Ultra fast ControlNet with ๐งจ Diffusers
ControlNet provides a minimal interface allowing users to customize the generation process up to a great extent.
๐ค Hugging face blog: https://huggingface.co/blog/controlnet
๐ฅ Colab: https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/controlnet.ipynb
๐ฅ Github: https://github.com/lllyasviel/ControlNet
โฉ Paprer: https://arxiv.org/abs/2302.05543
https://t.iss.one/DataScienceT
ControlNet provides a minimal interface allowing users to customize the generation process up to a great extent.
๐ค Hugging face blog: https://huggingface.co/blog/controlnet
๐ฅ Colab: https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/controlnet.ipynb
๐ฅ Github: https://github.com/lllyasviel/ControlNet
โฉ Paprer: https://arxiv.org/abs/2302.05543
https://t.iss.one/DataScienceT
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โญ๏ธ SplineCam: Exact Visualization and Characterization of Deep Network Geometry and Decision Boundaries, CVPR 2023
Exact method for computing partitions of a Deep Neural Network
๐ฅ Github: https://github.com/AhmedImtiazPrio/SplineCAM
๐ฅ Colab: https://bit.ly/splinecam-demo
โฉ Paper: https://arxiv.org/pdf/2302.12828.pdf
โญ๏ธ Project: https://imtiazhumayun.github.io/splinecam
https://t.iss.one/DataScienceT
Exact method for computing partitions of a Deep Neural Network
๐ฅ Github: https://github.com/AhmedImtiazPrio/SplineCAM
๐ฅ Colab: https://bit.ly/splinecam-demo
โฉ Paper: https://arxiv.org/pdf/2302.12828.pdf
โญ๏ธ Project: https://imtiazhumayun.github.io/splinecam
https://t.iss.one/DataScienceT
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Preference Transformer: Modeling Human Preferences using Transformers for RL (ICLR 2023)
๐ฅ Github: https://github.com/csmile-1006/preferencetransformer
โฉ Paper: https://arxiv.org/abs/2303.00957v1
https://t.iss.one/DataScienceT
๐ฅ Github: https://github.com/csmile-1006/preferencetransformer
โฉ Paper: https://arxiv.org/abs/2303.00957v1
https://t.iss.one/DataScienceT
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Handwritten Digit Recognition with LeNet5 Model in PyTorch
by Adrian Tam on March 8, 2023 in Deep Learning with PyTorch
๐: https://machinelearningmastery.com/handwritten-digit-recognition-with-lenet5-model-in-pytorch
https://t.iss.one/DataScienceT
by Adrian Tam on March 8, 2023 in Deep Learning with PyTorch
๐: https://machinelearningmastery.com/handwritten-digit-recognition-with-lenet5-model-in-pytorch
https://t.iss.one/DataScienceT
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๐ฌ GLIGEN: Open-Set Grounded Text-to-Image Generation
GLIGENโs zero-shot performance on COCO and LVIS outperforms that of existing supervised layout-to-image baselines by a large margin. Code comming soon.
โญ๏ธ Project: https://gligen.github.io/
โญ๏ธ Demo: https://aka.ms/gligen
โ ๏ธ Paper: https://arxiv.org/abs/2301.07093
๐ฅ Github: https://github.com/gligen/GLIGEN
https://t.iss.one/DataScienceT
GLIGENโs zero-shot performance on COCO and LVIS outperforms that of existing supervised layout-to-image baselines by a large margin. Code comming soon.
โญ๏ธ Project: https://gligen.github.io/
โญ๏ธ Demo: https://aka.ms/gligen
โ ๏ธ Paper: https://arxiv.org/abs/2301.07093
๐ฅ Github: https://github.com/gligen/GLIGEN
https://t.iss.one/DataScienceT
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๐ฅ MIT Introduction to Deep Learning
2023 Program has started!
๐ Site: https://introtodeeplearning.com/
Link course: https://www.youtube.com/playlist?app=desktop&list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI
https://t.iss.one/DataScienceT
2023 Program has started!
๐ Site: https://introtodeeplearning.com/
Link course: https://www.youtube.com/playlist?app=desktop&list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI
https://t.iss.one/DataScienceT
โคโ๐ฅ3
Pandas for Data Science
Learning Path โ Skills: Pandas, Data Science, Data Visualization
https://realpython.com/learning-paths/pandas-data-science/
https://t.iss.one/DataScienceT
Learning Path โ Skills: Pandas, Data Science, Data Visualization
https://realpython.com/learning-paths/pandas-data-science/
https://t.iss.one/DataScienceT
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Openicl
New open-source toolkit for ICL and LLM evaluation.
โฉ Paper: https://arxiv.org/abs/2303.02913
โญ๏ธ Dataset: https://paperswithcode.com/dataset/gsm8k
๐จ Docs: https://github.com/shark-nlp/openicl#docs
โฉ Examples: https://github.com/Shark-NLP/OpenICL/tree/main/examples
https://t.iss.one/DataScienceT
New open-source toolkit for ICL and LLM evaluation.
pip install openicl๐ฅ Github: https://github.com/shark-nlp/openicl
โฉ Paper: https://arxiv.org/abs/2303.02913
โญ๏ธ Dataset: https://paperswithcode.com/dataset/gsm8k
๐จ Docs: https://github.com/shark-nlp/openicl#docs
โฉ Examples: https://github.com/Shark-NLP/OpenICL/tree/main/examples
https://t.iss.one/DataScienceT
โคโ๐ฅ3โค2
An important collection of the 15 best machine learning cheat sheets.
ู ุฌู ูุนุฉ ู ูู ุฉ ุงูุงูุถู ูกูฅ ูุฑูุฉ ุบุด ูู ู ุฌุงู ุงูุชุนูู ุงูุขูู.
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
https://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/
โณ๏ธ ุณุงูู ุจูู ู ู ุฌุชู ุนูุง ู ู ุฎูุงู ุงุถุงูุฉ ุงูุงุตุฏูุงุก ุงู ู ุดุงุฑูุฉ ุงูู ูุดูุฑ.
ู ุฌู ูุนุฉ ู ูู ุฉ ุงูุงูุถู ูกูฅ ูุฑูุฉ ุบุด ูู ู ุฌุงู ุงูุชุนูู ุงูุขูู.
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
https://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/
โณ๏ธ ุณุงูู ุจูู ู ู ุฌุชู ุนูุง ู ู ุฎูุงู ุงุถุงูุฉ ุงูุงุตุฏูุงุก ุงู ู ุดุงุฑูุฉ ุงูู ูุดูุฑ.
GitHub
stanford-cs-229-machine-learning/en/cheatsheet-supervised-learning.pdf at master ยท afshinea/stanford-cs-229-machine-learning
VIP cheatsheets for Stanford's CS 229 Machine Learning - afshinea/stanford-cs-229-machine-learning
๐4โคโ๐ฅ2
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X-Avatar: Expressive Human Avatars
๐ฅ Github: https://github.com/Skype-line/X-Avatar
โฉ Paper: https://arxiv.org/abs/2303.04805
๐จ Dataset: https://github.com/Skype-line/X-Avatar/blob/main/xxx
โฉ Project: https://skype-line.github.io/projects/X-Avatar/
https://t.iss.one/DataScienceT
๐ฅ Github: https://github.com/Skype-line/X-Avatar
โฉ Paper: https://arxiv.org/abs/2303.04805
๐จ Dataset: https://github.com/Skype-line/X-Avatar/blob/main/xxx
โฉ Project: https://skype-line.github.io/projects/X-Avatar/
https://t.iss.one/DataScienceT
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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 ๐น๐น
@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 ๐น๐น
@DataScience_Books
๐4โคโ๐ฅ3
Multivariate Probabilistic Time Series Forecasting with Informer
Efficient transformer-based model for LSTF.
Method introduces a Probabilistic Attention mechanism to select the โactiveโ queries rather than the โlazyโ queries and provides a sparse Transformer thus mitigating the quadratic compute and memory requirements of vanilla attention.
๐คHugging face:
https://huggingface.co/blog/informer
โฉ Paper:
https://huggingface.co/docs/transformers/main/en/model_doc/informer
โญ๏ธ Colab:
https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multivariate_informer.ipynb
๐จ Dataset:
https://huggingface.co/docs/datasets/v2.7.0/en/package_reference/main_classes#datasets.Dataset.set_transform
https://t.iss.one/DataScienceT
Efficient transformer-based model for LSTF.
Method introduces a Probabilistic Attention mechanism to select the โactiveโ queries rather than the โlazyโ queries and provides a sparse Transformer thus mitigating the quadratic compute and memory requirements of vanilla attention.
๐คHugging face:
https://huggingface.co/blog/informer
โฉ Paper:
https://huggingface.co/docs/transformers/main/en/model_doc/informer
โญ๏ธ Colab:
https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multivariate_informer.ipynb
๐จ Dataset:
https://huggingface.co/docs/datasets/v2.7.0/en/package_reference/main_classes#datasets.Dataset.set_transform
https://t.iss.one/DataScienceT
โคโ๐ฅ2๐1
Linear Algebra in Python: Matrix Inverses and Least Squares
https://realpython.com/python-linear-algebra/
https://realpython.com/python-linear-algebra/
โคโ๐ฅ4๐1
GPT-4 Technical Report
Source code: https://github.com/openai/evals
Paper: https://cdn.openai.com/papers/gpt-4.pdf
Source code: https://github.com/openai/evals
Paper: https://cdn.openai.com/papers/gpt-4.pdf
โคโ๐ฅ4
Tuned Lens ๐
Simple interface training and evaluating tuned lenses. A tuned lens allows us to peak at the iterative computations a transformer uses to compute the next token.
๐ฅ Github: https://github.com/alignmentresearch/tuned-lens
โฉ Paper: https://arxiv.org/abs/2303.08112v1
โญ๏ธ Dataset: https://paperswithcode.com/dataset/the-pile
๐ฅ Colab: https://colab.research.google.com/github/AlignmentResearch/tuned-lens/blob/main/notebooks/interactive.ipynb
https://t.iss.one/DataScienceT
Simple interface training and evaluating tuned lenses. A tuned lens allows us to peak at the iterative computations a transformer uses to compute the next token.
pip install tuned-lens๐ฅ Github: https://github.com/alignmentresearch/tuned-lens
โฉ Paper: https://arxiv.org/abs/2303.08112v1
โญ๏ธ Dataset: https://paperswithcode.com/dataset/the-pile
๐ฅ Colab: https://colab.research.google.com/github/AlignmentResearch/tuned-lens/blob/main/notebooks/interactive.ipynb
https://t.iss.one/DataScienceT
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