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/

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🕷 Bayesian Active Learning (BaaL)

Blog: https://www.elementai.com/news/2019/element-ai-makes-its-bayesian-active-learning-library-open-source

Documentation: https://baal.readthedocs.io.

Github: https://github.com/ElementAI/baal

Paper: https://arxiv.org/abs/2112.06586v1

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Brain tumor detection and segmentation from MRI images using CNN and Unet models.

The CNN model is used to detect whether a tumor is there or not. After 15 epochs of training, the calculated accuracy is about 99.6%.
The U-net model is used to segment tumors in MRI images of the brain. After 10 epochs of training, the calculated accuracy is about 98%.
These deep neural networks are implemented with Keras functional API. Use the trained models to detect and segment tumors on brain MRI images. The result is satisfactory.

You can download my U-net trained model from: "https://drive.google.com/drive/folders/1qt7l3HOGIwOguWsMKc5fuwG2NGiGOucf?usp=sharing" and CNN trained model from: "https://drive.google.com/drive/folders/1fXFzMwNG6HrbNp6-GASAgeybeSB3JWCd?usp=sharing".

To access the codes, refer to my GitHub.

Github: https://github.com/AryaKoureshi/Brain-tumor-detection

Website: https://aryakoureshi.github.io/project/BT_detection

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Lane Detection With OpenCV (Part 1)

1. Intro
2. Thresholding
3. Perspective Correction
4. Warping

https://dzone.com/articles/lane-detection-with-opencv


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https://youtu.be/NMeycZYNpWc
New Video watch now
Heart Disease Prediction using Logistic Regression
How to use classification techniques in ML?

Training Machine Learning Models More Efficiently with Dataset Distillation


http://ai.googleblog.com/2021/12/training-machine-learning-models-more.html

Training Machine Learning Models More Efficiently with Dataset Distillation

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🎓 GAN-Supervised Dense Visual Alignment

Github: https://github.com/wpeebles/gangealing

Project: https://www.wpeebles.com/gangealing

Paper: https://arxiv.org/abs/2112.04894v1

Dataset: https://paperswithcode.com/dataset/celeba

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More than 5000+ amazing 🤩 DataSet!!!
Hope you find it useful!!!

https://paperswithcode.com/datasets

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How to built a data science and machine learning roadmap in 2022?

https://venturebeat.com/2022/01/10/how-to-build-a-data-science-and-machine-learning-roadmap-in-2022/amp/

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Found some good books on Clustering Algorithms:
Book1: Cluster Analysis: Basic Concept

Book2: DATA CLUSTERING Algorithms and Applications

Book3: Algorithms for Clustering Data

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Scaling and managing ML models is becoming easier using MLOps with Kubernetes


https://towardsdatascience.com/mlops-with-kubernetes-rabbitmq-and-fastapi-b67d82e35fa4

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New Video watch now
Math behind Logistic Regression/ Classification technique using Sigmoid function

https://youtu.be/165sOkHniJc

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An introduction to Probabilities and Statistics like no other:

Seeing Theory: https://seeing-theory.brown.edu/

Brown University.
Free.

This interactive website will take you through the concepts you need in machine learning.
#freecourse #probability #statistics
Credit: Twitter (svpino)

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A friendly introduction to machine learning compilers and optimizers

#beginner #datascience #machinelearning

https://huyenchip.com/2021/09/07/a-friendly-introduction-to-machine-learning-compilers-and-optimizers.html

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