This repository contains a collection of resources in the form of eBooks related to Data Science, Machine Learning, and similar topics.

📖 book

💠@Machine_learn

با عرض سلام مقاله زیر در مرحله ی اولیه ارسال می باشد. نفرات 2و ۳ خالی می باشد. دوستانی که نیاز دارند می تونن به ایدی بنده پیام بدن. همچنین امکان ریکام‌دادن بعد اتمام کار وجود داره.
💠💠
Title:
Automated Concrete Crack Detection and Geometry Measurement Using YOLOv8
Description:
This paper presents a comprehensive approach for automatic detection and quantification of concrete cracks using the YOLOv8 deep learning model. By leveraging advanced object detection capabilities, our system identifies concrete cracks in real-time with high accuracy, addressing challenges of complex backgrounds and varying crack patterns. Following crack detection, we employ image processing techniques to measure key geometric parameters such as width, length, and area. This integrated system enables rapid, precise analysis of structural integrity, offering a scalable solution for infrastructure monitoring and maintenance.

🔸Target Journal:
Nature, Scientific Reports

@Raminmousa
@Machine_learn
https://www.tg-me.com/+SP9l58Ta_zZmYmY0

📖 A Data-Centric Introduction to Computing



link

@Machine_learn

🖥 Awesome LLM Strawberry (OpenAI o1)



▪ Github

✅https://www.tg-me.com/deep_learning_proj

💠Title:BERTCaps: BERT Capsule for persian Multi-domain Sentiment Analysis.

🔺Abstract:
Sentiment classification is widely known as a domain-dependent problem. In order to learn an accurate domain-specific sentiment classifier, a large number of labeled samples are needed, which are expensive and time-consuming to annotate. Multi-domain sentiment analysis based on multi-task learning can leverage labeled samples in each single domain, which can alleviate the need for large amount of labeled data in all domains. In this article, the purpose is BERTCaps to provide a multi-domain classifier. In this model, BERT was used for Instance Representation and Capsule was used for instance learning. In the evaluation dataset, the model was able to achieve an accuracy of 0.9712 in polarity classification and an accuracy of 0.8509 in domain classification.

journal: https://www.sciencedirect.com/journal/array
If:2.3

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@Raminmousa
@Paper4money
@Machine_learn