Machine learning books and papers

Towards System 2 Reasoning in LLMs

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

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📃 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)



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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

✨Data base normalization✨

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Machine learning books and papers pinned Deleted message

Tensors in computations

📕Book

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Automating the Search for Artificial Life with Foundation Models

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

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

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📽 Introduction to Network Analysis using NetworkX

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📃A Survey of Graph Neural Networks for Social Recommender Systems


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هزینه نهایی برای این کار رو به ۲۵ میلیون کاهش دادیم برای نفر ۵ ...!🔥

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

⚡️ 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


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Machine learning books and papers pinned Deleted message

🌟 🌟 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

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🔥 nn-zero-to-hero

🖥 Github

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امشب اخرین فرصت برای مشارکت در این مقاله هستش...!🔸🔸

Machine learning books and papers pinned Deleted message

Lecture notes: mathematics for artificial intelligence

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