This media is not supported in your browser
VIEW IN TELEGRAM
🧩 Building LEGO for 3D Reconstruction on Mobile Devices
A novel data capturing and 3D annotation pipeline in MobileBrick without relying on expensive 3D scanners.
🖥 Github: https://github.com/ActiveVisionLab/MobileBrick
⏩ Paper: https://arxiv.org/abs/2303.01932
⭐️ Dataset: http://www.robots.ox.ac.uk/~victor/data/MobileBrick/MobileBrick_Mar23.zip
💨 Project: https://code.active.vision/MobileBrick/
https://news.1rj.ru/str/DataScienceT
A novel data capturing and 3D annotation pipeline in MobileBrick without relying on expensive 3D scanners.
🖥 Github: https://github.com/ActiveVisionLab/MobileBrick
⏩ Paper: https://arxiv.org/abs/2303.01932
⭐️ Dataset: http://www.robots.ox.ac.uk/~victor/data/MobileBrick/MobileBrick_Mar23.zip
💨 Project: https://code.active.vision/MobileBrick/
https://news.1rj.ru/str/DataScienceT
❤5👍2
Ultra fast ControlNet with 🧨 Diffusers
ControlNet provides a minimal interface allowing users to customize the generation process up to a great extent.
🤗 Hugging face blog: https://huggingface.co/blog/controlnet
🖥 Colab: https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/controlnet.ipynb
🖥 Github: https://github.com/lllyasviel/ControlNet
⏩ Paprer: https://arxiv.org/abs/2302.05543
https://news.1rj.ru/str/DataScienceT
ControlNet provides a minimal interface allowing users to customize the generation process up to a great extent.
🤗 Hugging face blog: https://huggingface.co/blog/controlnet
🖥 Colab: https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/controlnet.ipynb
🖥 Github: https://github.com/lllyasviel/ControlNet
⏩ Paprer: https://arxiv.org/abs/2302.05543
https://news.1rj.ru/str/DataScienceT
❤🔥6❤1
⭐️ SplineCam: Exact Visualization and Characterization of Deep Network Geometry and Decision Boundaries, CVPR 2023
Exact method for computing partitions of a Deep Neural Network
🖥 Github: http://github.com/AhmedImtiazPrio/SplineCAM
🖥 Colab: https://bit.ly/splinecam-demo
⏩ Paper: http://arxiv.org/pdf/2302.12828.pdf
⭐️ Project: http://imtiazhumayun.github.io/splinecam
https://news.1rj.ru/str/DataScienceT
Exact method for computing partitions of a Deep Neural Network
🖥 Github: http://github.com/AhmedImtiazPrio/SplineCAM
🖥 Colab: https://bit.ly/splinecam-demo
⏩ Paper: http://arxiv.org/pdf/2302.12828.pdf
⭐️ Project: http://imtiazhumayun.github.io/splinecam
https://news.1rj.ru/str/DataScienceT
❤🔥6
Preference Transformer: Modeling Human Preferences using Transformers for RL (ICLR 2023)
🖥 Github: https://github.com/csmile-1006/preferencetransformer
⏩ Paper: https://arxiv.org/abs/2303.00957v1
https://news.1rj.ru/str/DataScienceT
🖥 Github: https://github.com/csmile-1006/preferencetransformer
⏩ Paper: https://arxiv.org/abs/2303.00957v1
https://news.1rj.ru/str/DataScienceT
❤🔥4❤2👍1
Handwritten Digit Recognition with LeNet5 Model in PyTorch
by Adrian Tam on March 8, 2023 in Deep Learning with PyTorch
🔗: https://machinelearningmastery.com/handwritten-digit-recognition-with-lenet5-model-in-pytorch
https://news.1rj.ru/str/DataScienceT
by Adrian Tam on March 8, 2023 in Deep Learning with PyTorch
🔗: https://machinelearningmastery.com/handwritten-digit-recognition-with-lenet5-model-in-pytorch
https://news.1rj.ru/str/DataScienceT
❤🔥5❤1
💬 GLIGEN: Open-Set Grounded Text-to-Image Generation
GLIGEN’s zero-shot performance on COCO and LVIS outperforms that of existing supervised layout-to-image baselines by a large margin. Code comming soon.
⭐️ Project: https://gligen.github.io/
⭐️ Demo: https://aka.ms/gligen
✅️ Paper: https://arxiv.org/abs/2301.07093
🖥 Github: https://github.com/gligen/GLIGEN
https://news.1rj.ru/str/DataScienceT
GLIGEN’s zero-shot performance on COCO and LVIS outperforms that of existing supervised layout-to-image baselines by a large margin. Code comming soon.
⭐️ Project: https://gligen.github.io/
⭐️ Demo: https://aka.ms/gligen
✅️ Paper: https://arxiv.org/abs/2301.07093
🖥 Github: https://github.com/gligen/GLIGEN
https://news.1rj.ru/str/DataScienceT
❤🔥4
🔥 MIT Introduction to Deep Learning
2023 Program has started!
🔗 Site: http://introtodeeplearning.com/
Link course: https://www.youtube.com/playlist?app=desktop&list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI
https://news.1rj.ru/str/DataScienceT
2023 Program has started!
🔗 Site: http://introtodeeplearning.com/
Link course: https://www.youtube.com/playlist?app=desktop&list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI
https://news.1rj.ru/str/DataScienceT
❤🔥3
Pandas for Data Science
Learning Path ⋅ Skills: Pandas, Data Science, Data Visualization
https://realpython.com/learning-paths/pandas-data-science/
https://news.1rj.ru/str/DataScienceT
Learning Path ⋅ Skills: Pandas, Data Science, Data Visualization
https://realpython.com/learning-paths/pandas-data-science/
https://news.1rj.ru/str/DataScienceT
❤🔥5
Openicl
New open-source toolkit for ICL and LLM evaluation.
⏩ Paper: https://arxiv.org/abs/2303.02913
⭐️ Dataset: https://paperswithcode.com/dataset/gsm8k
💨 Docs: https://github.com/shark-nlp/openicl#docs
⏩ Examples: https://github.com/Shark-NLP/OpenICL/tree/main/examples
https://news.1rj.ru/str/DataScienceT
New open-source toolkit for ICL and LLM evaluation.
pip install openicl🖥 Github: https://github.com/shark-nlp/openicl
⏩ Paper: https://arxiv.org/abs/2303.02913
⭐️ Dataset: https://paperswithcode.com/dataset/gsm8k
💨 Docs: https://github.com/shark-nlp/openicl#docs
⏩ Examples: https://github.com/Shark-NLP/OpenICL/tree/main/examples
https://news.1rj.ru/str/DataScienceT
❤🔥3❤2
An important collection of the 15 best machine learning cheat sheets.
مجموعة مهمة الافضل ١٥ ورقة غش في مجال التعلم الآلي.
1- Supervised Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf
2- Unsupervised Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf
3- Deep Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf
4- Machine Learning Tips and Tricks
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf
5- Probabilities and Statistics
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf
6- Comprehensive Stanford Master Cheat Sheet
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf
7- Linear Algebra and Calculus
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf
8- Data Science Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf
9- Keras Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf
10- Deep Learning with Keras Cheat Sheet
https://github.com/rstudio/cheatsheets/raw/master/keras.pdf
11- Visual Guide to Neural Network Infrastructures
http://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png
12- Skicit-Learn Python Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf
13- Scikit-learn Cheat Sheet: Choosing the Right Estimator
https://scikit-learn.org/stable/tutorial/machine_learning_map/
14- Tensorflow Cheat Sheet
https://github.com/kailashahirwar/cheatsheets-ai/blob/master/PDFs/Tensorflow.pdf
15- Machine Learning Test Cheat Sheet
https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/
✳️ ساهم بنمو مجتمعنا من خلال اضافة الاصدقاء او مشاركة المنشور.
مجموعة مهمة الافضل ١٥ ورقة غش في مجال التعلم الآلي.
1- Supervised Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf
2- Unsupervised Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf
3- Deep Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf
4- Machine Learning Tips and Tricks
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf
5- Probabilities and Statistics
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf
6- Comprehensive Stanford Master Cheat Sheet
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf
7- Linear Algebra and Calculus
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf
8- Data Science Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf
9- Keras Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf
10- Deep Learning with Keras Cheat Sheet
https://github.com/rstudio/cheatsheets/raw/master/keras.pdf
11- Visual Guide to Neural Network Infrastructures
http://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png
12- Skicit-Learn Python Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf
13- Scikit-learn Cheat Sheet: Choosing the Right Estimator
https://scikit-learn.org/stable/tutorial/machine_learning_map/
14- Tensorflow Cheat Sheet
https://github.com/kailashahirwar/cheatsheets-ai/blob/master/PDFs/Tensorflow.pdf
15- Machine Learning Test Cheat Sheet
https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/
✳️ ساهم بنمو مجتمعنا من خلال اضافة الاصدقاء او مشاركة المنشور.
GitHub
stanford-cs-229-machine-learning/en/cheatsheet-supervised-learning.pdf at master · afshinea/stanford-cs-229-machine-learning
VIP cheatsheets for Stanford's CS 229 Machine Learning - afshinea/stanford-cs-229-machine-learning
👍4❤🔥2
This media is not supported in your browser
VIEW IN TELEGRAM
X-Avatar: Expressive Human Avatars
🖥 Github: https://github.com/Skype-line/X-Avatar
⏩ Paper: https://arxiv.org/abs/2303.04805
💨 Dataset: https://github.com/Skype-line/X-Avatar/blob/main/xxx
⏩ Project: https://skype-line.github.io/projects/X-Avatar/
https://news.1rj.ru/str/DataScienceT
🖥 Github: https://github.com/Skype-line/X-Avatar
⏩ Paper: https://arxiv.org/abs/2303.04805
💨 Dataset: https://github.com/Skype-line/X-Avatar/blob/main/xxx
⏩ Project: https://skype-line.github.io/projects/X-Avatar/
https://news.1rj.ru/str/DataScienceT
❤🔥2
Datasets
Datasets collected for network science, deep learning and general machine learning research.
Github: https://github.com/benedekrozemberczki/datasets
Paper: https://arxiv.org/abs/2101.03091v1
Invite your friends 🌹🌹
@DataScience_Books
Datasets collected for network science, deep learning and general machine learning research.
Github: https://github.com/benedekrozemberczki/datasets
Paper: https://arxiv.org/abs/2101.03091v1
Invite your friends 🌹🌹
@DataScience_Books
👍4❤🔥3
Multivariate Probabilistic Time Series Forecasting with Informer
Efficient transformer-based model for LSTF.
Method introduces a Probabilistic Attention mechanism to select the “active” queries rather than the “lazy” queries and provides a sparse Transformer thus mitigating the quadratic compute and memory requirements of vanilla attention.
🤗Hugging face:
https://huggingface.co/blog/informer
⏩ Paper:
https://huggingface.co/docs/transformers/main/en/model_doc/informer
⭐️ Colab:
https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multivariate_informer.ipynb
💨 Dataset:
https://huggingface.co/docs/datasets/v2.7.0/en/package_reference/main_classes#datasets.Dataset.set_transform
https://news.1rj.ru/str/DataScienceT
Efficient transformer-based model for LSTF.
Method introduces a Probabilistic Attention mechanism to select the “active” queries rather than the “lazy” queries and provides a sparse Transformer thus mitigating the quadratic compute and memory requirements of vanilla attention.
🤗Hugging face:
https://huggingface.co/blog/informer
⏩ Paper:
https://huggingface.co/docs/transformers/main/en/model_doc/informer
⭐️ Colab:
https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multivariate_informer.ipynb
💨 Dataset:
https://huggingface.co/docs/datasets/v2.7.0/en/package_reference/main_classes#datasets.Dataset.set_transform
https://news.1rj.ru/str/DataScienceT
❤🔥2👍1