Machine learning books and papers

Welcome to Ollama's Prompt Engineering Interactive Tutorial 🔗 Github https://t.me/deep_learning_proj

Python for Everyone 🖥 book @Machine_learn

یک هفته تا سابمیت نهایی این مقاله باقی مونده...!

با عرض سلام نفر سوم براي مقاله زير رو خالي داريم. Title: Alzheimer’s disease (AD) classification using swin transformer wavelet and Improved Gray Wolf Optimization (IGWO) Abstract: Alzheimer’s disease (AD) is a slow neurological disorder that destroys the thought process, and consciousness, of a human. It directly affects the development of mental ability and neurocognitive functionality. The number of patients with Alzheimer’s disease is increasing day by day, especially in old aged people, who are above 60 years of age, and, gradually, it becomes cause of their death. In this research, our goal is to present ALzSwinTNet for Alzheimer’s classification based on FMRI images. The proposed approach uses wavelet fusion in the swin transformer network to extract features. The igwo and fox optimization approaches were used to find the hyperparameters of the model. ALzSwinTNet was able to achieve an accuracy of 0.98 in 4-class classification and 1 in 2-class classification. journal: https://www.sciencedirect.com/journal/expert-systems-with-applications if:7.5 هزینه مشارکت برای نفر سوم ۲۰ تومن می باشد. این هزینه صرف تسویه سرورها خواهد شد. @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

⭐️ Region-Aware Text-to-Image Generation via Hard Binding and Soft Refinement RAG-Diffusion now supports FLUX.1 Redux! 🔥 Ready to take control? Customize your region-based images with our training-free solution and achieve powerful, precise results! 🔗 Code: https://github.com/NJU-PCALab/RAG-Diffusion @Machine_learn

Super beginner-friendly book on Linear Algebra 🔗 Book @Machine_learn

LLM-based agents for Software Engineering "Large Language Model-Based Agents for Software Engineering: A Survey". https://github.com/FudanSELab/Agent4SE-Paper-List. https://t.me/deep_learning_proj

فقط جايگاه ٣ باقي مونده...!

با عرض سلام نفر سوم و چهارم براي مقاله زير رو خالي داريم. Title: Alzheimer’s disease (AD) classification using swin transformer wavelet and Improved Gray Wolf Optimization (IGWO) Abstract: Alzheimer’s disease (AD) is a slow neurological disorder that destroys the thought process, and consciousness, of a human. It directly affects the development of mental ability and neurocognitive functionality. The number of patients with Alzheimer’s disease is increasing day by day, especially in old aged people, who are above 60 years of age, and, gradually, it becomes cause of their death. In this research, our goal is to present ALzSwinTNet for Alzheimer’s classification based on FMRI images. The proposed approach uses wavelet fusion in the swin transformer network to extract features. The igwo and fox optimization approaches were used to find the hyperparameters of the model. ALzSwinTNet was able to achieve an accuracy of 0.98 in 4-class classification and 1 in 2-class classification. journal: https://www.sciencedirect.com/journal/expert-systems-with-applications if:7.5 هزینه نفرات به ترتیب ۲۰ و ۱۵ تومن می باشد. این هزینه صرف تسویه سرورها خواهد شد. @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

با عرض سلام نفر سوم براي مقاله زير رو خالي داريم. Title: Alzheimer’s disease (AD) classification using swin transformer wavelet and Improved Gray Wolf Optimization (IGWO) Abstract: Alzheimer’s disease (AD) is a slow neurological disorder that destroys the thought process, and consciousness, of a human. It directly affects the development of mental ability and neurocognitive functionality. The number of patients with Alzheimer’s disease is increasing day by day, especially in old aged people, who are above 60 years of age, and, gradually, it becomes cause of their death. In this research, our goal is to present ALzSwinTNet for Alzheimer’s classification based on FMRI images. The proposed approach uses wavelet fusion in the swin transformer network to extract features. The igwo and fox optimization approaches were used to find the hyperparameters of the model. ALzSwinTNet was able to achieve an accuracy of 0.98 in 4-class classification and 1 in 2-class classification. journal: https://www.sciencedirect.com/journal/expert-systems-with-applications if:7.5 @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

📚 Machine learning mastery 🔗 Github @Machine_learn

📚 Deep Learning with Python Develop Deep Learning Models on Theano and TensorFLow Using Keras by Jason Brownlee 🔗 Book @Machine_learn

📃Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects 📎 Study the paper @Machine_learn

با عرض سلام نفر سوم براي مقاله زير رو خالي داريم. Title: Alzheimer’s disease (AD) classification using swin transformer wavelet and Improved Gray Wolf Optimization (IGWO) Abstract: Alzheimer’s disease (AD) is a slow neurological disorder that destroys the thought process, and consciousness, of a human. It directly affects the development of mental ability and neurocognitive functionality. The number of patients with Alzheimer’s disease is increasing day by day, especially in old aged people, who are above 60 years of age, and, gradually, it becomes cause of their death. In this research, our goal is to present ALzSwinTNet for Alzheimer’s classification based on FMRI images. The proposed approach uses wavelet fusion in the swin transformer network to extract features. The igwo and fox optimization approaches were used to find the hyperparameters of the model. ALzSwinTNet was able to achieve an accuracy of 0.98 in 4-class classification and 1 in 2-class classification. journal: https://www.sciencedirect.com/journal/expert-systems-with-applications if:7.5 @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

Gaussian Processes for Machine Learning 📚 link @Machine_learn

fmri alzheimer's disease classification target journal:https://www.sciencedirect.com/journal/computerized-medical-imaging-and-graphics نفر ٣ رو كم داريم. نيازمند كسي هستيم كه بتونه هزينه سرور رو پرداخت كنه . @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

⚡️ Biggest open text dataset release of the year: SmolTalk is a 1M sample big synthetic dataset that was used to train SmolLM v2. TL;DR; 🧩 New datasets: Smol-Magpie-Ultra (400K) for instruction tuning; Smol-contraints (36K) for precise output; Smol-rewrite (50K) & Smol-summarize (100K) for rewriting and summarization. 🤝 Public Dataset Integrations: OpenHermes2.5 (100K), MetaMathQA & NuminaMath-CoT, Self-Oss-Starcoder2-Instruct, LongAlign & SystemChats2.0 🥇 Outperforms the new Orca-AgenInstruct 1M when trained with 1.7B and 7B models 🏆 Outperform models trained on OpenHermes and Magpie Pro on IFEval and MT-Bench distilabel to generate all new synthetic datasets 🤗 Released under Apache 2.0 on huggingface Apache 2.0 Synthetic generation pipelines and training code released. Dataset: https://huggingface.co/datasets/HuggingFaceTB/smoltalk Generation Code: https://github.com/huggingface/smollm Training Code: https://github.com/huggingface/alignment-handbook/tree/main/recipes/smollm2 @Machine_learn

📖 General Relativity 📌 Book @Machine_learn

Title: Transformer and XGBoost for time-series forecasting of Bitcoin prices using high-dimensional features ABSTRACT: Bitcoin price prediction based on price indicators has become a hot field of study. In this article, Bitcoin price prediction is discussed based on hash rate features. For this purpose, a series of price indices were used in the beginning and the selection of features was done among 20 features. On the other hand, the selection of features was also done on the raw data of eight rates. This research used forecasting for one, seven, thirty and ninety days. In the classification based on raw features, the highest accuracy is 81%, and for a 90-day interval, on the other hand, the lowest RMSE value is 1.85, which is for a one-day interval. In the classification based on the features extracted from the indicators, the highest accuracy is 73% for the 90-day interval and the lowest RMSE is 1.58 for the 1-day interval. نياز به co-author براي اين مقاله هستيم شرايط رو اگر كسي از دوستان داشت به بنده مراجعه كنن. @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

ShowUI is a lightweight vision-language-action model for GUI agents. 🖥 Github: https://github.com/showlab/showui 📕 Paper: https://arxiv.org/abs/2411.17465v1 🌟 Dataset: https://huggingface.co/datasets/showlab/ShowUI-desktop-8K @Machine_learn