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/
@deeplearning_ai
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/
@deeplearning_ai
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
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GIRAFFE: A Closer Look at the Code for CVPR 2021’s Best Paper
GIRAFFE is a learning-based, fully differentiable rendering engine for composing scenes as the summation of multiple “feature fields.”
https://towardsdatascience.com/giraffe-a-closer-look-at-cvpr-2021s-best-paper-1ec81f593fa9
https://t.me/MachineLearning_Programming
GIRAFFE is a learning-based, fully differentiable rendering engine for composing scenes as the summation of multiple “feature fields.”
https://towardsdatascience.com/giraffe-a-closer-look-at-cvpr-2021s-best-paper-1ec81f593fa9
https://t.me/MachineLearning_Programming
Medium
GIRAFFE: A Closer Look at CVPR 2021’s Best Paper
GIRAFFE is a learning-based, fully differentiable rendering engine for composing scenes as the summation of multiple “feature fields.”
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Class Activation Map methods implemented in Pytorch
https://github.com/jacobgil/pytorch-grad-cam
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𝗟𝗲𝗮𝗿𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 👨💻
𝗟𝗲𝗮𝗿𝗻 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗛𝗮𝗰𝗸𝗶𝗻𝗴 🚀
𝗟𝗲𝗮𝗿𝗻 𝗕𝗹𝗮𝗰𝗸𝗛𝗮𝘁 𝗠𝗲𝘁𝗵𝗼𝗱𝘀 💙
𝗔𝗻𝗱 𝗺𝘂𝗰𝗵 𝗺𝗼𝗿𝗲 𝗹𝗮𝘁𝗲𝘀𝘁 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗺𝗲𝘁𝗵𝗼𝗱𝘀, 𝘁𝗶𝗽𝘀 𝗮𝗻𝗱 𝘁𝗿𝗶𝗰𝗸𝘀.
💻 𝗛𝗲𝗿𝗲 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗹𝗲𝗮𝗿𝗻 :- 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴, 𝗛𝗮𝗰𝗸𝗶𝗻𝗴, 𝗖𝗿𝗮𝗰𝗸𝗶𝗻𝗴, 𝗪𝗲𝗯 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁, 𝗔𝗽𝗽 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁, 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴, 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲, 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴, 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲, 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴, 𝗚𝗿𝗮𝗽𝗵𝗶𝗰 𝗱𝗲𝘀𝗶𝗴𝗻, 𝗔𝗻𝗶𝗺𝗮𝘁𝗶𝗼𝗻, 𝗩𝗶𝗱𝗲𝗼 𝗲𝗱𝗶𝘁𝗶𝗻𝗴, 𝗣𝗵𝗼𝘁𝗼𝗴𝗿𝗮𝗽𝗵𝘆, 𝗣𝗵𝗼𝘁𝗼𝘀 𝗲𝗱𝗶𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗺𝗮𝗻𝘆 𝗺𝗼𝗿𝗲 𝗹𝗼𝘁𝘀 𝗼𝗳 𝘁𝗵𝗶𝗻𝗴 𝗶𝗻 𝗳𝗿𝗲𝗲 📚🏅🎖
✅ 𝗔 𝗰𝗹𝗲𝗮𝗻 𝗹𝗶𝗯𝗿𝗮𝗿𝘆 𝗳𝗼𝗿 𝗴𝗲𝗲𝗸𝘀.
𝗚𝗲𝘁 𝗕𝘂𝗴 𝗕𝗼𝘂𝗻𝘁𝘆, 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝗶𝗻𝗴, 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗛𝗮𝗰𝗸𝗶𝗻𝗴, 𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 & 𝗹𝗼𝘁 𝗺𝗼𝗿𝗲 𝗹𝗮𝘁𝗲𝘀𝘁 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗯𝗮𝘀𝗲𝗱 𝗲𝗕𝗼𝗼𝗸𝘀.
𝗜𝗻 𝘁𝗵𝗶𝘀 𝗖𝗵𝗮𝗻𝗻𝗲𝗹, 𝗬𝗼𝘂 𝘄𝗶𝗹𝗹 𝗴𝗲𝘁 𝗨𝗱𝗲𝗺𝘆 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀, 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝗿𝗮 𝗖𝗼𝘂𝗿𝘀𝗲𝘀, & 𝗙𝗿𝗲𝗲𝗢𝗻𝗹𝗶𝗻𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀.
𝙁𝙤𝙧 𝙛𝙧𝙚𝙚 𝙘𝙤𝙪𝙧𝙨𝙚𝙨,𝙗𝙤𝙤𝙠𝙨,𝙥𝙧𝙤𝙟𝙚𝙘𝙩𝙨,𝙞𝙣𝙩𝙚𝙧𝙣𝙨𝙝𝙞𝙥𝙨,𝙥𝙡𝙖𝙘𝙚𝙢𝙚𝙣𝙩𝙨 𝙖𝙣𝙙 𝙟𝙤𝙗𝙨 𝙧𝙚𝙡𝙖𝙩𝙚𝙙 𝙢𝙖𝙩𝙚𝙧𝙞𝙖𝙡 𝙖𝙣𝙙 𝙪𝙥𝙙𝙖𝙩𝙚𝙨 𝙟𝙤𝙞𝙣 𝙤𝙪𝙧 𝙩𝙚𝙡𝙚𝙜𝙧𝙖𝙢 𝙘𝙝𝙖𝙣𝙣𝙚𝙡:
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𝗟𝗲𝗮𝗿𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 👨💻
𝗟𝗲𝗮𝗿𝗻 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗛𝗮𝗰𝗸𝗶𝗻𝗴 🚀
𝗟𝗲𝗮𝗿𝗻 𝗕𝗹𝗮𝗰𝗸𝗛𝗮𝘁 𝗠𝗲𝘁𝗵𝗼𝗱𝘀 💙
𝗔𝗻𝗱 𝗺𝘂𝗰𝗵 𝗺𝗼𝗿𝗲 𝗹𝗮𝘁𝗲𝘀𝘁 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗺𝗲𝘁𝗵𝗼𝗱𝘀, 𝘁𝗶𝗽𝘀 𝗮𝗻𝗱 𝘁𝗿𝗶𝗰𝗸𝘀.
💻 𝗛𝗲𝗿𝗲 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗹𝗲𝗮𝗿𝗻 :- 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴, 𝗛𝗮𝗰𝗸𝗶𝗻𝗴, 𝗖𝗿𝗮𝗰𝗸𝗶𝗻𝗴, 𝗪𝗲𝗯 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁, 𝗔𝗽𝗽 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁, 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴, 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲, 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴, 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲, 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴, 𝗚𝗿𝗮𝗽𝗵𝗶𝗰 𝗱𝗲𝘀𝗶𝗴𝗻, 𝗔𝗻𝗶𝗺𝗮𝘁𝗶𝗼𝗻, 𝗩𝗶𝗱𝗲𝗼 𝗲𝗱𝗶𝘁𝗶𝗻𝗴, 𝗣𝗵𝗼𝘁𝗼𝗴𝗿𝗮𝗽𝗵𝘆, 𝗣𝗵𝗼𝘁𝗼𝘀 𝗲𝗱𝗶𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗺𝗮𝗻𝘆 𝗺𝗼𝗿𝗲 𝗹𝗼𝘁𝘀 𝗼𝗳 𝘁𝗵𝗶𝗻𝗴 𝗶𝗻 𝗳𝗿𝗲𝗲 📚🏅🎖
✅ 𝗔 𝗰𝗹𝗲𝗮𝗻 𝗹𝗶𝗯𝗿𝗮𝗿𝘆 𝗳𝗼𝗿 𝗴𝗲𝗲𝗸𝘀.
𝗚𝗲𝘁 𝗕𝘂𝗴 𝗕𝗼𝘂𝗻𝘁𝘆, 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝗶𝗻𝗴, 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗛𝗮𝗰𝗸𝗶𝗻𝗴, 𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 & 𝗹𝗼𝘁 𝗺𝗼𝗿𝗲 𝗹𝗮𝘁𝗲𝘀𝘁 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗯𝗮𝘀𝗲𝗱 𝗲𝗕𝗼𝗼𝗸𝘀.
𝗜𝗻 𝘁𝗵𝗶𝘀 𝗖𝗵𝗮𝗻𝗻𝗲𝗹, 𝗬𝗼𝘂 𝘄𝗶𝗹𝗹 𝗴𝗲𝘁 𝗨𝗱𝗲𝗺𝘆 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀, 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝗿𝗮 𝗖𝗼𝘂𝗿𝘀𝗲𝘀, & 𝗙𝗿𝗲𝗲𝗢𝗻𝗹𝗶𝗻𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀.
𝙁𝙤𝙧 𝙛𝙧𝙚𝙚 𝙘𝙤𝙪𝙧𝙨𝙚𝙨,𝙗𝙤𝙤𝙠𝙨,𝙥𝙧𝙤𝙟𝙚𝙘𝙩𝙨,𝙞𝙣𝙩𝙚𝙧𝙣𝙨𝙝𝙞𝙥𝙨,𝙥𝙡𝙖𝙘𝙚𝙢𝙚𝙣𝙩𝙨 𝙖𝙣𝙙 𝙟𝙤𝙗𝙨 𝙧𝙚𝙡𝙖𝙩𝙚𝙙 𝙢𝙖𝙩𝙚𝙧𝙞𝙖𝙡 𝙖𝙣𝙙 𝙪𝙥𝙙𝙖𝙩𝙚𝙨 𝙟𝙤𝙞𝙣 𝙤𝙪𝙧 𝙩𝙚𝙡𝙚𝙜𝙧𝙖𝙢 𝙘𝙝𝙖𝙣𝙣𝙚𝙡:
https://telegram.me/realgroupforprogrammer
𝗦𝗼 𝘄𝗵𝗮𝘁 𝗮𝗿𝗲 𝘆𝗼𝘂 𝘄𝗮𝗶𝘁𝗶𝗻𝗴 𝗳𝗼𝗿?
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Dive into Deep Learning
Interactive deep learning book with code, math, and discussions
Implemented with NumPy/MXNet, PyTorch, and TensorFlow
Adopted at 300 universities from 55 countries
@MachineLearning_Programming
Interactive deep learning book with code, math, and discussions
Implemented with NumPy/MXNet, PyTorch, and TensorFlow
Adopted at 300 universities from 55 countries
@MachineLearning_Programming
Page: https://d2l.ai/
PyTorch based: https://d2l.ai/d2l-en-pytorch.pdf
MXNET based: https://d2l.ai/d2l-en.pdf
Github: https://github.com/d2l-ai/d2l-en
👉👉@MachineLearning_Programming
PyTorch based: https://d2l.ai/d2l-en-pytorch.pdf
MXNET based: https://d2l.ai/d2l-en.pdf
Github: https://github.com/d2l-ai/d2l-en
👉👉@MachineLearning_Programming
GitHub
GitHub - d2l-ai/d2l-en: Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities…
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge. - d2l-ai/d2l-en
An important collection of the 15 best machine learning cheat sheets.
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/
https://t.me/MachineLearning_Programming
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/
https://t.me/MachineLearning_Programming
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
—————— ConvNeXt ——————--
Facebook propose ConvNeXt, a pure ConvNet model constructed entirely from standard ConvNet modules. ConvNeXt is accurate, efficient, scalable and very simple in design.
Github: https://github.com/facebookresearch/ConvNeXt
Paper: https://arxiv.org/abs/2201.03545
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@MachineLearning_Programming
Facebook propose ConvNeXt, a pure ConvNet model constructed entirely from standard ConvNet modules. ConvNeXt is accurate, efficient, scalable and very simple in design.
Github: https://github.com/facebookresearch/ConvNeXt
Paper: https://arxiv.org/abs/2201.03545
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@MachineLearning_Programming
5TH UG2+ PRIZE CHALLENGE CVPR 2022
$10K PRIZES
http://cvpr2022.ug2challenge.org/
https://docs.google.com/forms/d/e/1FAIpQLSeK0j4cPRNFQbm27qMfaTr27wRQ6tXMV2gmohjaJlbn2fAX0A/viewform
https://cmt3.research.microsoft.com/User/Login?ReturnUrl=%2FUG2CHALLENGE2022
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@MachineLearning_Programming
$10K PRIZES
http://cvpr2022.ug2challenge.org/
https://docs.google.com/forms/d/e/1FAIpQLSeK0j4cPRNFQbm27qMfaTr27wRQ6tXMV2gmohjaJlbn2fAX0A/viewform
https://cmt3.research.microsoft.com/User/Login?ReturnUrl=%2FUG2CHALLENGE2022
invite your friends 🌹🌹
@MachineLearning_Programming
Google Docs
CVPR2022 UG2+ Challenge Registration
Registration Deadline: April 30, 2022
One registration per team.
The primary contact email addresses must be institutional, i.e., commercial email addresses (e.g., Gmail or QQmail) are NOT allowed.
One registration per team.
The primary contact email addresses must be institutional, i.e., commercial email addresses (e.g., Gmail or QQmail) are NOT allowed.
The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.
https://www.ritchieng.com/the-incredible-pytorch/
https://github.com/ritchieng/the-incredible-pytorch
t.me/deeplearning_ai
.
https://www.ritchieng.com/the-incredible-pytorch/
https://github.com/ritchieng/the-incredible-pytorch
t.me/deeplearning_ai
.
GitHub
GitHub - ritchieng/the-incredible-pytorch: The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and…
The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch. - GitHub - ritchieng/the-incredible-pytorch: The Incredible PyTorch: a curated list...
Want to jump ahead in artificial intelligence and/or digital pathology? Excited to share that after 2+ years of development PathML 2.0 is out! An open source #computational #pathology software library created by Dana-Farber Cancer Institute/Harvard Medical School and Weill Cornell Medicine led by Massimo Loda to lower the barrier to entry to #digitalpathology and #artificialintelligence , and streamline all #imageanalysis or #deeplearning workflows.
⭐ Code: https://github.com/Dana-Farber-AIOS/pathml
⭐ Code: https://github.com/Dana-Farber-AIOS/pathml
GitHub
GitHub - Dana-Farber-AIOS/pathml: Tools for computational pathology
Tools for computational pathology. Contribute to Dana-Farber-AIOS/pathml development by creating an account on GitHub.
9 Best Tools to Debug Python for 2022
https://www.ittsystems.com/best-tools-to-debug-python/
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@Deeplearning_ai
.
https://www.ittsystems.com/best-tools-to-debug-python/
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@Deeplearning_ai
.
ITT Systems
9 Best Tools to Debug Python for 2025
Python is a high-level programming language, one of the top ten in the world in 2025. Find out the best tools to debug Python applications.
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PyAutoGUI is a cross-platform GUI automation Python module for human beings. Used to programmatically control the mouse & keyboard.
https://github.com/YashIndane/Call-of-Duty-
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@Deeplearning_ai
https://github.com/YashIndane/Call-of-Duty-
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@Deeplearning_ai
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A lightweight vision library for performing large scale object detection & instance segmentation
Github: https://github.com/obss/sahi
Paper: https://arxiv.org/abs/2202.06934v1
Kaggle notebook: https://www.kaggle.com/remekkinas/sahi-slicing-aided-hyper-inference-yv5-and-yx
Dataset: https://paperswithcode.com/dataset/xview
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@Deeplearning_ai
Github: https://github.com/obss/sahi
Paper: https://arxiv.org/abs/2202.06934v1
Kaggle notebook: https://www.kaggle.com/remekkinas/sahi-slicing-aided-hyper-inference-yv5-and-yx
Dataset: https://paperswithcode.com/dataset/xview
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@Deeplearning_ai
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Generative Occupancy Fields for 3D Surface-Aware Image Synthesis (NeurIPS 2021)
Project Page Paper Github
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@Deeplearning_ai
Project Page Paper Github
invite your friends 🌹🌹
@Deeplearning_ai