🌠AnyDoor: Zero-shot Object-level Image Customization

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

🌹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

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

🔥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

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

💊 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

📷 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

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

⭐️ 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

⚡️ 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

EasyKV

🖥 Github: https://github.com/drsy/easykv

📕 Paper: https://arxiv.org/pdf/2402.06262v1.pdf

🔥Datasets: https://paperswithcode.com/dataset/webtext

@Machine_learn

Book: 📚Successful #AlgorithmicTrading
Authors: By Michael L. Halls-Moore
ISBN: Null
year: 2023
pages: 208
Tags: #Machine_learning# Trading
@Machine_learn

امشب اخرين تخفيف از اين پك هاي يادگيري مي باشد....!

@Raminmousa

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

Book: 📚An introduction to graph theory
Authors: Darij Grinberg
ISBN: Null
year: 2023
pages: 442
Tags: #Graph
@Machine_learn

⚡️ 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

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

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