Machine learning books and papers @machine_learn Channel on Telegram

Machine learning books and papers

@machine_learn

Admin: @Raminmousa
Watsapp: +989333900804
ID: @Machine_learn
link: https://t.me/Machine_learn

Machine learning books and papers (English)

Are you a fan of machine learning and looking to expand your knowledge in the field? Look no further than the Telegram channel 'Machine learning books and papers' curated by the knowledgeable admin @Raminmousa. This channel is dedicated to providing its members with a vast collection of machine learning books and research papers to help them stay up-to-date with the latest trends and developments in the industry. Whether you are a beginner looking to learn the basics or an experienced professional seeking advanced resources, this channel has something for everyone. By joining 'Machine learning books and papers', you will have access to a diverse range of resources that will enhance your understanding of machine learning algorithms, techniques, and applications. Stay informed, inspired, and ahead of the curve by joining this valuable community today. For more information and to join the channel, visit the following link: https://t.me/Machine_learn

Machine learning books and papers

23 Jan, 14:53

با عرض سلام پروژه جدیدمون شروع شد.
هدف اصلی این پروژه اموزش یک مدل پیشنهاد دهنده ی مدل برای مسائله طبقه بندی تصاویر پزشکی میباشد که از اموزش مجدد مدل ها جلوگیری میکند. این مسائله با جنبه جلوگیری از مصرف انرژی اموزشی و زمان اموزش مدل ها ارائه می شود. برای این منظور ۵۰۰۰ مقاله در این زمینه جمع اوری شده است. جزئیات بیشتر در لینک گیت قرار دارد.

Project Title: MedRec: Medical recommender system for image classification without retraining

Github: https://github.com/Ramin1Mousa/MedicalRec

Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence

Impact factor: 20.8

۷ نفر دیگر امکان اضافه شدن به این پروژه رو دارند. هر شخص نیاز هست که حدودا داده های ۴۰۰ مقاله رو بررسی کند. زمان تقریبی هر مقاله ۵-۱۰ دقیقه می باشد. هزینه مشارکت در مقاله:

🔹 2- 600$
🔺 3- 500$
💠 4- 400$
🔺 5- 300$
🔹 6- 200$
🔸 7- 200$
جهت مشارکت می تونید به ایدی بنده پیام بدین.

🔹شنبه شروع این پروژه هست🔹

@Raminmousa
203
Machine learning books and papers

23 Jan, 14:51

📚 Mathematics of Machine Learning
👨🏻‍🏫 Philipp Christian Petersen

📝 Table of Contents:
● Language of Machine Learning
● ML Mathematical Framework
● Rademacher Complexities
● Rademacher Complexities Applications
●The Mysterious Machine
● Lower Bounds on Learning
● Model Selection
● Regression and Regularization
● Freezing Fritz
● Support Vector Machines
● Kernel Methods
● Nearest Neighbour
● Neural Networks
● Boosting
● Clustering
● Dimensionality Reduction

@Machine_learn
191
Machine learning books and papers

23 Jan, 13:52

🔎 Depth Anything
git clone https://github.com/DepthAnything/Video-Depth-Anything
cd Video-Depth-Anything
pip install -r requirements.txt

▪GitHub
▪Paper
▪Model Small
▪Model Large
▪Demo

@Machine_learn
308
Machine learning books and papers

23 Jan, 13:51

🧑‍🍳 New Cookbook guide: How to use the Usage API and Cost API to monitor your OpenAI usage

📚 Book

@Machine_learn
298
Machine learning books and papers

23 Jan, 13:47

Transformers

@Machine_learn
297
Machine learning books and papers

23 Jan, 09:24

Transformers 2: Self-adaptive LLMs

Paper: https://arxiv.org/pdf/2501.06252v2.pdf

Code:
https://github.com/SakanaAI/self-adaptive-llms
https://github.com/codelion/adaptive-classifier

Datasets: GSM8K - HumanEval - MATH
MBPP - TextVQA - OK-VQA - ARC (AI2 Reasoning Challenge)

@Machine_learn
512
Machine learning books and papers

23 Jan, 03:15

🚀rStar-Math от Microsoft .

GitHub

@Machine_learn
653
Machine learning books and papers

22 Jan, 07:02

دوستاني كه نياز به همكاري در يه مقاله خوب دارند اين مقاله جايگاه ٤ و ٥ باقي مونده...!

@Raminmousa
883
Machine learning books and papers

22 Jan, 07:00

MiniCPM-V: A GPT-4V Level MLLM on Your Phone

The recent surge of Multimodal Large Language Models (MLLMs) has fundamentally reshaped the landscape of #AI research and industry, shedding light on a promising path toward the next AI milestone. However, significant challenges remain preventing MLLMs from being practical in real-world applications. The most notable challenge comes from the huge cost of running an MLLM with a massive number of parameters and extensive computation. As a result, most MLLMs need to be deployed on high-performing cloud servers, which greatly limits their application scopes such as mobile, offline, energy-sensitive, and privacy-protective scenarios. In this work, we present MiniCPM-V, a series of efficient #MLLMs deployable on end-side devices. By integrating the latest MLLM techniques in architecture, pretraining and alignment, the latest MiniCPM-Llama3-V 2.5 has several notable features: (1) Strong performance, outperforming GPT-4V-1106, Gemini Pro and Claude 3 on OpenCompass, a comprehensive evaluation over 11 popular benchmarks, (2) strong #OCR capability and 1.8M pixel high-resolution #image perception at any aspect ratio, (3) trustworthy behavior with low hallucination rates, (4) multilingual support for 30+ languages, and (5) efficient deployment on mobile phones. More importantly, MiniCPM-V can be viewed as a representative example of a promising trend: The model sizes for achieving usable (e.g., GPT-4V) level performance are rapidly decreasing, along with the fast growth of end-side computation capacity. This jointly shows that GPT-4V level MLLMs deployed on end devices are becoming increasingly possible, unlocking a wider spectrum of real-world AI applications in the near future.

Paper: https://arxiv.org/pdf/2408.01800v1.pdf

Codes:
https://github.com/OpenBMB/MiniCPM-o
https://github.com/openbmb/minicpm-v

Datasets: Video-MME

@Machine_learn
852
Machine learning books and papers

22 Jan, 03:08

Title: Breast Cancer Ultrasound Image Segmentation Using Improved 3DUnet++
🔹🔹🔹🔹🔹🔹🔹🔹
Author: @Raminmousa
🔹🔹🔹🔹🔹🔹🔹🔹
Cite: https://doi.org/10.1016/j.wfumbo.2024.100068

ABSTRACT: Breast cancer is the most common cancer and the main cause of cancer-related deaths in women around the world. Early detection reduces the number of deaths. Automated breast ultrasound (ABUS) is a new and promising screening method for examining the entire breast. Volumetric ABUS examination is time-consuming, and lesions may be missed during the examination. Therefore, computer-aided cancer diagnosis in ABUS volume is highly expected to help the physician for breast cancer screening. In this research, we presented 3D structures based on UNet, ResUNet, and UNet++ for the automatic detection of cancer in ABUS volume to speed up examination while providing high detection sensitivity with low false positives (FPs). The three investigated approaches were evaluated on equal datasets in terms of training and testing as well as with proportional hyperparameters. Among the proposed approaches in classification and segmentation problems, the UNet++ approach was able to achieve more acceptable results. The UNet++ approach on the dataset of the Tumor Segmentation, Classification, and Detection Challenge on Automated 3D Breast Ultrasound 2023 (Named TSCD-ABUS2023) was able to achieve Accuracy=0.9911 and AUROC=0.9761 in classification and Dice=0.4930 in segmentation.

#Accepted ✅

@Machine_learn
860
Machine learning books and papers

21 Jan, 20:29

📄 A comprehensive bibliometric analysis on social network anonymization: current approaches and future directions


📎 Study the paper

@Machine_learn
873
Machine learning books and papers

21 Jan, 15:51

نفرات ٤ و ٥ از اين پروژه باقي موندن دوستاني كه حاضر به همكاري هستن به ايدي بنده مراجعه كنند.

@Raminmousa
959
Machine learning books and papers

21 Jan, 15:11

Deep Learning for Coders with fastai and PyTorch

@Machine_learn
988
Machine learning books and papers

21 Jan, 15:09

WIS Python programming course started in 2024.04

📖 Github

@Machine_learn
975
Machine learning books and papers

21 Jan, 03:08

DeepSeek-V3 Technical Report

We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in #DeepSeek V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models. Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training. In addition, its training process is remarkably stable. Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks. The model checkpoints are available at https://github.com/deepseek-ai/DeepSeek-V3.

Paper: https://arxiv.org/pdf/2412.19437v1.pdf

Code: https://github.com/deepseek-ai/deepseek-v3

@Machine_learn
1,102
Machine learning books and papers

20 Jan, 18:33

Machine learning books and papers pinned «با عرض سلام پروژه جدیدمون شروع شد. هدف اصلی این پروژه اموزش یک مدل پیشنهاد دهنده ی مدل برای مسائله طبقه بندی تصاویر پزشکی میباشد که از اموزش مجدد مدل ها جلوگیری میکند. این مسائله با جنبه جلوگیری از مصرف انرژی اموزشی و زمان اموزش مدل ها ارائه می شود. برای…»
0
Machine learning books and papers

20 Jan, 18:33

با عرض سلام پروژه جدیدمون شروع شد.
هدف اصلی این پروژه اموزش یک مدل پیشنهاد دهنده ی مدل برای مسائله طبقه بندی تصاویر پزشکی میباشد که از اموزش مجدد مدل ها جلوگیری میکند. این مسائله با جنبه جلوگیری از مصرف انرژی اموزشی و زمان اموزش مدل ها ارائه می شود. برای این منظور ۵۰۰۰ مقاله در این زمینه جمع اوری شده است. جزئیات بیشتر در لینک گیت قرار دارد.

Project Title: MedRec: Medical recommender system for image classification without retraining

Github: https://github.com/Ramin1Mousa/MedicalRec

Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence

Impact factor: 20.8

۷ نفر دیگر امکان اضافه شدن به این پروژه رو دارند. هر شخص نیاز هست که حدودا داده های ۴۰۰ مقاله رو بررسی کند. زمان تقریبی هر مقاله ۵-۱۰ دقیقه می باشد. هزینه مشارکت در مقاله:

🔹 2- 600$❌
🔺 3- 500$❌
💠 4- 400$✅
🔺 5- 300$✅
🔹 6- 200$❌
🔸 7- 200$❌
جهت مشارکت می تونید به ایدی بنده پیام بدین.
تنها نفرات ۴ و ۵ باقی مانده....!

@Raminmousa
1,198
Machine learning books and papers

20 Jan, 18:16

📽 Accelerating Drug Discovery With a Biomedical Knowledge Graph

🎞 Watch

@Machine_learn
929
Machine learning books and papers

20 Jan, 12:04

Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap

Large language models (LLMs) have not only revolutionized natural language processing but also extended their prowess to various domains, marking a significant stride towards artificial general intelligence. The interplay between LLMs and evolutionary algorithms (EAs), despite differing in objectives and methodologies, share a common pursuit of applicability in complex problems. Meanwhile, EA can provide an optimization framework for LLM's further enhancement under black-box settings, empowering LLM with flexible global search capacities. On the other hand, the abundant domain knowledge inherent in LLMs could enable EA to conduct more intelligent searches. Furthermore, the text processing and generative capabilities of LLMs would aid in deploying EAs across a wide range of tasks. Based on these complementary advantages, this paper provides a thorough review and a forward-looking roadmap, categorizing the reciprocal inspiration into two main avenues: LLM-enhanced EA and EA-enhanced #LLM. Some integrated synergy methods are further introduced to exemplify the complementarity between LLMs and EAs in diverse scenarios, including code generation, software engineering, neural architecture search, and various generation tasks. As the first comprehensive review focused on the EA research in the era of #LLMs, this paper provides a foundational stepping stone for understanding the collaborative potential of LLMs and EAs. The identified challenges and future directions offer guidance for researchers and practitioners to unlock the full potential of this innovative collaboration in propelling advancements in optimization and artificial intelligence.

Paper: https://arxiv.org/pdf/2401.10034v3.pdf

Code: https://github.com/wuxingyu-ai/llm4ec

https://t.me/deep_learning_proj
1,084
Machine learning books and papers

20 Jan, 09:43

Machine learning books and papers pinned «تنها دو روز تا شروع اين پروژه باقي مونده دوستاني كه مايل به همكاري هستن به ايدي بنده پيام بدن @Raminmousa»
0
Machine learning books and papers

20 Jan, 09:43

تنها دو روز تا شروع اين پروژه باقي مونده دوستاني كه مايل به همكاري هستن به ايدي بنده پيام بدن
@Raminmousa
804
Machine learning books and papers

19 Jan, 20:17

Foundations of Large Language Models

📝 Table of Contents:
● Pre-training
● Generative Models
● Prompting
● Alignment

Tong Xiao and Jingbo Zhu
January 17, 2025

📃 Download from arXiv.

@Machine_learn
1,106
Machine learning books and papers

19 Jan, 17:56

با عرض سلام پروژه جدیدمون شروع شد.
هدف اصلی این پروژه اموزش یک مدل پیشنهاد دهنده ی مدل برای مسائله طبقه بندی تصاویر پزشکی میباشد که از اموزش مجدد مدل ها جلوگیری میکند. این مسائله با جنبه جلوگیری از مصرف انرژی اموزشی و زمان اموزش مدل ها ارائه می شود. برای این منظور ۵۰۰۰ مقاله در این زمینه جمع اوری شده است. جزئیات بیشتر در لینک گیت قرار دارد.

Project Title: MedRec: Medical recommender system for image classification without retraining

Github: https://github.com/Ramin1Mousa/MedicalRec

Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence

Impact factor: 20.8

۷ نفر دیگر امکان اضافه شدن به این پروژه رو دارند. هر شخص نیاز هست که حدودا داده های ۴۰۰ مقاله رو بررسی کند. زمان تقریبی هر مقاله ۵-۱۰ دقیقه می باشد. هزینه مشارکت در مقاله:

🔹 2- 600$❌
🔺 3- 500$❌
💠 4- 400$✅
🔺 5- 300$✅
🔹 6- 200$❌
🔸 7- 200$❌
جهت مشارکت می تونید به ایدی بنده پیام بدین.
تنها نفرات ۴ و ۵ باقی مانده....!

@Raminmousa
1,001
Machine learning books and papers

19 Jan, 12:30

Mathematics of Backpropagation Through Time.

📕 Paper


@Machine_learn
1,087
Machine learning books and papers

19 Jan, 12:02

Top 10 Machine Learning Algorithms

@Machine_learn
1,148
Machine learning books and papers

19 Jan, 08:35

از این پروژه نفرات ۲ ، ۳، ۶ و ۷ رزرو شدن...!🔸🔹🔺
1,161
Machine learning books and papers

19 Jan, 06:20

LatentSync: Audio Conditioned Latent Diffusion Models for Lip Sync

Paper: https://arxiv.org/pdf/2412.09262v1.pdf

Code: https://github.com/bytedance/LatentSync

@Machine_learn
1,257
Machine learning books and papers

18 Jan, 17:15

Continual Forgetting for Pre-trained Vision Models (CVPR2024)

🖥 Github: https://github.com/bjzhb666/GS-LoRA

📕 Paper: https://arxiv.org/abs/2501.09705v1

🧠 Dataset: https://paperswithcode.com/dataset/coco

@Machine_learn
1,217
Machine learning books and papers

18 Jan, 11:51

دوستان این مقاله هم If اش بالاست و هم یک کار بنیادیه. جزئیات کار داخل گیت هست.
1,247
Machine learning books and papers

18 Jan, 11:39

با عرض سلام پروژه جدیدمون شروع شد.
هدف اصلی این پروژه اموزش یک مدل پیشنهاد دهنده ی مدل برای مسائله طبقه بندی تصاویر پزشکی میباشد که از اموزش مجدد مدل ها جلوگیری میکند. این مسائله با جنبه جلوگیری از مصرف انرژی اموزشی و زمان اموزش مدل ها ارائه می شود. برای این منظور ۵۰۰۰ مقاله در این زمینه جمع اوری شده است. جزئیات بیشتر در لینک گیت قرار دارد.

Project Title: MedRec: Medical recommender system for image classification without retraining

Github: https://github.com/Ramin1Mousa/MedicalRec

Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence

Impact factor: 20.8

۷ نفر دیگر امکان اضافه شدن به این پروژه رو دارند. هر شخص نیاز هست که حدودا داده های ۴۰۰ مقاله رو بررسی کند. زمان تقریبی هر مقاله ۵-۱۰ دقیقه می باشد. هزینه مشارکت در مقاله:

🔹 2- 600$
🔺 3- 500$
💠 4- 400$
🔺 5- 300$
🔹 6- 200$
🔸 7- 200$
جهت مشارکت می تونید به ایدی بنده پیام بدین.
@Raminmousa
1,258
Machine learning books and papers

17 Jan, 20:03

📃Understanding When and Why Graph Attention Mechanisms Work via Node Classification


📎 Study the paper

@Machine_learn
1,290
Machine learning books and papers

17 Jan, 10:42

Titans: Transformer v.2?
📃🖋Read the paper.

@Machine_learn
1,484
Machine learning books and papers

15 Jan, 15:17

Mathematicians and physicists alike will jump on this Representation theory primer by Etingof, Hensel, Golberg++

📕 Paper


@Machine_learn
1,867
Machine learning books and papers

15 Jan, 15:16

📄 Application of Artificial Intelligence In Drug-target Interactions Prediction: A Review

📗 Journal: npj Biomedical Innovations
🗓Publish year: 2025


📎 Study the paper



@Machine_learn
1,596
Machine learning books and papers

15 Jan, 07:17

📃 Bioinformatics perspectives on transcriptomics: A comprehensive review of bulk and single-cell RNA sequencing analyses



📎 Study the paper


@Machine_learn
1,413
Machine learning books and papers

14 Jan, 03:12

Are They the Same? Exploring Visual Correspondence Shortcomings of Multimodal LLMs

🖥 Github: https://github.com/zhouyiks/CoLVA/tree/main

📕 Paper: https://arxiv.org/pdf/2501.04670v1.pdf

⭐️ Dataset: https://paperswithcode.com/dataset/bdd100k

@Machine_learn
1,405
Machine learning books and papers

14 Jan, 03:12

🌟 LLaVA-CoT: VLM с


🟡Arxiv
🟡Demo
🖥GitHub


@Machine_learn
1,351
Machine learning books and papers

14 Jan, 03:09

🌟 🌟 OuteTTS-0.2-500M

# Install from PyPI
pip install outetts

# Interface Usage
import outetts

# Configure the model
model_config = outetts.HFModelConfig_v1(
model_path="OuteAI/OuteTTS-0.2-500M",
language="en", # Supported languages in v0.2: en, zh, ja, ko
)

# Initialize the interface
interface = outetts.InterfaceHF(model_version="0.2", cfg=model_config)

# Optional: Create a speaker profile (use a 10-15 second audio clip)
speaker = interface.create_speaker(
audio_path="path/to/audio/file",
transcript="Transcription of the audio file."
)

# Optional: Load speaker from default presets
interface.print_default_speakers()
speaker = interface.load_default_speaker(name="male_1")

output = interface.generate(
text="%Prompt Text%%.",
temperature=0.1,
repetition_penalty=1.1,
max_length=4096,

# Optional: Use a speaker profile
speaker=speaker,
)

# Save the synthesized speech to a file
output.save("output.wav")





🟡Demo

🖥GitHub


@Machine_learn
1,495
Machine learning books and papers

13 Jan, 15:42

این فقط نفر ۴ امش باقی مونده
1,472
Machine learning books and papers

13 Jan, 09:24

Machine learning books and papers pinned Deleted message
0
Machine learning books and papers

10 Jan, 13:54

امکان ریکام دادن برای این مقاله هم هستش...!
550
Machine learning books and papers

10 Jan, 13:50

Machine learning books and papers pinned «با عرض سلام مقاله زیر در مرحله major revision می‌باشد. نفر ۴ ام از این مقاله قابل اضافه کردن. Abstract Breast cancer stands as a prevalent cause of fatality among females on a global scale, with prompt detection playing a pivotal role in diminishing mortality…»
0
Machine learning books and papers

10 Jan, 13:50

با عرض سلام مقاله زیر در مرحله major revision می‌باشد. نفر ۴ ام از این مقاله قابل اضافه کردن.

Abstract
Breast cancer stands as a prevalent cause of fatality among females on a global scale, with
prompt detection playing a pivotal role in diminishing mortality rates. The utilization of
ultrasound scans in the BUSI dataset for medical imagery pertaining to breast cancer has
exhibited commendable segmentation outcomes through the application of UNet and UNet++
networks. Nevertheless, a notable drawback of these models resides in their inattention towards
the temporal aspects embedded within the images. This research endeavors to enrich the
UNet++ architecture by integrating LSTM layers and self-attention mechanisms to exploit
temporal characteristics for segmentation purposes. Furthermore, the incorporation of a
Multiscale Feature Extraction Module aims to grasp varied scale features within the UNet++.
Through the amalgamation of our proposed methodology with data augmentation on the BUSI
with GT dataset, an accuracy rate of 98.88%, specificity of 99.53%, precision of 95.34%,
sensitivity of 91.20%, F1-score of 93.74, and Dice coefficient of 92.74% are achieved. These
findings demonstrate competitiveness with cutting-edge techniques outlined in existing
literature.
Keywords: Attention mechanisms, BUSI dataset, Deep Learning, Feature Extraction,
Multi-Scale features
دوستانی که نیاز دارن به ایدی بنده پیام بدن.
@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
538
Machine learning books and papers

10 Jan, 13:49

🌟 DepthLab

# Clone repo
git clone https://github.com/Johanan528/DepthLab.git
cd DepthLab

# Create conda env
conda env create -f environment.yaml
conda activate DepthLab

# Run inference
cd scripts
bash infer.sh


🟡Arxiv
🖥GitHub


@Machine_learn
503
Machine learning books and papers

09 Jan, 19:33

Are They the Same? Exploring Visual Correspondence Shortcomings of Multimodal LLMs

🖥 Github: https://github.com/zhouyiks/CoLVA/tree/main

📕 Paper: https://arxiv.org/pdf/2501.04670v1.pdf

🌟 Dataset: https://paperswithcode.com/dataset/bdd100k

@Machine_learn
769
Machine learning books and papers

09 Jan, 19:22

با عرض سلام تمامي كار هاي مشترك تموم شدن و فقط اين كار باقي مونده....!
@Raminmousa
748
Machine learning books and papers

09 Jan, 14:37

Towards System 2 Reasoning in LLMs

📕 Link


@Machine_learn
894
Machine learning books and papers

09 Jan, 14:36

📑 Advances of the recent data-driven paradigm shift in medicine and healthcare: From machine learning to deep learning

📎 Study the paper


@Machine_learn
871
Machine learning books and papers

08 Jan, 16:30

📃 Large language model to multimodal large language model: A journey to shape the biological macromolecules to biological sciences and medicine

📓 Journal: Molecular Therapy Nucleic Acids (I.F.=6.5)



📎 Study the paper


@Machine_learn
1,077
Machine learning books and papers

08 Jan, 14:05

با عرض سلام مقاله زیر در مرحله major revision می‌باشد. نفر ۴ ام از این مقاله قابل اضافه کردن.

Abstract
Breast cancer stands as a prevalent cause of fatality among females on a global scale, with
prompt detection playing a pivotal role in diminishing mortality rates. The utilization of
ultrasound scans in the BUSI dataset for medical imagery pertaining to breast cancer has
exhibited commendable segmentation outcomes through the application of UNet and UNet++
networks. Nevertheless, a notable drawback of these models resides in their inattention towards
the temporal aspects embedded within the images. This research endeavors to enrich the
UNet++ architecture by integrating LSTM layers and self-attention mechanisms to exploit
temporal characteristics for segmentation purposes. Furthermore, the incorporation of a
Multiscale Feature Extraction Module aims to grasp varied scale features within the UNet++.
Through the amalgamation of our proposed methodology with data augmentation on the BUSI
with GT dataset, an accuracy rate of 98.88%, specificity of 99.53%, precision of 95.34%,
sensitivity of 91.20%, F1-score of 93.74, and Dice coefficient of 92.74% are achieved. These
findings demonstrate competitiveness with cutting-edge techniques outlined in existing
literature.
Keywords: Attention mechanisms, BUSI dataset, Deep Learning, Feature Extraction,
Multi-Scale features
دوستانی که نیاز دارن به ایدی بنده پیام بدن.
@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
975
Machine learning books and papers

08 Jan, 07:24

✨Data base normalization✨

@Machine_learn
1,016
Machine learning books and papers

07 Jan, 13:44

دوستان از این بین Biopars برای نیچر هستش.
841
Machine learning books and papers

07 Jan, 09:41

Machine learning books and papers pinned «با عرض سلام دوستان كه مي خوان توي تيم هاي paper ما شركت كنن موضوعات زير رو مي خواهيم جلو ببريم. 1: survey on whole slide image ▫️ 2: Proposed a new model for enrergy efficiency in deep image classification models Authers: 2, 3, 4 🔺 3:BioPars: a pretrained…»
0
Machine learning books and papers

07 Jan, 09:41

با عرض سلام دوستان كه مي خوان توي تيم هاي paper ما شركت كنن موضوعات زير رو مي خواهيم جلو ببريم.

1: survey on whole slide image

▫️

2: Proposed a new model for enrergy efficiency in deep image classification models
Authers: 2, 3, 4 🔺

3:BioPars: a pretrained biomedical large language
model for persian biomedical text mining
Authors: 5🔺

4: Air quality prediction by hybrid deep learning and machine learning models
Authors:4🔺
در تمامی این موارد نیاز به انجام تسک و پرداخت هزینه سرور ها می باشیم.

@Raminmousa
1,315
Machine learning books and papers

07 Jan, 08:40

Tensors in computations

📕Book

@Machine_learn
1,285
Machine learning books and papers

07 Jan, 08:39

Automating the Search for Artificial Life with Foundation Models

paper: https://arxiv.org/pdf/2412.17799v1.pdf

Code: https://github.com/sakanaai/asal

@Machine_learn
1,113
Machine learning books and papers

06 Jan, 07:46

📽 Introduction to Network Analysis using NetworkX

🎞 Watch

@Machine_learn
1,453
Machine learning books and papers

06 Jan, 07:46

📃A Survey of Graph Neural Networks for Social Recommender Systems


📎 Study paper

@Machine_learn
1,298
Machine learning books and papers

05 Jan, 14:05

هزینه نهایی برای این کار رو به ۲۵ میلیون کاهش دادیم برای نفر ۵ ...!🔥
1,342
Machine learning books and papers

05 Jan, 14:04

با عرض سلام پروژه Biopars رو شروع كرديم نفر ٥ ام از اين مقاله رو نياز داريم.
این کار تحت نظر استاد
Rex (Zhitao) Ying
انجام میشه.
link: https://scholar.google.com.au/citations?user=6fqNXooAAAAJ&hl=en
BioPars: a pre-trained biomedical large language model for persian biomedical text mining.
١- مراحل اوليه: جمع اوري متن هاي فارسي بيولوژيكي از منابع (...)
٢- پيش پردازش متن ها و تميز كردن متن ها
٣- اموزش ترنسفورمرها ي مورد نظر
٤- استفاده از بردارها ي اموزش داده شده در سه تسك (...)

هزينه سرور به ازاي هر ساعت ١.٢ دلار مي باشد. و حدود ٢ هزار ساعت براي اموزش مدل زباني نياز ميباشد.

دوستاني كه نياز دارن مي تونن به تيم ما اضافه بشن
🔸🔸🔸🔸🔸

@Raminmousa
681
Machine learning books and papers

05 Jan, 13:55

⚡️ NeuZip

▶️

# Install from PyPI
pip install neuzip

# Use Neuzip for Pytorch model
model: torch.nn.Module = # your model
+ manager = neuzip.Manager()
+ model = manager.convert(model)


🟡Arxiv
🖥GitHub


@Machine_learn
1,280
Machine learning books and papers

04 Jan, 18:24

Machine learning books and papers pinned Deleted message
0
Machine learning books and papers

04 Jan, 17:19

🌟 🌟 OuteTTS-0.2-500M

# Install from PyPI
pip install outetts

# Interface Usage
import outetts

# Configure the model
model_config = outetts.HFModelConfig_v1(
model_path="OuteAI/OuteTTS-0.2-500M",
language="en", # Supported languages in v0.2: en, zh, ja, ko
)

# Initialize the interface
interface = outetts.InterfaceHF(model_version="0.2", cfg=model_config)

# Optional: Create a speaker profile (use a 10-15 second audio clip)
speaker = interface.create_speaker(
audio_path="path/to/audio/file",
transcript="Transcription of the audio file."
)

# Optional: Load speaker from default presets
interface.print_default_speakers()
speaker = interface.load_default_speaker(name="male_1")

output = interface.generate(
text="%Prompt Text%%.",
temperature=0.1,
repetition_penalty=1.1,
max_length=4096,

# Optional: Use a speaker profile
speaker=speaker,
)

# Save the synthesized speech to a file
output.save("output.wav")

🟡Demo

🖥GitHub

@Machine_learn
1,397
Machine learning books and papers

04 Jan, 17:16

🔥 nn-zero-to-hero

🖥 Github

@Machine_learn
1,213
Machine learning books and papers

04 Jan, 06:40

امشب اخرین فرصت برای مشارکت در این مقاله هستش...!🔸🔸
1,331
Machine learning books and papers

03 Jan, 19:22

با عرض سلام پروژه Biopars رو شروع كرديم نفر ٥ ام از اين مقاله رو نياز داريم.
این کار تحت نظر استاد
Rex (Zhitao) Ying
انجام میشه.
link: https://scholar.google.com.au/citations?user=6fqNXooAAAAJ&hl=en
BioPars: a pre-trained biomedical large language model for persian biomedical text mining.
١- مراحل اوليه: جمع اوري متن هاي فارسي بيولوژيكي از منابع (...)
٢- پيش پردازش متن ها و تميز كردن متن ها
٣- اموزش ترنسفورمرها ي مورد نظر
٤- استفاده از بردارها ي اموزش داده شده در سه تسك (...)

هزينه سرور به ازاي هر ساعت ١.٢ دلار مي باشد. و حدود ٢ هزار ساعت براي اموزش مدل زباني نياز ميباشد.

دوستاني كه نياز دارن مي تونن به تيم ما اضافه بشن
🔸🔸🔸🔸🔸

@Raminmousa
1,348
Machine learning books and papers

02 Jan, 08:45

Lecture notes: mathematics for artificial intelligence

📕 Link


@Machine_learn
1,895
Machine learning books and papers

02 Jan, 08:44

📄 RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis


📎 Study the paper


@Machine_learn
1,608
Machine learning books and papers

01 Jan, 06:48

با عرض سلام خيلي از دوستان در رابطه با طراحي صفر تا صد پروژه هاي ديپ از بنده سوال پرسيدن داخل پك زير ٣٦ پروژه رو با جزئيات شرح دادم:

1-Deep Learning Basic
-01_Introduction
--01_How_TensorFlow_Works
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
دوستاني كه نياز به اين پروژه ها دارن ميتونن با بنده در ارتباط باشن.
@Raminmousa
@Machine_learn
433
Machine learning books and papers

31 Dec, 03:09

Arcade Academy - Learn Python

📖 Book

@Machine_learn
2,025
Machine learning books and papers

30 Dec, 16:27

CogAgent: A Visual Language Model for GUI Agents

Paper: https://arxiv.org/pdf/2312.08914v3.pdf

CVPR 2024: http://openaccess.thecvf.com//content/CVPR2024/papers/Hong_CogAgent_A_Visual_Language_Model_for_GUI_Agents_CVPR_2024_paper.pdf

Code1: https://github.com/thudm/cogvlm
Code2: https://github.com/digirl-agent/digirl
Code3: https://github.com/THUDM/CogAgent

Dataset: TextVQA

💠@Machine_learn
2,016
Machine learning books and papers

30 Dec, 16:26

KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation

Paper: https://arxiv.org/pdf/2409.13731v3.pdf

Code: https://github.com/openspg/kag

Dataset: 2WikiMultiHopQA

🔸@Machine_learn
1,718
Machine learning books and papers

30 Dec, 11:25

Python for Everybody Exploring Data Using Python 3

📓 book

@Machine_learn
1,758
Machine learning books and papers

29 Dec, 13:09

Large Language Models Course: Learn by Doing LLM Projects

🖥 Github: https://github.com/peremartra/Large-Language-Model-Notebooks-Course

📕 Paper: https://doi.org/10.31219/osf.io/qgxea

@Machine_learn
1,946
Machine learning books and papers

28 Dec, 12:44

📚 Transfer Learning for CNNs: Leveraging Pre-trained Models


Transfer learning is a machine learning technique where a pre-trained model is used as a starting point for a new task. In the context of convolutional neural networks (CNNs), this means using a CNN that has been trained on a large dataset for one task (e.g., ImageNet) as a foundation for a new task (e.g., classifying medical images).


🌐 Why Transfer Learning?


1. Reduced Training Time: Training a CNN from scratch on a large dataset can be computationally expensive and time-consuming. Transfer learning allows you to leverage the knowledge learned by the pre-trained model, reducing training time significantly.
2. Improved Performance: Pre-trained models have often been trained on massive datasets, allowing them to learn general-purpose features that can be useful for a wide range of tasks. Using these pre-trained models can improve the performance of your new task.
3. Smaller Datasets: Transfer learning can be particularly useful when you have a small dataset for your new task. By using a pre-trained model, you can augment your limited data with the knowledge learned from the larger dataset.


💸 How Transfer Learning Works:


1. Choose a Pre-trained Model: Select a pre-trained CNN that is suitable for your task. Common choices include VGG16, ResNet, InceptionV3, and EfficientNet.
2. Freeze Layers: Typically, the earlier layers of a CNN learn general-purpose features, while the later layers learn more task-specific features. You can freeze the earlier layers of the pre-trained model to prevent them from being updated during training. This helps to preserve the learned features
3. Add New Layers: Add new layers, such as fully connected layers or convolutional layers, to the end of the pre-trained model. These layers will be trained on your new dataset to learn task-specific features.
4. Fine-tune: Train the new layers on your dataset while keeping the frozen layers fixed. This process is called fine-tuning.


🔊 Common Transfer Learning Scenarios:


1. Feature Extraction: Extract features from the pre-trained model and use them as input to a different model, such as a support vector machine (SVM) or a random forest.
2. Fine-tuning: Fine-tune the pre-trained model on your new dataset to adapt it to your specific task.
3. Hybrid Approach: Combine feature extraction and fine-tuning by extracting features from the pre-trained model and using them as input to a new model, while also fine-tuning some layers of the pre-trained model.


Transfer learning is a powerful technique that can significantly improve the performance and efficiency of CNNs, especially when working with limited datasets or time constraints.

🚀 Common Used Transfer Learning Meathods:

1️⃣. VGG16: A simple yet effective CNN architecture with multiple convolutional layers followed by max-pooling layers. It excels at image classification tasks.

2️⃣ . MobileNet: Designed for mobile and embedded vision applications, MobileNet uses depthwise separable convolutions to reduce the number of parameters and computational cost.

3️⃣ DenseNet: Connects each layer to every other layer, promoting feature reuse and improving information flow. It often achieves high accuracy with fewer parameters.

4️⃣ Inception: Employs a combination of different sized convolutional filters in parallel, capturing features at multiple scales. It's known for its efficient use of computational resources.

5️⃣ ResNet: Introduces residual connections, enabling the network to learn more complex features by allowing information to bypass layers. It addresses the vanishing gradient problem.

6️⃣ EfficientNet: A family of models that systematically scale up network width, depth, and resolution using a compound scaling method. It achieves state-of-the-art accuracy with improved efficiency.

7️⃣ NASNet: Leverages neural architecture search to automatically design efficient CNN architectures. It often outperforms manually designed models in terms of accuracy and efficiency.

@Machine_learn
1,848
Machine learning books and papers

27 Dec, 16:09

با عرض سلام اخرين فرصت مشاركت در اين مقاله تا فردا شب...!
2,072
Machine learning books and papers

27 Dec, 10:25

🌟 RLtools

🟢TD3 - Pendulum, Racing Car, MuJoCo Ant-v4, Acrobot;
🟢PPO - Pendulum, Racing Car, MuJoCo Ant-v4 (CPU), MuJoCo Ant-v4 (CUDA);
🟢Multi-Agent PPO - Bottleneck;
🟢SAC - Pendulum (CPU), Pendulum (CUDA), Acrobot.





# Clone and checkout
git clone https://github.com/rl-tools/example
cd example
git submodule update --init external/rl_tools

# Build and run
mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
cmake --build .
./my_pendulum




🟡Arxiv
🟡RLTools Design Studio
🟡Demo
🟡Zoo Experiment Tracking
🟡Google Collab (Python Interface)
🖥GitHub


@Machine_learn
1,675
Machine learning books and papers

27 Dec, 10:20

📌 Convex Optimization

Book

@Machine_learn
1,313
Machine learning books and papers

27 Dec, 08:32

با عرض سلام
اولين مقاله ي LLM ما در مرحله ي سابميت. نفر چهارم قابل اضافه كردن مي باشد. جهت مشاركت به ايدي بنده مراجعه كنين.


ExKG-LLM: Leveraging Large Language Models for Automated Expan-
sion of Cognitive Neuroscience Knowledge Graphs


Abstract
Objective: This paper introduces ExKG-LLM, an innovative framework designed to automate expanding cognitive neuroscience knowledge graphs (CNKG) using large-scale linguistic models (LLM). This model includes increasing knowledge graphs’ accuracy, completeness and usefulness in cognitive neuroscience.

Method: To address the limitations of existing tools for creating knowledge accounts, this is especially true in dealing with the complex hierarchical relationships within the cognitive neuroscience literature. We use a large dataset of scientific paper and clinical reports, the ExKG-LLM framework, new entities and relationships in CNKG to apply state - state of the art LLM to extract, optimize and integrate, evaluating performance based on
metrics such as precision, recall and graph density.

Findings: The ExKG-LLM framework achieved significant improvements, including precision of 0.80 (increase of 6.67%), recall of 0.81 (increase of 15.71%), F1 score of 0.805 (increase of 11.81%), and number of edge nodes increased by 21.13% and 31.92%, respectively. Also, the density of the graph decreased slightly. Reflecting the broader but more fragmented structure, engagement rates have also increased by 20%, highlighting areas where stability needs improvement. From the perspective of a complex network, increasing the diameter of CNKG to 15 compared to 13 shows that although the size of ExKG-LLM has increased, more steps are now required to discover additional nodes.Although time complexity improved to 𝑂(𝑛log 𝑛), space complexity became less efficient, rising to 𝑂(𝑛2), indicating higher memory usage for managing the expanded
graph.
journal: https://www.inderscience.com/jhome.php?jcode=ijdmb


هزينه مشاركت ١٢ ميليون
@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
1,442
Machine learning books and papers

27 Dec, 07:06

Text-to-Image Generation with GANs
#GANs
@Machine_learn
1,496
Machine learning books and papers

26 Dec, 14:18

Approaching (Almost) Any Machine Learning Problem
#Book
#ML

@Machine_learn
1,729
Machine learning books and papers

26 Dec, 13:10

با عرض سلام
اولين مقاله ي LLM ما در مرحله ي سابميت. نفر چهارم قابل اضافه كردن مي باشد. جهت مشاركت به ايدي بنده مراجعه كنين.


ExKG-LLM: Leveraging Large Language Models for Automated Expan-
sion of Cognitive Neuroscience Knowledge Graphs


Abstract
Objective: This paper introduces ExKG-LLM, an innovative framework designed to automate expanding cognitive neuroscience knowledge graphs (CNKG) using large-scale linguistic models (LLM). This model includes increasing knowledge graphs’ accuracy, completeness and usefulness in cognitive neuroscience.

Method: To address the limitations of existing tools for creating knowledge accounts, this is especially true in dealing with the complex hierarchical relationships within the cognitive neuroscience literature. We use a large dataset of scientific paper and clinical reports, the ExKG-LLM framework, new entities and relationships in CNKG to apply state - state of the art LLM to extract, optimize and integrate, evaluating performance based on
metrics such as precision, recall and graph density.

Findings: The ExKG-LLM framework achieved significant improvements, including precision of 0.80 (increase of 6.67%), recall of 0.81 (increase of 15.71%), F1 score of 0.805 (increase of 11.81%), and number of edge nodes increased by 21.13% and 31.92%, respectively. Also, the density of the graph decreased slightly. Reflecting the broader but more fragmented structure, engagement rates have also increased by 20%, highlighting areas where stability needs improvement. From the perspective of a complex network, increasing the diameter of CNKG to 15 compared to 13 shows that although the size of ExKG-LLM has increased, more steps are now required to discover additional nodes.Although time complexity improved to 𝑂(𝑛log 𝑛), space complexity became less efficient, rising to 𝑂(𝑛2), indicating higher memory usage for managing the expanded
graph.
journal: https://www.inderscience.com/jhome.php?jcode=ijdmb
@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
1,798
Machine learning books and papers

26 Dec, 08:27

Machine learning books and papers pinned Deleted message
0
Machine learning books and papers

26 Dec, 08:27

فقط نفر سوم از اين تحقيق باقي مونده. خروجي كار سه مقاله خواهد بود...! 💠💠 امكان ريكام نيز فراهم است.💠💠
793
Machine learning books and papers

25 Dec, 19:06

New research papers and github codes

🟢Motivo
🟡Paper 🟡Demo 🟡Github
🟢Video Seal
🟡Paper 🟡Demo 🟡Github
🟢Flow Matching
🟡Paper 🟡Github
🟢Explore Theory-of-Mind
🟡Paper 🟡Github 🟡Dataset
🟢Large Concept Model (LCM)
🟡Paper 🟡Github
🟢Dynamic Byte Latent Transformer
🟡Paper 🟡Github
🟢Memory Layers.
🟡Paper 🟡Github
🟢EvalGym
🟡Paper 🟡Github
🟢CLIP 1.2
🟡Paper 🟡Github 🟡Dataset 🟡Model

@Machine_learn
1,843
Machine learning books and papers

25 Dec, 11:31

دوستان خروجي اين كار ٣ تا مقاله خواهد بود...!
1,700
Machine learning books and papers

25 Dec, 09:40

Machine learning books and papers pinned Deleted message
0
Machine learning books and papers

25 Dec, 09:40

با عرض سلام در راستاي ادامه تحقيقات مشترك سعي داريم روي حوزه ي LLM مدل ها كار كنيم.
این کار تحت نظر استاد
Rex (Zhitao) Ying
انجام میشه.
link: https://scholar.google.com.au/citations?user=6fqNXooAAAAJ&hl=en
۲نفر براي همکاری نياز داريم.

BioPars: a pre-trained biomedical large language model for persian biomedical text mining.
١- مراحل اوليه: جمع اوري متن هاي فارسي بيولوژيكي از منابع (...)
٢- پيش پردازش متن ها و تميز كردن متن ها
٣- اموزش ترنسفورمرها ي مورد نظر
٤- استفاده از بردارها ي اموزش داده شده در سه تسك (...)
دوستاني كه مايل به مشاركت هستن مي تونين بهم اطلاع بدن.
هزينه سرور به ازاي هر ساعت ١.٢ دلار مي باشد. و حدود ٢ هزار ساعت براي اموزش مدل زباني نياز ميباشد. هزينه به ترتيب براي نفرات علاوه بر انجام تسك ها به صورت زير مي باشد.
🔹نفر سوم 500 دلار
🔺نفر چهارم 400 دلار
@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
1,129
Machine learning books and papers

23 Dec, 20:40

🌟 LLaMA-Mesh:
🟡Arxiv
🖥GitHub

https://t.me/deep_learning_proj
2,297
Machine learning books and papers

23 Dec, 20:38

🌟 AlphaFold 3

🟡Paper
🟡Demo
🖥GitHub


@Machine_learn
2,126
Machine learning books and papers

23 Dec, 08:42

Building Blocks for Theoretical Computer Science

🎓 Link

@Machine_learn
1,764
Machine learning books and papers

22 Dec, 08:21

📑 Application of graph theory in liver research: A review

📎 Study paper

@Machine_learn
1,908
Machine learning books and papers

21 Dec, 14:40

Probability, Random Processes, and Statistical Analysis Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance

📕 Book


@Machine_learn
1,708
Machine learning books and papers

21 Dec, 14:38

Book: The Art of Data Science
Authors: Roger D. Peng & Elizabeth Matsui

@Machine_learn
1,597
Machine learning books and papers

21 Dec, 14:36

Time Series Visualization from Raw Data to Insights
🔹 #Code

@Machine_learn
1,624
Machine learning books and papers

21 Dec, 10:50

New o3 OpenAI model is changing the game!

For a long time, ARC was seen as proof that AI models “can’t think.” The argument went: if they truly could, why do they perform so poorly on this benchmark?

Well, those days are over. The o3 model demonstrates not only the ability to think but also the capability to tackle tasks once considered out of reach.

👀 Check out the full breakdown of this breakthrough: https://arcprize.org/blog/oai-o3-pub-breakthrough

It might be time to rethink what AI can achieve. Looking forward to the release!

@Machine_learn
1,700
Machine learning books and papers

21 Dec, 06:40

Gemini API Cookbook

📚 Github


@Machine_learn
1,669
Machine learning books and papers

20 Dec, 13:50

Perfect Roadmap To Learn Data Science In 2024

📖 Book

@Machine_learn
1,750
Machine learning books and papers

19 Dec, 19:33

🌟 SmolLM2



⏩SmolLM2-1.7B🟢SmolLM2-1.7B-Instruct🟢Instruct GGUF

⏩SmolLM2-360M🟠SmolLM2-360M-Instruct 🟠Instruct GGUF

⏩SmolLM2-135M 🟠SmolLM2-135M-Instruct 🟠Instruct GGUF от комьюнити


▶️SmolLM2-1.7B :

from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "HuggingFaceTB/SmolLM2-1.7B"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)

model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
inputs = tokenizer.encode("Gravity is", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))

📌Apache 2.0 License.


🟡Demo SmolLM2 1.7B


@Machine_learn
1,617
Machine learning books and papers

19 Dec, 19:30

Practitioner Guide for Creating Effective Prompts in Large Language Models

🔗 Paper

@Machine_learn
1,516
Machine learning books and papers

19 Dec, 10:21

تنها نفر ۴ ام از این کار مشترک باقی مونده
شروع کار ۱ دی ماه هستش. جهت همکاری به ایدی بنده پیام بدین.
@Raminmousa
1,373
Machine learning books and papers

19 Dec, 03:03

Introduction to Data Science – Lecture Material

🔗 Github

@Machine_learn
1,522
Machine learning books and papers

18 Dec, 20:21

🀄 GuoFeng Webnovel: A Discourse-Level and Multilingual Corpus of Web Fiction

🖥 Github: https://github.com/longyuewangdcu/guofeng-webnovel

📕 Paper: https://arxiv.org/abs/2412.11732v1

🌟 Dataset: www2.statmt.org/wmt24/literary-trans

@Machine_learn
1,562
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18 Dec, 06:41

Machine learning books and papers pinned Deleted message
0
Machine learning books and papers

17 Dec, 19:58

📃A Comprehensive Survey on Automatic Knowledge Graph Construction

📎 Study paper

@Machine_learn
1,524
Machine learning books and papers

17 Dec, 12:34

PDF Math Translate

DF scientific paper translation with preserved formats

Creator: Byaidu
Stars ⭐️: 5.1k
Forked By: 375
https://github.com/Byaidu/PDFMathTranslate

@Machine_learn
1,696
Machine learning books and papers

30 Nov, 18:10

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

🔗 Book


@Machine_learn
587
Machine learning books and papers

30 Nov, 18:09

📃Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects

📎 Study the paper

@Machine_learn
575
Machine learning books and papers

30 Nov, 14:18

Machine learning books and papers pinned «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…»
0
Machine learning books and papers

30 Nov, 14:18

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
717
Machine learning books and papers

29 Nov, 16:32

Gaussian Processes for Machine Learning

📚 link

@Machine_learn
1,015
Machine learning books and papers

28 Nov, 19:10

Machine learning books and papers pinned «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»
0
Machine learning books and papers

28 Nov, 19:09

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
1,255
Machine learning books and papers

28 Nov, 19:09

⚡️ 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
1,157
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28 Nov, 17:09

📖 General Relativity

📌 Book

@Machine_learn
1,114
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27 Nov, 19:52

Machine learning books and papers pinned Deleted message
0
Machine learning books and papers

27 Nov, 19:49

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
1,357
Machine learning books and papers

27 Nov, 11:58

📖 Penn State University's "Graph Theory"


📌 Lectures

@Machine_learn
1,448
Machine learning books and papers

27 Nov, 02:52

O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson?

🖥 Github: https://github.com/gair-nlp/o1-journey

📕 Paper: https://arxiv.org/abs/2411.16489v1

🌟 Dataset: https://paperswithcode.com/dataset/lima

💠@Machine_learn
1,541
Machine learning books and papers

26 Nov, 20:10

👩‍💻 Julia Programming Language for Biologists





📎 Study the paper


@Machine_learn
1,495
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26 Nov, 07:46

تيم دوم :
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 و توي نگارش مقاله كمكمون كنه.

@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
1,531
Machine learning books and papers

25 Nov, 19:53

📑 A review of transformers in drug discovery and beyond


📎 Study the paper



🔺@Machine_learn
1,613
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25 Nov, 18:51

https://github.com/andrewyng/aisuite
#LLMs

https://t.me/deep_learning_proj
1,414
Machine learning books and papers

25 Nov, 18:49

📄Advancing biomolecular simulation through exascale HPC, AI and quantum computing


📎 Study the paper

@Machine_learn
1,441
Machine learning books and papers

25 Nov, 17:53

C O M P U T E R V I S I O N : F O U N D AT I O N S A N D A P P L I C AT I O N S

🖥 book

@Machine_learn
1,372
Machine learning books and papers

25 Nov, 17:52

Primers • Overview of Large Language Models

📖 Link

@Machine_learn
1,326
Machine learning books and papers

22 Nov, 20:01

با عرض سلام
جایگاه ۲ از مقاله زیر باقی مونده دوستانی که نیاز دارند به ایدی بنده پیام بدن.
همچنین امکان ریکام دادن بعد چاپ امکان پذیر.
title:
UNet++ and LSTM combined approach for Breast Ultrasound Image Segmentation

Abstract:

 Breast cancer stands as a prevalent cause of fatality among females on a global scale, with prompt detection playing a pivotal role in diminishing mortality rates. The utilization of ultrasound scans in the BUSI dataset for medical imagery pertaining to breast cancer has exhibited commendable segmentation outcomes through the application of UNet and UNet++ networks. Nevertheless, a notable drawback of these models resides in their inattention towards the temporal aspects embedded within the images. This research endeavors to enrich the UNet++ architecture by integrating LSTM layers and self-attention mechanisms to exploit temporal characteristics for segmentation purposes. Furthermore, the incorporation of a Multiscale Feature Extraction Module aims to grasp varied scale features within the UNet++. Through the amalgamation of our proposed methodology with data augmentation on the BUSI with GT dataset, an accuracy rate of 98.88%, specificity of 99.53%, precision of 95.34%, sensitivity of 91.20%, F1-score of 93.74, and Dice coefficient of 92.74% are achieved. These findings demonstrate competitiveness with cutting-edge techniques outlined in existing literature. Keywords: Attention mechanisms, BUSI dataset, Deep Learning, Feature Extraction, Multi-Scale features  

🔹@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
198
Machine learning books and papers

22 Nov, 19:54

Compare NLP Transformer-based Models used for Sentiment Analysis code

🔺@Machine_learn
261
Machine learning books and papers

22 Nov, 19:52

Decision Trees: A Comprehensive Guide
with Handwritten Notes, Explanations,
and Code

#DT
🔸@Machine_learn
290
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22 Nov, 16:03

BashBook

📚 Book

@Machine_learn
602
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22 Nov, 08:16

Machine learning books and papers pinned Deleted message
0
Machine learning books and papers

21 Nov, 09:58

📃Understanding Graph Databases: A Comprehensive Tutorial and Survey

📎 Study paper

@Machine_learn
1,266
Machine learning books and papers

20 Nov, 07:46

A Brief Introduction to Neural Networks

📕 Book

@Machine_learn
1,432
Machine learning books and papers

20 Nov, 07:46

Nexusflow released Athene v2 72B - competetive with GPT4o & Llama 3.1 405B Chat, Code and Math 🔥

> Arena Hard: GPT4o (84.9) vs Athene v2 (77.9) vs L3.1 405B (69.3)

> Bigcode-Bench Hard: GPT4o (30.8) vs Athene v2 (31.4) vs L3.1 405B (26.4)

> MATH: GPT4o (76.6) vs Athene v2 (83) vs L3.1 405B (73.8)

> Models on the Hub along and work out of the box w/ Transformers 🤗

https://huggingface.co/Nexusflow/Athene-V2-Chat

They also release an Agent model: https://huggingface.co/Nexusflow/Athene-V2-Agent

@Machine_learn
1,311
Machine learning books and papers

18 Nov, 20:48

Collection of resources in the form of eBooks related to Data Science, Machine Learning, and similar topics

📖 Github

@Machine_learn
1,636
Machine learning books and papers

18 Nov, 20:48

Deep Learning and Computational Physics - Lecture Notes, University of South California

📓 book

@Machine_learn
1,548
Machine learning books and papers

17 Nov, 11:46

Competitive Programmer's Handbook

📚 Book

🔺@Machine_learn
1,844
Machine learning books and papers

17 Nov, 08:48

FRONTIERMATH: A BENCHMARK FOR EVALUATING ADVANCED
MATHEMATICAL REASONING IN AI

📚 Read

💠@Machine_learn
1,874
Machine learning books and papers

17 Nov, 08:43

How to Build Your Career in AI

📚 Book

@Machine_learn
1,696
Machine learning books and papers

16 Nov, 11:44

DeepArUco++: improved detection of square fiducial markers in challenging lighting conditions

🖥 Github: https://github.com/avauco/deeparuco

📕 Paper: https://arxiv.org/pdf/2411.05552v1.pdf

⚡️ Dataset: https://paperswithcode.com/dataset/coco

@Machine_learn
1,774
Machine learning books and papers

16 Nov, 11:40

The Arcade Learning Environment (ALE) is a simple framework that allows researchers and hobbyists to develop AI agents for Atari 2600 game

🖥 Github: https://github.com/farama-foundation/arcade-learning-environment

📕 Paper: https://arxiv.org/abs/2410.23810v1

⚡️ Dataset: https://paperswithcode.com/dataset/mujoco

@Machine_learn
1,746
Machine learning books and papers

16 Nov, 07:05

📃A Comprehensive Review of Propagation Models in Complex Networks: From Deterministic to Deep Learning Approaches


📎 Study paper

🔺@Machine_learn
1,762
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16 Nov, 07:04

📃A Comprehensive Survey on Automatic Knowledge Graph Construction



📎 Study paper

🔺@Machine_learn
1,717
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15 Nov, 17:26

Machine learning books and papers pinned Deleted message
0
Machine learning books and papers

15 Nov, 07:27

20 Python Libraries You Aren't Using But Should

📕 Book

@Machine_learn
2,224
Machine learning books and papers

13 Nov, 21:14

🖥 Awesome LLM Strawberry (OpenAI o1)



▪ Github

✅https://t.me/deep_learning_proj
2,422
Machine learning books and papers

12 Nov, 13:26

با عرض سلام نفرات ٢ و ٣ اين مقاله باقي موندن
633
Machine learning books and papers

12 Nov, 12:39

📖 A Data-Centric Introduction to Computing



link

@Machine_learn
1,204
Machine learning books and papers

10 Nov, 23:13

Financial Statement Analysis with Large Language Models (LLMs)

📕 Book

@Machine_learn
1,624
Machine learning books and papers

10 Nov, 22:21

Foundations Of The Theory Of Probability by
Andrey Nikolaevich Kolmogorov
🔥🔥🔥
Read the book

@Machine_learn
1,429
Machine learning books and papers

10 Nov, 10:18

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://t.me/+SP9l58Ta_zZmYmY0
1,479
Machine learning books and papers

10 Nov, 10:17

How to Build Your Career in AI

📚 Book

@Machine_learn
1,391
Machine learning books and papers

10 Nov, 10:16

understanding deep learning

📚 Book

@Machine_learn
1,383
Machine learning books and papers

10 Nov, 10:15

Applied Mathematics of the Future

📚 Book

@Machine_learn
1,449
Machine learning books and papers

08 Nov, 18:01

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

📖 book

💠@Machine_learn
2,067
Machine learning books and papers

07 Nov, 15:14

📃 Plant-based anti-cancer drug discovery using computational approaches

📎 Study the paper

@Machine_learn
2,141
Machine learning books and papers

07 Nov, 07:56

Constrained Diffusion Implicit Models!

We use diffusion models to solve noisy inverse problems like inpainting, sparse-recovery, and colorization. 10-50x faster than previous methods!

Paper: arxiv.org/pdf/2411.00359

Demo: https://t.co/m6o9GLnnZF

@Machine_learn
2,099
Machine learning books and papers

07 Nov, 07:55

Smol TTS models are here! OuteTTS-0.1-350M - Zero shot voice cloning, built on LLaMa architecture, CC-BY license! 🔥

> Pure language modeling approach to TTS
> Zero-shot voice cloning
> LLaMa architecture w/ Audio tokens (WavTokenizer)
> BONUS: Works on-device w/ llama.cpp ⚡

Three-step approach to TTS:

> Audio tokenization using WavTokenizer (75 tok per second).
> CTC forced alignment for word-to-audio token mapping.
> Structured prompt creation w/ transcription, duration, audio tokens.

https://huggingface.co/OuteAI/OuteTTS-0.1-350M

@Machine_learn
1,767
Machine learning books and papers

07 Nov, 07:54

📕 Machine Learning for Absolute Beginners

▪️Link

@Machine_learn
1,591
Machine learning books and papers

06 Nov, 12:39

Machine Learning with PyTorch and Scikit-Learn Book

📚 book

@Machine_learn
1,982
Machine learning books and papers

05 Nov, 19:03

AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent

🖥 Github: https://github.com/thudm/autowebglm

📕 Paper: https://arxiv.org/abs/2404.03648v1

🔥Dataset: https://paperswithcode.com/dataset/mind2web

@Machine_learn
2,108
Machine learning books and papers

04 Nov, 20:10

❤️ اکستنشن ChatGPT Search برای مرورگرهای کرومیوم منتشر شد

از طریق این لینک میتونید این افزونه رو دانلود کنید
@Machine_learn
2,221
Machine learning books and papers

04 Nov, 15:50

Conformal prediction under ambiguous ground truth

Paper: https://arxiv.org/pdf/2307.09302v2.pdf

Codes:

https://github.com/google-deepmind/uncertain_ground_truth

https://github.com/alaalab/webcp

Dataset: Dermatology ddx dataset

@Machine_learn
2,346
Machine learning books and papers

04 Nov, 08:08

فقط جایگاه دوم از این مقاله باقی مونده
1,827
Machine learning books and papers

04 Nov, 06:09

الحمدالله تو اين بازه ٣ ماه تونستيم مقالات مشاركتي رو تحت وظايف زير انجام بديم:
🔹ثبت ٤ مقاله در حوزه Multi-modal wond classification

🔹ارائه ی دو مقاله در حوزه ی breast cancer segmentation

🔹 ارائه ی سه مقاله در حوزه ی cancer detection
که ۸۰٪ مراحل این مقالات هم تموم شده.

به زودی پس از اتمام این مقالات لیستی از مقالات مشارکتی رو خواهیم داشت .

https://t.me/+SP9l58Ta_zZmYmY0
1,962
Machine learning books and papers

04 Nov, 04:38

👩‍💻 Python Notes for Professionals book

🔗 Book

@Machine_learn
2,088
Machine learning books and papers

04 Nov, 04:37

📖 LLM-Agent-Paper-List is a repository of papers on the topic of agents based on large language models (LLM)! The papers are divided into categories such as LLM agent architectures, autonomous LLM agents, reinforcement learning (RL), natural language processing methods, multimodal approaches and tools for developing LLM agents, and more.

🖥 Github

https://t.me/deep_learning_proj
1,856
Machine learning books and papers

03 Nov, 09:26

💠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

جايگاه ٢ و ٤ اين مقاله رو نياز داريم.
دوستاني كه مايل به شركت هستن مي تونن به ايدي بنده پيام بدن.
@Raminmousa
@Paper4money
@Machine_learn
1,732
Machine learning books and papers

03 Nov, 09:24

Data Pipelines with Apache Airflow

📘 book

@Machine_learn
1,483
Machine learning books and papers

01 Nov, 17:07

📑A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models



📎 Study the paper

@Machine_learn
1,882
Machine learning books and papers

01 Nov, 14:58

Ms - SmolLM2 1.7B - beats Qwen 2.5 1.5B & Llama 3.21B, Apache 2.0 licensed, trained on 11 Trillion tokens 🔥

> 135M, 360M, 1.7B parameter model
> Trained on FineWeb-Edu, DCLM, The Stack, along w/ new mathematics and coding datasets
> Specialises in Text rewriting, Summarization & Function Calling
> Integrated with transformers & model on the hub!

You can run the 1.7B in less than 2GB VRAM on a Q4 👑

Fine-tune, run inference, test, train, repeat - intelligence is just 5 lines of code away!

https://huggingface.co/collections/HuggingFaceTB/smollm2-6723884218bcda64b34d7db9

@Machine_learn
1,810
Machine learning books and papers

01 Nov, 06:29

💠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

جايگاه ٢ و ٤ اين مقاله رو نياز داريم.
دوستاني كه مايل به شركت هستن مي تونن به ايدي بنده پيام بدن.
@Raminmousa
@Paper4money
@Machine_learn
1,694
Machine learning books and papers

31 Oct, 10:55

SAM2Long: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree

🖥 Github: https://github.com/mark12ding/sam2long

📕 Paper: https://arxiv.org/abs/2410.16268v1

🤗 HF: https://huggingface.co/papers/2410.16268

@Machine_learn
1,866
Machine learning books and papers

31 Oct, 10:52

Intermediate Python

📖 Book

@Machine_learn
1,504
Machine learning books and papers

31 Oct, 10:51

🌟 Aya Expanse


🟢Aya Expanse 32B
🟢Aya Expanse 8B


🟠Aya Expanse 32B-GGUF
🟠Aya Expanse 8B-GGUF

Expanse 8B Transformers :

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "CohereForAI/aya-expanse-8b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

# Format the message with the chat template
messages = [{"role": "user", "content": " %prompt% "}]
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>%prompt%<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>

gen_tokens = model.generate(
input_ids,
max_new_tokens=100,
do_sample=True,
temperature=0.3,
)

gen_text = tokenizer.decode(gen_tokens[0])
print(gen_text)




🟡GGUF 32B
🟡GGUF 8B
🟡Demo


@Machine_learn
1,212
Machine learning books and papers

31 Oct, 10:48

⚡️ Stable Diffusion 3.5 Large.

# install Diffusers
pip install -U diffusers


# Inference
import torch
from diffusers import StableDiffusion3Pipeline

pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3.5-large", torch_dtype=torch.bfloat16)
pipe = pipe.to("cuda")

image = pipe(
"A happy woman laying on a grass",
num_inference_steps=28,
guidance_scale=3.5,
).images[0]
image.save("woman.png")




🟡Arxiv



@Machine_learn
1,266
Machine learning books and papers

31 Oct, 08:55

با عرض سلام امروز اخرين وقت براي مشاركت در اين مقاله مي باشد...!
1,327
Machine learning books and papers

30 Oct, 13:11

Title:
Advanced Classification of Drug-Drug Interactions for Assessing Adverse Effect Risks of Fluvoxamine and Curcumin Using Deep Learning in COVID-19
———————————————————————
Keywords:
Drug–Drug Interactions; Deep Neural Network; Fluvoxamine; Curcumin; Machine Learning.
———————————————————————
Journal of Infrastructure, Policy and Development


نفر اول پرشده
نفر دوم و سوم و چهارم خالی هست.

مقاله در اخرین ریوایزد خود می باشد.

@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
594
Machine learning books and papers

30 Oct, 13:06

THINKING LLMS: GENERAL INSTRUCTION FOLLOWING WITH THOUGHT GENERATION

📚 Reed

@Machine_learn
1,674
Machine learning books and papers

30 Oct, 13:06

📕 Applied Causal #Inference Powered by #MachineLearning

📌Book

@Machine_learn
1,120
Machine learning books and papers

30 Oct, 07:00

با عرض سلام نيازمند co-author براي مقاله زیر هستيم.
Target Journal: International Journal of Media and Networks | Opast Publishing Group (opastpublishers.com)
if: 1.2
Paper link: A Survey of Generative Adversarial Network on Next Generation Network[v1] | Preprints.org

تغييرات كامل نسخه نهايي تا يك هفته اينده اعمال ميشه كسي از دوستان تمايل به همكاري داشت به ايدي بنده پيام بدن.

@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
1,272
Machine learning books and papers

29 Oct, 19:16

🌟 Zamba2-Instruct

В семействе 2 модели:

🟢Zamba2-1.2B-instruct;
🟠Zamba2-2.7B-instruct.



# Clone repo
git clone https://github.com/Zyphra/transformers_zamba2.git
cd transformers_zamba2

# Install the repository & accelerate:
pip install -e .
pip install accelerate

# Inference:
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba2-2.7B-instruct")
model = AutoModelForCausalLM.from_pretrained("Zyphra/Zamba2-2.7B-instruct", device_map="cuda", torch_dtype=torch.bfloat16)

user_turn_1 = "user_prompt1."
assistant_turn_1 = "assistant_prompt."
user_turn_2 = "user_prompt2."
sample = [{'role': 'user', 'content': user_turn_1}, {'role': 'assistant', 'content': assistant_turn_1}, {'role': 'user', 'content': user_turn_2}]
chat_sample = tokenizer.apply_chat_template(sample, tokenize=False)

input_ids = tokenizer(chat_sample, return_tensors='pt', add_special_tokens=False).to("cuda")
outputs = model.generate(**input_ids, max_new_tokens=150, return_dict_in_generate=False, output_scores=False, use_cache=True, num_beams=1, do_sample=False)
print((tokenizer.decode(outputs[0])))




🖥GitHub

https://t.me/deep_learning_proj
1,225
Machine learning books and papers

29 Oct, 19:14

An Infinite Descent into Pure Mathematics

📚 Book

@Machine_learn
1,346
Machine learning books and papers

27 Oct, 18:53

Tutorial on Diffusion Models for Imaging and Vision

📚 Book

@Machine_learn
1,818
Machine learning books and papers

27 Oct, 18:53

NotebookLlama: An Open Source version of NotebookLM

📚 Book

@Machine_learn
1,633
Machine learning books and papers

27 Oct, 18:52

The State of AI Report

📚 Report

@Machine_learn
1,653
Machine learning books and papers

24 Oct, 19:49

📑 A guide to RNA sequencing and functional analysis


📎 Study the paper

@Machine_learn
2,399
Machine learning books and papers

24 Oct, 19:47

💡 SAM2Long, a training-free enhancement to SAM 2 for long-term video segmentation


🟡Technical Report: https://huggingface.co/papers/2410.16268
🟡Github: https://github.com/Mark12Ding/SAM2Long
🟡Homepage: https://mark12ding.github.io/project/SAM2Long/



@Machine_learn
2,135
Machine learning books and papers

24 Oct, 06:55

فقط نفر ۲ و ۴ از این باقی مونده ....!
2,115
Machine learning books and papers

24 Oct, 04:19

private link:
https://t.me/+SP9l58Ta_zZmYmY0
1,987
Machine learning books and papers

23 Oct, 17:13

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

نفرات ٢ تا ٤ اين مقاله رو نياز داريم.
دوستاني كه مايل به شركت هستن مي تونن به ايدي بنده پيام بدن.
@Raminmousa
@Paper4money
@Machine_learn
2,013
Machine learning books and papers

23 Oct, 13:42

LLM Engineer's Handbook: Master the art of engineering Large Language Models from concept to production.

🖥 Github

@Machine_learn
1,912
Machine learning books and papers

22 Oct, 19:53

💡 Ultimate Guide to Fine-Tuning LLMs

📚 link

@Machine_learn
1,805
Machine learning books and papers

22 Oct, 07:47

Linear Algebra Done Right

📓 Book

@Machine_learn
2,004
Machine learning books and papers

21 Oct, 11:59

فقط نفر دوم از این مقاله مونده...!
2,043
Machine learning books and papers

21 Oct, 02:31

يكي از بهترين موضوعات در طبقه بندي متن؛ تحليل احساس چند دامنه اي مي باشد. براي اين منظور مدلي تحت عنوان
Title: TRCAPS: The Transformer-based Capsule Approach for Persian Multi-
Domain Sentiment Analysis
طراحي كرديم كه نتايج خيلي بهتري نسبت به IndCaps داشته است.
دوستاني كه نياز به مقاله تو حوزه NLP دارن مي تونن تا اخر اين هفته داخل اين مقاله شركت كنند.

ژورنال هدف Array elsevier مي باشد.

شركت كنندگان داخل اين مقاله نياز به انجام تسك هايي نيز مي باشند.

@Raminmousa
@Machine_learn
@Paper4money
1,999
Machine learning books and papers

20 Oct, 19:47

📄 Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade



📎 Study the paper

@Machine_learn
1,870
Machine learning books and papers

20 Oct, 19:31

🌟 Zamba2-Instruct

🟢Zamba2-1.2B-instruct;
🟠Zamba2-2.7B-instruct.


# Clone repo
git clone https://github.com/Zyphra/transformers_zamba2.git
cd transformers_zamba2

# Install the repository & accelerate:
pip install -e .
pip install accelerate

# Inference:
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba2-2.7B-instruct")
model = AutoModelForCausalLM.from_pretrained("Zyphra/Zamba2-2.7B-instruct", device_map="cuda", torch_dtype=torch.bfloat16)

user_turn_1 = "user_prompt1."
assistant_turn_1 = "assistant_prompt."
user_turn_2 = "user_prompt2."
sample = [{'role': 'user', 'content': user_turn_1}, {'role': 'assistant', 'content': assistant_turn_1}, {'role': 'user', 'content': user_turn_2}]
chat_sample = tokenizer.apply_chat_template(sample, tokenize=False)

input_ids = tokenizer(chat_sample, return_tensors='pt', add_special_tokens=False).to("cuda")
outputs = model.generate(**input_ids, max_new_tokens=150, return_dict_in_generate=False, output_scores=False, use_cache=True, num_beams=1, do_sample=False)
print((tokenizer.decode(outputs[0])))

🖥GitHub


@Machine_learn
1,764
Machine learning books and papers

20 Oct, 19:30

estimating body and hand motion from a pair of glasses 🤓

website: http://egoallo.github.io

code: http://github.com/brentyi/egoallo

@Machine_learn
1,640
Machine learning books and papers

19 Oct, 14:59

Prompt Engineering Techniques: Comprehensive Repository for Development and Implementation 🖋️

📓 Github

@Machine_learn
2,259
Machine learning books and papers

18 Oct, 20:09

🔥 NVIDIA silently release a Llama 3.1 70B fine-tune that outperforms
GPT-4o and Claude Sonnet 3.5

Llama 3.1 Nemotron 70B Instruct a further RLHFed model on
huggingface


https://huggingface.co/collections/nvidia/llama-31-nemotron-70b-670e93cd366feea16abc13d8
✅https://t.me/deep_learning_proj
2,164
Machine learning books and papers

18 Oct, 15:11

✔️ LVD-2M: A Long-take Video Dataset with Temporally Dense Captions

New pipeline for selecting high-quality long-take videos and generating temporally dense captions.

Dataset with four key features essential for training long video generation models: (1) long videos covering at least 10 seconds, (2) long-take videos without cuts, (3) large motion and diverse contents, and (4) temporally dense captions.

🖥 Github: https://github.com/silentview/lvd-2m

📕 Paper: https://arxiv.org/abs/2410.10816v1

🖥 Dataset: https://paperswithcode.com/dataset/howto100m

🔸@Machine_learn
2,083
Machine learning books and papers

18 Oct, 14:00

Algebraic topology for physicists

📓 Book

@Machine_learn
1,986
Machine learning books and papers

17 Oct, 18:53

📑 Nine quick tips for open meta-analyses


📎 Study the paper

✅@Machine_learn
2,205
Machine learning books and papers

17 Oct, 18:21

📃Network Modeling and Control of Dynamic Disease Pathways, Review and Perspectives


📎 Study the paper

@Machine_learn
2,124
Machine learning books and papers

17 Oct, 11:22

پروژه های بیشتر شبیه این ریپورت داخل این پک قرار داره. دوستانی که نیاز دارن می تونن به ایدی بنده مراجعه کنن.

@Raminmousa
1,569
Machine learning books and papers

17 Oct, 11:14

Thesis: Yolo object detection

این پروژه سال ۲۰۲۰ با یکی از دوستان انجام دادیم که هدف تشخیص وزن پل با استفاده از Yolo بود. جزئیات مدل یولو رو داخل این بررسی کردیم . برای دوستانی که می خوان بیشتر این مدل رو بررسی کنن می تونه مفید باشه.
@Machine_learn
1,578
Machine learning books and papers

17 Oct, 06:16

Neural Networks and Deep Learning

📓 book

@Machine_learn
1,563
Machine learning books and papers

16 Oct, 06:45

Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of Language Family Experts

💻 Github: https://github.com/freedomintelligence/apollomoe

🔖 Paper: https://arxiv.org/abs/2410.10626v1

🤗 Dataset: https://paperswithcode.com/dataset/mmlu

✅@Machine_learn
1,741
Machine learning books and papers

15 Oct, 09:48

با عرض سلام خيلي از دوستان در رابطه با طراحي صفر تا صد پروژه هاي ديپ از بنده سوال پرسيدن داخل پك زير ٣٦ پروژه رو با جزئيات شرح دادم:

1-Deep Learning Basic
-01_Introduction
--01_How_TensorFlow_Works
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
دوستاني كه نياز به اين پروژه ها دارن ميتونن با بنده در ارتباط باشن.
@Raminmousa
@Machine_learn
2,071
Machine learning books and papers

15 Oct, 08:34

Probability and Statistics The Science of Uncertainty

📖 book

✅@Machine_learn
1,752
Machine learning books and papers

14 Oct, 18:44

UC Berkeley's "Machine Learning" lecture notes

📓 Book

✅@Machine_learn
2,052
Machine learning books and papers

14 Oct, 18:42

📃Fake news detection: A survey of graph neural network methods

📎 Study paper


@Machine_learn
1,747
Machine learning books and papers

13 Oct, 19:47

Artificial Intelligence A Modern Approach

📚 Book

@Machine_learn
2,125
Machine learning books and papers

13 Oct, 06:24

Generalizable and Animatable Gaussian Head Avatar

🖥 Github: https://github.com/xg-chu/gagavatar

📕 Paper: https://arxiv.org/abs/2410.07971v1

@Machine_learn
2,161
Machine learning books and papers

12 Oct, 11:28

Financial Machine Learning

📓 book

@Machine_learn
2,173
Machine learning books and papers

11 Oct, 14:41

Crawl 4 AI

Crawl4AI: Open-source LLM Friendly Web Crawler & Scrapper

Creator: UncleCode
Stars ⭐️: 8.6k
Forked By: 627
https://github.com/unclecode/crawl4ai

✅https://t.me/deep_learning_proj
2,329
Machine learning books and papers

11 Oct, 14:40

Deep Learning and Computational Physics - Lecture Notes, University of South California

📓 book

@Machine_learn
1,993
Machine learning books and papers

10 Oct, 05:55

Mathematical theory of deep learning

📚 Book

✅@Machine_learn
2,277

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