Essential Cheat Sheets for Machine Learning and Deep Learning Engineers

Machine learning is complex. For newbies, starting to learn machine learning can be painful if they don’t have right resources to learn from. Most of the machine learning libraries are difficult to understand and learning curve can be a bit frustrating. I am creating a repository on Github(cheatsheets-ai) containing cheatsheets for different machine learning frameworks, gathered from different sources. Do visit the Github repository, also, contribute cheat sheets if you have any. Thanks.

List of Cheatsheets:
1. Keras
2. Numpy
3. Pandas
4. Scipy
5. Matplotlib
6. Scikit-learn
7. Neural Networks Zoo
8. ggplot2
9. PySpark
10. R Studio
11. Jupyter Notebook
12. Dask

  1. Keras
Source — https://www.datacamp.com/community/blog/keras-cheat-sheet#gs.DRKeNMs

2. Numpy

Source — https://www.datacamp.com/community/blog/python-numpy-cheat-sheet#gs.AK5ZBgE

3. Pandas

Source — https://www.datacamp.com/community/blog/pandas-cheat-sheet-python#gs.HPFoRIc
Source — https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.oundfxM

4. Scipy

Source — https://www.datacamp.com/community/blog/python-scipy-cheat-sheet#gs.JDSg3OI

5. Matplotlib

Source — https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet#gs.uEKySpY

6. Scikit-learn

Source — https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet

7. Neural Networks Zoo

Source — http://www.asimovinstitute.org/neural-network-zoo/

8. ggplot2

Source — https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf

9. PySpark

Source — https://www.datacamp.com/community/blog/pyspark-cheat-sheet-python#gs.L=J1zxQ
Source — https://www.datacamp.com/community/blog/pyspark-cheat-sheet-python#gs.L=J1zxQ
Source — https://www.datacamp.com/community/blog/pyspark-sql-cheat-sheet

10. R Studio (dplyr and tidyr)

Source — https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf

11. Jupyter Notebook

Source — https://www.datacamp.com/community/blog/jupyter-notebook-cheat-sheet

12. Dask

Source — http://docs.dask.org/en/latest/_downloads/daskcheatsheet.pdf
Source — http://docs.dask.org/en/latest/_downloads/daskcheatsheet.pdf
Source — http://docs.dask.org/en/latest/_downloads/daskcheatsheet.pdf
Source — http://docs.dask.org/en/latest/_downloads/daskcheatsheet.pdf

Thank you for reading.

If you want to get into contact, you can reach out to me at ahikailash1@gmail.com

About author

Kailash Ahirwar

I am a Co-Founder of MateLabs, where we have built Mateverse, an ML Platform which enables everyone to easily build and train Machine Learning Models, without writing a single line of code.

Note: Recently, I published a book on GANs titled “Generative Adversarial Networks Projects”, in which I covered most of the widely popular GAN architectures and their implementations. DCGAN, StackGAN, CycleGAN, Pix2pix, Age-cGAN, and 3D-GAN have been covered in details at the implementation level. Each architecture has a chapter dedicated to it. I have explained these networks in a very simple and descriptive language using Keras framework with Tensorflow backend. If you are working on GANs or planning to use GANs, give it a read and share your valuable feedback with me at ahikailash1@gmail.com

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