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

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

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://www.tgoop.com/MachineLearning_Programming

—————— 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

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

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

www.tgoop.com/deeplearning_ai

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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

9 Best Tools to Debug Python for 2022

https://www.ittsystems.com/best-tools-to-debug-python/

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@Deeplearning_ai

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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

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

Generative Occupancy Fields for 3D Surface-Aware Image Synthesis (NeurIPS 2021)

Project Page Paper Github


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@Deeplearning_ai

EG3D: Efficient Geometry-aware 3D Generative Adversarial Networks

https://youtu.be/cXxEwI7QbKg


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@Deeplearning_ai

🛸UFO: segmentation @140+ FPS🛸

👉Unified Transformer Framework for Co-Segmentation, Co-Saliency & Salient Object Detection. All in one!


𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Unified framework for co-segmentation
✅Co-segmentation, co-saliency, saliency
✅Block for long-range dependencies
✅Able to reach for 140 FPS in inference
✅The new SOTA on multiple datasets
✅Source code under MIT License


[PAPER] [Source Code]

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@Deeplearning_ai

If you are learning Machine Learning and wants to make end-to-end Machine Learning real-world projects, then this website can be a great resource for you.

It has project bundle(Dragon bundle) comprising more than 550+ real-world projects in ML, DL, DS, CV and NLP and PYTHON3.

More details are showned in the image above.

- Each project comes with required Dataset, complete source code(Python3) and documentation along with explanatory comments so that even beginner can understand.

- Life time access and projects are getting updates each month.

You can download the list of complete 550+ projects from our website.

Visit our website for more information.
Website Link:
https://tensorprojects.com/dragonbundle

At DAIR.AI we heart open education. We are excited to share some of the best and most recent machine learning courses available on YouTube.

Hot topics:
1. Stanford CS229: Machine Learning
2. Practical Deep Learning for Coders (2020)
3. Deep Unsupervised Learning
4. Advanced NLP
5. Deep Learning for Computer Vision
6. Deep Reinforcement Learning
7. Full Stack Deep Learning
8. Self-Driving Cars (Tübingen)

https://github.com/dair-ai/ML-YouTube-Courses

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@Deeplearning_ai

Free programming courses & quests with cash rewards for your time in one place 📚💰

StackUp [app.stackup.dev] is a platform made for devs where you can learn about programming languages like Rust, Python, Go, Solidity, and other technologies, and earn while learning. Rewards are given after successful completion of quests.

With new campaigns every week, you can earn from a pool of over 10,000USD in cash rewards each month!

To sign up use code "machinelearning0" and gain early access: https://bit.ly/3FpfqHr

Hope it helps you to level up in the community and master different tools essential to your career as a developer! 🚀

@deeplearning_ai

Lightweight Python library for adding real-time object tracking to any detector.

https://github.com/tryolabs/norfair


@deeplearning_ai

Accelerate AI training in a few lines of code without changing the training setup.


https://github.com/nebuly-ai/nebulgym



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@Deeplearning_ai