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🖼 ImageDream: Image-Prompt Multi-view Diffusion for 3D Generation
🖥 GitHub: https://github.com/bytedance/ImageDream
📚 Paper: https://arxiv.org/abs/2312.02201
🥩 Demo: https://github.com/bytedance/ImageDream/blob/main
@Machine_learn
🖥 GitHub: https://github.com/bytedance/ImageDream
📚 Paper: https://arxiv.org/abs/2312.02201
🥩 Demo: https://github.com/bytedance/ImageDream/blob/main
@Machine_learn
SAM-Med2D ➕
SAM-Med2D, the most comprehensive studies on applying SAM to medical 2D images.
🖥 Github: https://github.com/uni-medical/sam-med2d
🖥 Colab: https://colab.research.google.com/github/uni-medical/SAM-Med2D/blob/main/predictor_example.ipynb
📕 Paper: https://arxiv.org/abs/2308.16184
⭐️ Dataset: https://paperswithcode.com/dataset/sa-1b
@Machine_learn
SAM-Med2D, the most comprehensive studies on applying SAM to medical 2D images.
🖥 Github: https://github.com/uni-medical/sam-med2d
🖥 Colab: https://colab.research.google.com/github/uni-medical/SAM-Med2D/blob/main/predictor_example.ipynb
📕 Paper: https://arxiv.org/abs/2308.16184
⭐️ Dataset: https://paperswithcode.com/dataset/sa-1b
@Machine_learn
Machine_Learning_For_Financial_Risk_Management_With_Python_Algorithms.pdf
3.6 MB
Book: Machine Learning for Financial
Risk Management with Python
Authors: Abdullah Karasan
ISBN: 978-1-492-08518-8
year: 2022
pages: 194
Tags: #Machine_learning #financial
@Machine_learn
Risk Management with Python
Authors: Abdullah Karasan
ISBN: 978-1-492-08518-8
year: 2022
pages: 194
Tags: #Machine_learning #financial
@Machine_learn
Forwarded from Machine learning books and papers
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
GraphEmb
🖥 Github: https://github.com/ubioinformat/graphemb
📕 Paper: https://arxiv.org/pdf/2311.12670v1.pdf
✨ Tasks: https://paperswithcode.com/task/benchmarking
@Machine_learn
🖥 Github: https://github.com/ubioinformat/graphemb
📕 Paper: https://arxiv.org/pdf/2311.12670v1.pdf
✨ Tasks: https://paperswithcode.com/task/benchmarking
@Machine_learn
2306.08302.pdf
3.2 MB
paper: Unifying Large Language Models and Knowledge Graphs: A Roadmap
index: Natural Language Processing, Large Language Models, Generative Pre-Training, Knowledge Graphs, Roadmap, Bidirectional Reasoning.
@Machine_learn
index: Natural Language Processing, Large Language Models, Generative Pre-Training, Knowledge Graphs, Roadmap, Bidirectional Reasoning.
@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
Machine learning books and papers pinned «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…»
CreativeSynth: Creative Blending and Synthesis of Visual Arts based on Multimodal Diffusion
🖥 Github: https://github.com/haha-lisa/creativesynth
📕 Paper: https://arxiv.org/pdf/2401.14066v1.pdf
✨ Tasks: https://paperswithcode.com/task/image-generation
@Machine_learn
🖥 Github: https://github.com/haha-lisa/creativesynth
📕 Paper: https://arxiv.org/pdf/2401.14066v1.pdf
✨ Tasks: https://paperswithcode.com/task/image-generation
@Machine_learn
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🥳FreeInit with AnimateDiff Gradio Colab
🖥 colab: https://github.com/camenduru/FreeInit-colab
🔮page: https://tianxingwu.github.io/pages/FreeInit/
📚paper: https://arxiv.org/abs/2312.07537
🥩code: https://github.com/TianxingWu/FreeInit
@Machie_learn
🖥 colab: https://github.com/camenduru/FreeInit-colab
🔮page: https://tianxingwu.github.io/pages/FreeInit/
📚paper: https://arxiv.org/abs/2312.07537
🥩code: https://github.com/TianxingWu/FreeInit
@Machie_learn
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🔍 FIND: Interface Foundation Models' Embeddings
🖥 Code: https://github.com/UX-Decoder/FIND
🎓 Demo: http://find.xyzou.net/
🔮 Project Page: https://x-decoder-vl.github.io
🥩 Demo: http://find.xyzou.net
📚 ArXiv: https://arxiv.org/pdf/2312.07532.pdf
@Machine_learn
🖥 Code: https://github.com/UX-Decoder/FIND
🎓 Demo: http://find.xyzou.net/
🔮 Project Page: https://x-decoder-vl.github.io
🥩 Demo: http://find.xyzou.net
📚 ArXiv: https://arxiv.org/pdf/2312.07532.pdf
@Machine_learn
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pix2gestalt: Amodal Segmentation by Synthesizing Wholes
🖥 Github: https://github.com/cvlab-columbia/pix2gestalt
📕 Paper: https://arxiv.org/pdf/2401.14398v1.pdf
✨ Tasks: https://paperswithcode.com/task/3d-reconstruction
@Machine_learn
🖥 Github: https://github.com/cvlab-columbia/pix2gestalt
📕 Paper: https://arxiv.org/pdf/2401.14398v1.pdf
✨ Tasks: https://paperswithcode.com/task/3d-reconstruction
@Machine_learn
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🌪 Can machine learning predict chaos?
https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.5.043252
@Machine_learn
https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.5.043252
@Machine_learn
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🌠AnyDoor: Zero-shot Object-level Image Customization
🖥 Code: https://github.com/damo-vilab/AnyDoor
🎓 HF: https://huggingface.co/spaces/xichenhku/AnyDoor-online
🔮 Project Page: https://damo-vilab.github.io/AnyDoor-Page/
📚 ArXiv: https://arxiv.org/abs/2307.09481
@Machine_learn
pip install git+https://github.com/cocodataset/panopticapi.git
pip install pycocotools -i https://pypi.douban.com/simple
pip install lvis
🖥 Code: https://github.com/damo-vilab/AnyDoor
🎓 HF: https://huggingface.co/spaces/xichenhku/AnyDoor-online
🔮 Project Page: https://damo-vilab.github.io/AnyDoor-Page/
📚 ArXiv: https://arxiv.org/abs/2307.09481
@Machine_learn
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🌹4DGen: Grounded 4D Content Generation with Spatial-temporal Consistency
🖥 Code: https://github.com/VITA-Group/4DGen
🔮 Project: https://vita-group.github.io/4DGen/
📚 ArXiv: https://arxiv.org/abs/2305.06456
@Machine_learn
🖥 Code: https://github.com/VITA-Group/4DGen
🔮 Project: https://vita-group.github.io/4DGen/
📚 ArXiv: https://arxiv.org/abs/2305.06456
@Machine_learn
Vaccine: Perturbation-aware Alignment for Large Language Model
🖥 Github: https://github.com/git-disl/vaccineT
📕 Paper: https://arxiv.org/pdf/2402.01109v1.pdf
🔥Datasets: https://paperswithcode.com/dataset/sst
✨ Tasks: https://paperswithcode.com/task/language-modelling
@Machine_learn
🖥 Github: https://github.com/git-disl/vaccineT
📕 Paper: https://arxiv.org/pdf/2402.01109v1.pdf
🔥Datasets: https://paperswithcode.com/dataset/sst
✨ Tasks: https://paperswithcode.com/task/language-modelling
@Machine_learn
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🔥Physics-based Text-to-Motion🔥
🖥 Code: github.com/jiawei-ren/insactor
📚 Paper: arxiv.org/abs/2312.17135
⚡️ Project: https://jiawei-ren.github.io/projects/insactor/
@Machine_learn
🖥 Code: github.com/jiawei-ren/insactor
📚 Paper: arxiv.org/abs/2312.17135
⚡️ Project: https://jiawei-ren.github.io/projects/insactor/
@Machine_learn
OReilly.Training.Data.for.Machine.Learning.pdf
21.3 MB
Book: 📚Training Data for Machine Learning: Human Supervision from Annotation to Data Science (2023)
Authors: Anthony Sarkis
ISBN: null
year: 2023
pages: 332
Tags: #Machine_learning#Data
@Machine_learn
Authors: Anthony Sarkis
ISBN: null
year: 2023
pages: 332
Tags: #Machine_learning#Data
@Machine_learn
💊 AMIE: A research AI system for diagnostic medical reasoning and conversations
💡 Blog: https://blog.research.google/2024/01/amie-research-ai-system-for-diagnostic_12.html
📚 Paper: https://arxiv.org/abs/2401.05654
@Machine_learn
💡 Blog: https://blog.research.google/2024/01/amie-research-ai-system-for-diagnostic_12.html
📚 Paper: https://arxiv.org/abs/2401.05654
@Machine_learn