Graph Diffusion Policy Optimization
🖥 Github: https://github.com/sail-sg/gdpo
📕 Paper: https://arxiv.org/pdf/2402.16302v1.pdf
🔥Dataset: https://paperswithcode.com/dataset/zinc
✨ Tasks: https://paperswithcode.com/task/graph-generation
@Machine_learn
🖥 Github: https://github.com/sail-sg/gdpo
📕 Paper: https://arxiv.org/pdf/2402.16302v1.pdf
🔥Dataset: https://paperswithcode.com/dataset/zinc
✨ Tasks: https://paperswithcode.com/task/graph-generation
@Machine_learn
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DCVSMNet: Double Cost Volume Stereo Matching Network
🖥 Github: https://github.com/m2219/dcvsmnet
📕 Paper: https://arxiv.org/pdf/2402.16473v1.pdf
🔥Dataset: https://paperswithcode.com/dataset/kitti
✨ Tasks: https://paperswithcode.com/task/stereo-matching-1
@Machine_learn
🖥 Github: https://github.com/m2219/dcvsmnet
📕 Paper: https://arxiv.org/pdf/2402.16473v1.pdf
🔥Dataset: https://paperswithcode.com/dataset/kitti
✨ Tasks: https://paperswithcode.com/task/stereo-matching-1
@Machine_learn
👍7
Fundamentals of Data Science.pdf
12.4 MB
Book: 📚Fundamentals of Data Science Theory and Practice
Authors: Jugal K. Kalita Dhruba K. Bhattacharyya Swarup Roy
ISBN: 978-0-323-91778-0
year: 2023
pages: 336
Tags: #Data_science
@Machine_learn
Authors: Jugal K. Kalita Dhruba K. Bhattacharyya Swarup Roy
ISBN: 978-0-323-91778-0
year: 2023
pages: 336
Tags: #Data_science
@Machine_learn
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با عرض سلام
مقاله ي فوق به صورت كامل نوشته شده است. نيازمند شخصي هستيم كه بتونه اكسپت مقاله رو بگيره و هزينه هاي سرور رو پرداخت كنه(جايگاه ٢: co-author).
@Raminmousa
مقاله ي فوق به صورت كامل نوشته شده است. نيازمند شخصي هستيم كه بتونه اكسپت مقاله رو بگيره و هزينه هاي سرور رو پرداخت كنه(جايگاه ٢: co-author).
@Raminmousa
ZHEM: An Integrated Data Processing Framework for Pretraining Foundation Models
🖥 Github: https://github.com/emanual20/zhem
📕 Paper: https://arxiv.org/pdf/2402.16358v1.pdf
🔥Dataset: https://paperswithcode.com/dataset/wikitext-2
@Machine_learn
🖥 Github: https://github.com/emanual20/zhem
📕 Paper: https://arxiv.org/pdf/2402.16358v1.pdf
🔥Dataset: https://paperswithcode.com/dataset/wikitext-2
@Machine_learn
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🖼 Differential Diffusion: Giving Each Pixel Its Strength 🔥
▪code: github.com/exx8/differential-diffusion
▪page: differential-diffusion.github.io
▪paper: arxiv.org/abs/2306.00950
@Machine_learn
▪code: github.com/exx8/differential-diffusion
▪page: differential-diffusion.github.io
▪paper: arxiv.org/abs/2306.00950
@Machine_learn
👍4❤2🔥2
aipython.pdf
2.4 MB
Book: 📚Python code for Artificial Intelligence Foundations of Computational Agents
Authors: David L. Poole and Alan K. Mackworth
year: 2024
pages: 392
Tags: #Python
@Machine_learn
Authors: David L. Poole and Alan K. Mackworth
year: 2024
pages: 392
Tags: #Python
@Machine_learn
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⚡️ ResAdapter: Domain Consistent Resolution Adapter for Diffusion Models
▪page: https://res-adapter.github.io
▪paper: https://arxiv.org/abs/2403.02084
▪code: https://github.com/bytedance/res-adapter
@Machine_learn
▪page: https://res-adapter.github.io
▪paper: https://arxiv.org/abs/2403.02084
▪code: https://github.com/bytedance/res-adapter
@Machine_learn
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2206.13446.pdf
3 MB
Book: 📚Exercises in Machine Learning
Authors: Michael U. Gutmann
year: 2024
pages: 211
Tags: #ML
@Machine_learn
Authors: Michael U. Gutmann
year: 2024
pages: 211
Tags: #ML
@Machine_learn
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Arbitrary-Scale Point Cloud Upsampling by Voxel-Based Network with Latent Geometric-Consistent Learning
🖥 Github: https://github.com/hikvision-research/3dvision
📕 Paper: https://arxiv.org/abs/2403.05117v1
🔥Dataset: https://paperswithcode.com/dataset/scanobjectnn
@Machine_learn
🖥 Github: https://github.com/hikvision-research/3dvision
📕 Paper: https://arxiv.org/abs/2403.05117v1
🔥Dataset: https://paperswithcode.com/dataset/scanobjectnn
@Machine_learn
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Miguel_Morales_Grokking_Deep_Reinforcement_Learning_Manning_Publications.pdf
17.3 MB
Book: 📚grokking Deep Reinforcement Learning
Authors: Miguel Morales Foreword by Charles Isbell, Jr.
year: 2020
pages: 472
Tags: #RL #DRL
@Machine_learn
Authors: Miguel Morales Foreword by Charles Isbell, Jr.
year: 2020
pages: 472
Tags: #RL #DRL
@Machine_learn
❤8
ViT-CoMer: Vision Transformer with Convolutional Multi-scale Feature Interaction for Dense Predictions
🖥 Github: https://github.com/Traffic-X/ViT-CoMer
📕 Paper: https://arxiv.org/pdf/2403.07392.pdf
✨ Tasks: https://paperswithcode.com/task/object-detection
🔥Dataset: https://paperswithcode.com/dataset/coco
@Machine_learn
🖥 Github: https://github.com/Traffic-X/ViT-CoMer
📕 Paper: https://arxiv.org/pdf/2403.07392.pdf
✨ Tasks: https://paperswithcode.com/task/object-detection
🔥Dataset: https://paperswithcode.com/dataset/coco
@Machine_learn
👍6
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🏎 TripoSR: Fast 3D Object Reconstruction from a Single Image
▪page: https://tripo3d.ai
▪paper: https://drive.google.com/file/d/1LWlZPT2aASi9jHiGVhDSr4YCTANoFW5t/view
▪code: https://github.com/VAST-AI-Research/TripoSR
@Machine_learn
▪page: https://tripo3d.ai
▪paper: https://drive.google.com/file/d/1LWlZPT2aASi9jHiGVhDSr4YCTANoFW5t/view
▪code: https://github.com/VAST-AI-Research/TripoSR
@Machine_learn
❤5
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
👍2