23171
Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
🌟 BioNeMo: A Framework for Developing AI Models for Drug Design.
NVIDIA BioNeMo2 Framework is a set of tools, libraries, and models for computational drug discovery and design.
▶️ Pre-trained models:
🟢 ESM-2 is a pre-trained bidirectional encoder (BERT-like) for amino acid sequences. BioNeMo2 includes checkpoints with parameters 650M and 3B;
🟢 Geneformer is a tabular scoring model that generates a dense representation of a cell's scRNA by examining co-expression patterns in individual cells.
▶️ Datasets:
🟠 CELLxGENE is a collection of publicly available single-cell datasets collected by the CZI (Chan Zuckerberg Initiative) with a total volume of 24 million cells;
🟠 UniProt is a database of clustered sets of protein sequences from UniProtKB, created on the basis of translated genomic data.
🟡 Project page
🟡 Documentation
🖥 GitHub
@Machine_learn
🌟 D-FINE:
D-FINE
# Create env via conda
conda create -n dfine python=3.11.9
conda activate dfine
# Install requirements for inference
pip install -r tools/inference/requirements.txt
# Install ONNX
pip install onnx onnxsim
# Choose a model
export model=l # s, m, x
# Inference
python tools/inference/onnx_inf.py --onnx model.onnx --input image.jpg # video.mp4
📚Book Chapter:
Recent Advances in Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences
📎 Study
@Machine_learn
🎓Ensemble approaches for Link Prediction
📎 Study thesis
@Machine_learn
🌟 GRIN MoE: Mixture-of-Experts от Microsoft.
🟢total parameters: 16x3.8B;
🟢active parameters: 6.6B;
🟢context length: 4096;
🟢number of embeddings 4096;
🟢number of layers: 32;
✅/channel/deep_learning_proj
🟡Arxiv
🟡Demo
🖥Github
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
/channel/+SP9l58Ta_zZmYmY0
Reinforcement Learning: An Overview
📕 Book
@Machine_learn
Hands-On Large Language Models
📚 Github
@Machine_learn
🛹 RollingDepth: Video Depth without Video Models
🔗 Discover More:
* Source Code: GitHub
* Paper Page: RollingDepth
* Try Demo: Try it here
* Paper Page: RollingDepth
@Machine_learn
با عرض سلام نفر سوم از مقاله مروری بالا رو نیاز داریم. با قبولی شرایط کسی نیاز داشت به بنده اطلاع بده.
نشریه مد نظر : Nature
@Raminmousa
Primers • Overview of Large Language Models
📖 Link
@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
/channel/+SP9l58Ta_zZmYmY0
📃Graph Neural Networks: A Bibliometric Mapping of the Research Landscape and Applications
📎 Study paper
@Machine_learn
Data Structures and Information Retrieval in Python
📓 link
@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
/channel/+SP9l58Ta_zZmYmY0
با عرض سلام در راستاي ادامه تحقيقات مشترك سعي داريم از ١ ام دي ماه 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 تا ١ دي 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 پنجم 500 دلار
🔺نفر ششم 400 دلار
@Raminmousa
@Machine_learn
/channel/+SP9l58Ta_zZmYmY0
با عرض سلام نفر ۳ از مقاله زیر رو نیاز داریم.
Title: hybrid deep learnings and machine learning frameworks
for air quality prediction
during the COVID‑19 pandemic
journal: https://www.sciencedirect.com/journal/expert-systems-with-applications
if:7.5
در این مقاله تاثیر ۲۶ مدل ansemble و ترکیبی رو برای پیش بینی کیفیت هوا در بازه ۱ روزه ۳ روزه و ۷ روزه بررسی کردیم. جهت شرکت در این مقاله به ایدی بنده پیام بدین.
@Raminmousa
@Machine_learn
/channel/+SP9l58Ta_zZmYmY0
Fine Tuning LLMs with Hugging Face LLMs Code
/channel/deep_learning_proj
📑Drug Discovery in the Age of Artificial Intelligence: Transformative Target-Based Approaches
📎 Study the paper
@Machine_learn
با عرض سلام در راستاي ادامه تحقيقات مشترك سعي داريم از ١ ام دي ماه 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 تا ١ دي 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 چهارم 600 دلار
🔺نفر پنجم 500 دلار
@Raminmousa
@Machine_learn
/channel/+SP9l58Ta_zZmYmY0
OminiControl: Minimal and Universal Control for Diffusion Transformer
🔗 Discover More:
* Source Code: GitHub
* Try Demo: Try it here
* Paper Page: Read Paper
@Machine_learn
با عرض سلام دو پکیچ یادگیری ماشین و یادگیری عمیق را برای دوستانی که می خواهند تا فرداشب با تخفیف ۵۰٪ مجدد قرار دادیم این تخفیف اخرین سری از تخفیف های این دو پکیچ می باشد
1: introduction to machine learning
2: Regression (linear and non-linear)
3: Tensorflow introduction
4: Tensorflow computaion graph
5: Tensorflow optimizer and loss function
6: Tensorflow linear and non linear regression
7: logistic regression
8: Tensorflow regression
___________
9: introduction to traditional machine learning
*10: knn and desicion tree
*11: desicion tree and Naive bayes
*12: desicion tree, knn, Naive bayes implementation
*13: k-means
*14: Guassion Mixture Model(GMM)
*15: implementation K-means and GMM
_
16: introduction to Artificial Neural Network
17: Multi-level Neural Network
18: Introduction to Convolution Neural Network
19: Tensorflow Multi-level Neural Network
20:Tensorflow CNN
21:CNN image clasaification
22: Cnn text clasaification
23: Recurrent Neural Network(RNN)
جهت تهیه می تونین به ایدی بنده مراجعه کنین
@Raminmousa
Neural Networks
@Machine_learn
Artificial Intelligence for Beginners - A Curriculum
📚 Course
@Machine_learn
با عرض سلام در راستاي ادامه تحقيقات مشترك سعي داريم از ١ ام دي ماه روي حوزه ي LLM مدل ها كار كنيم. حدودا ٤ نفر براي كار زير 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 تا ١ دي 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 ٣٠ ميليون
🔹نفر چهارم ٢٥ ميليون
🔺نفر پنجم ٢٠ ميليون
🔹نفر سوم ١٥ ميليون ث
نفرات اول و دوم: رامین موسی و سروش سرابی.
@Raminmousa
@Machine_learn
/channel/+SP9l58Ta_zZmYmY0
با عرض سلام اين مقاله اين هفته سابميت ميشه...!
Читать полностью…
Calculus 1 for Honours Mathematics
🔗 Book
@Machine_learn
Harvard's "Advanced Complex Analysis"
📓Course
@Machine_learn
🌟 OmniParser
🟡Arxiv
🖥Github
@Machine_learn
🌟 INTELLECT-1: Release of the first decentralized learning model.
PRIME Intellect has published INTELLECT-1 ( Instruct + Base ), the first 10 billion parameter language model collaboratively trained in 50 days by 30 participants worldwide.
PRIME Intellect used its own PRIME platform, designed to address the main problems of decentralized learning: network unreliability and dynamic management of computing nodes.
The platform utilized a network of 112 H100 GPUs across 3 continents and achieved a compute utilization rate of 96% under optimal conditions.
The training corpus consisted of 1 trillion public dataset tokens with the following percentage distribution: 55% fineweb-edu, 10% fineweb, 20% Stack V1, 10% dclm-baseline, 5% open-web-math.
▶️ Technical specifications:
🟢 Parameters: 10B;
🟢 Layers: 42;
🟢 Attention Heads: 32;
🟢 Hidden Size: 4096;
🟢 Context Length: 8192;
🟢 Vocabulary Size: 128256.
INTELLECT-1 achieved 37.5% accuracy on the MMLU test and 72.26% on HellaSwag, and outperformed several other open-source models on WinoGrande with a score of 65.82%.
While these figures lag slightly behind today's popular models, the results of the experiment are a critical step toward democratizing AI development and preventing the consolidation of AI capabilities within a few organizations.
▶️ GGUF quantized versions of INTELLECT-1_Instruct in 3-bit (5.46 GB) to 8-bit (10.9 GB) bit depths from the LM Studio community.
▶️ Example of inference on Transformers:import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
torch.set_default_device("cuda")
model = AutoModelForCausalLM.from_pretrained("PrimeIntellect/INTELLECT-1")
tokenizer = AutoTokenizer.from_pretrained("PrimeIntellect/INTELLECT-1")
input_text = "%prompt%"
input_ids = tokenizer.encode(input_text, return_tensors="pt")
output_ids = model.generate(input_ids, max_length=50, num_return_sequences=1)
output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(output_text)
📌 Licensing: Apache 2.0 License.
🟡 Article
🟡 HF Model Kit
🟡 Set of GGUF versions
🟡 Technical report
🟡 Demo
🖥 GitHub
@Machine_learn