشب...!

@Raminmousa1

🔥 8 skills = 8 free certifications >>>

AI (Microsoft) -
https://learn.microsoft.com/en-us/training/paths/get-started-artificial-intelligence/

Deep learning (NVIDIA) -
https://learn.nvidia.com/en-us/training/self-paced-courses

Data science (IBM) -
https://skillsbuild.org/students/course-catalog/data-science

Data Analyst (Microsoft) -
https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/

Python (Microsoft) -
https://learn.microsoft.com/en-us/shows/intro-to-python-development/

SQL (Infosys) -
https://www.coursejoiner.com/freeonlinecourses/infosys-free-certification-course-9/

Java (Infosys) -
https://www.coursejoiner.com/uncategorized/infosys-launched-free-java-certification-course/

Cloud computing (AWS) -
https://explore.skillbuilder.aws/learn/course/134/aws-cloud-practitioner-essentials


@Machine_learn

"The Mathematics of Bitcoin" is a concise work that analyzes Bitcoin from a mathematical perspective. 📊

It utilizes probability theory, stochastic processes, martingales, combinatorics, and special functions to explore the mechanisms of the Bitcoin protocol. 🧮

In particular, the authors examine the probability of double-spending, the profitability of mining, block generation, miner strategies, and the resilience of the protocol. ⛏️

If you want to delve deeper, I also recommend "Bitcoin and Cryptocurrency Technologies" from Princeton University. This is a much broader introduction to cryptographic hash functions, digital signatures, consensus, Proof of Work, mining, transactions, anonymity, security, and the incentive system in cryptocurrencies. 🎓

The Mathematics of Bitcoin:
https://arxiv.org/pdf/2003.00001

Bitcoin and Cryptocurrency Technologies:
https://d28rh4a8wq0iu5.cloudfront.net/bitcointech/readings/princeton_bitcoin_book.pdf

@Machine_learn

🔖 ML algorithms in visualizations

A useful repository that helps you understand how machine learning algorithms work – through interactive diagrams and step-by-step explanations.

You can run it in your browser or locally using Docker.

⛓ Link to GitHub
https://github.com/gavinkhung/machine-learning-visualized

@Machine_learn

Matrix Calculus for Machine Learning and Beyond! — a free ebook from MIT.

This is the 2025 MIT textbook by Alan Edelman, Steven G. Johnson, and Paige Bright.

The book directly connects matrix calculus to modern machine learning.

Inside:

*   Derivatives of matrices and vectors
*   Jacobian and Hessian
*   Matrix decompositions
*   Optimization
*   Differentiation in reverse mode
*   Backpropagation of error
*   Automatic differentiation
*   Derivatives through ODEs
*   Problems focused on machine learning

This is a comprehensive mathematical bridge between linear algebra, calculus, optimization, backpropagation, and machine learning.

Free ebook:
https://geni.us/Matrix-Calculus-Book

@Machine_learn

🔖Computer Science Fundamentals from MIT

We found the textbook Mathematics for Computer Science – covering the mathematics that underlies algorithms and computer science.

Logic, graphs, combinatorics, probability, induction, recurrence relations, and discrete structures – all in one place.

⛓️ Link to the textbook
https://ocw.mit.edu/courses/6-042j-mathematics-for-computer-science-spring-2015/mit6_042js15_textbook.pdf

@Machine_learn

با عرض سلام برای یکی از مقالاتمون تحت عنون زیر نیازمند نفر دوم و سوم هستیم.
Price: 2 --> 200$
Price 3--> 150$
Title:Skin cancer diagnosis (scd) using efficientnet-wavelet and Optimization algortithms

@Raminmousa1

This repository contains Jupyter notebooks for the O'Reilly book "Transformers: The Definitive Guide."

It includes code for computer vision tasks, time series analysis, audio processing, and reinforcement learning.

https://github.com/Nicolepcx/transformers-the-definitive-guide

@Machine_learn

فقط نفر دوم از این مقاله باقی مونده...!

ReCamMaster: Camera-Controlled Generative Rendering from A Single Video

Source code: https://github.com/KlingAIResearch/ReCamMaster


@Machine_learn

❇️ MindDrive: A Vision-Language-Action Model for Autonomous Driving via Online Reinforcement Learning 🔥

Source code: https://github.com/xiaomi-mlab/Minddrive

@Machine_learn

High-Impact Research Paper – MedicalRec / GROKRec

Title: MedicalRec

Price: $1,000 USD

Looking for a ready-to-publish, novel research paper in medical AI and sustainable deep learning?

This paper introduces GROKRec, an innovative recommender framework that uses embedding vectors from the Grok language model combined with numerical features to recommend the best deep learning model for any medical image classification task — without the need to train dozens of models on the target dataset.

Key Highlights:
Addresses major real-world problems: high computational cost, energy consumption, carbon emissions, and e-waste caused by training large DL models.
Built on a newly curated public dataset MedicalRec-Bench II containing 3,500 research papers and over 6,000 model evaluation records across diverse medical imaging tasks.
Evaluated under four feature configurations (MedicalRec I) using 13 different models.
Achieves strong performance with HitRate@100 ranging from 72.43% to 77.08% — the highest among compared approaches.
Uses composite loss functions and regularization techniques for accurate recommendations.
Fully eliminates the trial-and-error process of training multiple models, significantly reducing carbon footprint.

This is a complete, self-contained research contribution with a novel dataset and a practical, environmentally conscious solution for the medical AI community.

Ideal for: Researchers, academic publishers, journals, or institutions looking for high-quality, ready-to-use work in medical image analysis, recommender systems, and green AI.

Price: $1,000 USD (one-time transfer of ownership/rights as agreed).

Interested? Contact me for the full manuscript, dataset details, or to discuss terms.
@Raminmousa1

Uniface

Automate face detection, recognition, and analysis of key facial landmarks with the Uniface Python library.

https://github.com/yakhyo/uniface

@Machine_learn

Understanding Attention
From Q, K, V to Modern Transformer Attention

https://drive.google.com/file/d/1fCHQ5xCQJ6jZszAYf-qP3VIySbzFIEDv/view

@Machine_learn