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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
👍2
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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
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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
👍5❤3
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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
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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
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💊 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
TimesFM is a forecasting model, pre-trained on a large time-series corpus of 100 billion real world time-points
https://blog.research.google/2024/02/a-decoder-only-foundation-model-for.html
@Machine_learn
https://blog.research.google/2024/02/a-decoder-only-foundation-model-for.html
@Machine_learn
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📷 InstructIR: High-Quality Image Restoration Following Human Instructions
🖥 Code: https://github.com/mv-lab/InstructIR
🚀 Project: mv-lab.github.io/InstructIR/
🎮 Colab: https://colab.research.google.com/drive/1OrTvS-i6uLM2Y8kIkq8ZZRwEQxQFchfq
📚 Paper: https://arxiv.org/abs/2401.16468
@Machine_learn
🖥 Code: https://github.com/mv-lab/InstructIR
🚀 Project: mv-lab.github.io/InstructIR/
🎮 Colab: https://colab.research.google.com/drive/1OrTvS-i6uLM2Y8kIkq8ZZRwEQxQFchfq
📚 Paper: https://arxiv.org/abs/2401.16468
@Machine_learn
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SoftEDA: Rethinking Rule-Based Data Augmentation with Soft Labels
🖥 Github: https://github.com/IIPL-CAU/SoftEDA
📕 Paper: https://arxiv.org/pdf/2402.05591v1.pdf
🔥Datasets: https://paperswithcode.com/dataset/cola
@Machine_learn
🖥 Github: https://github.com/IIPL-CAU/SoftEDA
📕 Paper: https://arxiv.org/pdf/2402.05591v1.pdf
🔥Datasets: https://paperswithcode.com/dataset/cola
@Machine_learn
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⭐️ YOLO-World Real-Time Open-Vocabulary Object Detection
🖥 Github: https://github.com/AILab-CVC/YOLO-World
📚 Paper: https://arxiv.org/abs/2401.17270
⚡️Demo: https://www.yoloworld.cc
🤗Hf: https://huggingface.co/spaces/stevengrove/YOLO-World
@Machine_learn
🖥 Github: https://github.com/AILab-CVC/YOLO-World
📚 Paper: https://arxiv.org/abs/2401.17270
⚡️Demo: https://www.yoloworld.cc
🤗Hf: https://huggingface.co/spaces/stevengrove/YOLO-World
@Machine_learn
❤5👍3
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⚡️ LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation
🖥 Github: https://github.com/3DTopia/LGM
📚 Paper: https://arxiv.org/abs/2402.05054
🔗 Demo: https://huggingface.co/spaces/ashawkey/LGM
💻 Weights: https://huggingface.co/ashawkey/LGM
⏩ Project: https://me.kiui.moe/lgm/
@Machine_learn
🖥 Github: https://github.com/3DTopia/LGM
📚 Paper: https://arxiv.org/abs/2402.05054
🔗 Demo: https://huggingface.co/spaces/ashawkey/LGM
💻 Weights: https://huggingface.co/ashawkey/LGM
⏩ Project: https://me.kiui.moe/lgm/
@Machine_learn
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EasyKV
🖥 Github: https://github.com/drsy/easykv
📕 Paper: https://arxiv.org/pdf/2402.06262v1.pdf
🔥Datasets: https://paperswithcode.com/dataset/webtext
@Machine_learn
🖥 Github: https://github.com/drsy/easykv
📕 Paper: https://arxiv.org/pdf/2402.06262v1.pdf
🔥Datasets: https://paperswithcode.com/dataset/webtext
@Machine_learn
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Successful Algorithmic Trading (1).pdf
2.2 MB
Book: 📚Successful #AlgorithmicTrading
Authors: By Michael L. Halls-Moore
ISBN: Null
year: 2023
pages: 208
Tags: #Machine_learning# Trading
@Machine_learn
Authors: By Michael L. Halls-Moore
ISBN: Null
year: 2023
pages: 208
Tags: #Machine_learning# Trading
@Machine_learn
👍9
SQ-Transformer: Inducing Systematicity in Transformers by Attending to Structurally Quantized Embeddings
🖥 Github: https://github.com/jiangyctarheel/sq-transformer
📕 Paper: https://arxiv.org/pdf/2402.06492v1.pdf
🔥Datasets: https://paperswithcode.com/dataset/wmt-2014
@Machine_learn
🖥 Github: https://github.com/jiangyctarheel/sq-transformer
📕 Paper: https://arxiv.org/pdf/2402.06492v1.pdf
🔥Datasets: https://paperswithcode.com/dataset/wmt-2014
@Machine_learn
❤2
2308.04512.pdf
3.1 MB
Book: 📚An introduction to graph theory
Authors: Darij Grinberg
ISBN: Null
year: 2023
pages: 442
Tags: #Graph
@Machine_learn
Authors: Darij Grinberg
ISBN: Null
year: 2023
pages: 442
Tags: #Graph
@Machine_learn
👍9
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⚡️ V-JEPA: The next step toward Yann LeCun’s vision of advanced machine intelligence (AMI)
▪Github: https://github.com/facebookresearch/jepa
▪Paper: https://ai.meta.com/research/publications/revisiting-feature-prediction-for-learning-visual-representations-from-video/
▪Blog: https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/
@Machine_learn
▪Github: https://github.com/facebookresearch/jepa
▪Paper: https://ai.meta.com/research/publications/revisiting-feature-prediction-for-learning-visual-representations-from-video/
▪Blog: https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/
@Machine_learn
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MIM-Refiner
🖥 Github: https://github.com/ml-jku/MIM-Refiner
📕 Paper: https://arxiv.org/pdf/2402.10093v1.pdf
🔥Datasets: https://paperswithcode.com/dataset/imagenet
✨ Tasks: https://paperswithcode.com/task/image-clustering
@Machine_learn
🖥 Github: https://github.com/ml-jku/MIM-Refiner
📕 Paper: https://arxiv.org/pdf/2402.10093v1.pdf
🔥Datasets: https://paperswithcode.com/dataset/imagenet
✨ Tasks: https://paperswithcode.com/task/image-clustering
@Machine_learn
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با عرض سلام دو پکیچ یادگیری ماشین و یادگیری عمیق را برای دوستانی که می خواهند تا فرداشب با تخفیف ۵۰٪ مجدد قرار دادیم.
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
👍7