📖 General Relativity

📌 Book

@Machine_learn

⚡️ 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

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://www.tg-me.com/+SP9l58Ta_zZmYmY0

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

🔗 Book


@Machine_learn

📚 Machine learning mastery

🔗 Github


@Machine_learn

فقط جايگاه ٣ باقي مونده...!

LLM-based agents for Software Engineering
"Large Language Model-Based Agents for Software Engineering: A Survey".

https://github.com/FudanSELab/Agent4SE-Paper-List.

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

Super beginner-friendly book on Linear Algebra

🔗 Book

@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

هزینه مشارکت برای نفر سوم ۲۰ تومن می باشد. این هزینه صرف تسویه سرورها خواهد شد.

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

Welcome to Ollama's Prompt Engineering Interactive Tutorial

🔗 Github

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