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نشریه مد نظر : Nature
@Raminmousa
نشریه مد نظر : Nature
@Raminmousa
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🛹 RollingDepth: Video Depth without Video Models
🔗 Discover More:
* Source Code: GitHub
* Paper Page: RollingDepth
* Try Demo: Try it here
* Paper Page: RollingDepth
@Machine_learn
🔗 Discover More:
* Source Code: GitHub
* Paper Page: RollingDepth
* Try Demo: Try it here
* Paper Page: RollingDepth
@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
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
👍1
OminiControl: Minimal and Universal Control for Diffusion Transformer
🔗 Discover More:
* Source Code: GitHub
* Try Demo: Try it here
* Paper Page: Read Paper
@Machine_learn
🔗 Discover More:
* Source Code: GitHub
* Try Demo: Try it here
* Paper Page: Read Paper
@Machine_learn
❤1
Forwarded from Github LLMs
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📑Drug Discovery in the Age of Artificial Intelligence: Transformative Target-Based Approaches
📎 Study the paper
@Machine_learn
📎 Study the paper
@Machine_learn
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📚Book Chapter:
Recent Advances in Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences
📎 Study
@Machine_learn
Recent Advances in Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences
📎 Study
@Machine_learn
👍1
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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
@Machine_learn
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NVIDIA BioNeMo2 Framework is a set of tools, libraries, and models for computational drug discovery and design.
@Machine_learn
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polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics
https://www.nature.com/articles/s41467-023-39868-6.pdf
@Machine_learn
https://www.nature.com/articles/s41467-023-39868-6.pdf
@Machine_learn
👍1🔥1
NPGPT: Natural Product-Like Compound Generation with GPT-based Chemical Language
Models
https://arxiv.org/pdf/2411.12886
@Machine_learn
Models
https://arxiv.org/pdf/2411.12886
@Machine_learn