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🌠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
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👍53
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
6👍3
💊 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
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
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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
👍72
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
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
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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
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
👍9
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
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2025/07/10 02:49:18
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