The Most Complete List of Best AI Cheat Sheets
Over the past few months, I have been collecting AI cheat sheets. From time to time I share them with friends and colleagues and recently I have been getting asked a lot, so I decided to organize and share the entire collection. To make things more interesting and give context, I added descriptions and/or excerpts for each major topic.
This is the most complete list and the Big-O is at the very end, enjoy…
If you like this list, you can let me know here.
Neural Networks
Neural Networks Graphs
Neural Network Cheat Sheet
Machine Learning Overview
Machine Learning: Scikit-learn algorithm
This machine learning cheat sheet will help you find the right estimator for the job which is the most difficult part. The flowchart will help you check the documentation and rough guide of each estimator that will help you to know more about the problems and how to solve it.
Scikit-Learn
Scikit-learn (formerly scikits.learn) is a free softwaremachine learninglibrary for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
MACHINE LEARNING : ALGORITHM CHEAT SHEET
This machine learning cheat sheet from Microsoft Azure will help you choose the appropriate machine learning algorithms for your predictive analytics solution. First, the cheat sheet will asks you about the data nature and then suggests the best algorithm for the job.
Python for Data Science
TensorFlow
In May 2017 Google announced the second-generation of the TPU, as well as the availability of the TPUs in Google Compute Engine.[12] The second-generation TPUs deliver up to 180 teraflops of performance, and when organized into clusters of 64 TPUs provide up to 11.5 petaflops.
Keras
In 2017, Google’s TensorFlow team decided to support Keras in TensorFlow’s core library. Chollet explained that Keras was conceived to be an interface rather than an end-to-end machine-learning framework. It presents a higher-level, more intuitive set of abstractions that make it easy to configure neural networks regardless of the backend scientific computing library.
Numpy
NumPy targets the CPython reference implementation of Python, which is a non-optimizing bytecode interpreter. Mathematical algorithms written for this version of Python often run much slower than compiled equivalents. NumPy address the slowness problem partly by providing multidimensional arrays and functions and operators that operate efficiently on arrays, requiring rewriting some code, mostly inner loops using NumPy.
Pandas
The name ‘Pandas’ is derived from the term “panel data”, an econometricsterm for multidimensional structured data sets.
Data Wrangling
The term “data wrangler” is starting to infiltrate pop culture. In the 2017 movie Kong: Skull Island, one of the characters, played by actor Marc Evan Jackson is introduced as “Steve Woodward, our data wrangler”.
Data Wrangling with dplyr and tidyr
Scipy
SciPy builds on the NumPy array object and is part of the NumPy stack which includes tools like Matplotlib, pandas and SymPy, and an expanding set of scientific computing libraries. This NumPy stack has similar users to other applications such as MATLAB, GNU Octave, and Scilab. The NumPy stack is also sometimes referred to as the SciPy stack.[3]
Matplotlib
matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-orientedAPI for embedding plots into applications using general-purpose GUI toolkitslike Tkinter, wxPython, Qt, or GTK+. There is also a procedural “pylab” interface based on a state machine (like OpenGL), designed to closely resemble that of MATLAB, though its use is discouraged.[2] SciPy makes use of matplotlib.
pyplot is a matplotlib module which provides a MATLAB-like interface.[6]matplotlib is designed to be as usable as MATLAB, with the ability to use Python, with the advantage that it is free.
Data Visualization
PySpark
Big-O
About Stefan
Stefan is the founder of Chatbot’s Life, a Chatbot media and consulting firm. Chatbot’s Life has grown to over 150k views per month and has become the premium place to learn about Bots & AI online. Chatbot’s Life has also consulted many of the top Bot companies like Swelly, Instavest, OutBrain, NearGroup and a number of Enterprises.
Resources
Big-O Algorithm Cheat Sheet: http://bigocheatsheet.com/
Bokeh Cheat Sheet: https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Bokeh_Cheat_Sheet.pdf
Data Science Cheat Sheet: https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics
Data Wrangling Cheat Sheet: https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf
Data Wrangling: https://en.wikipedia.org/wiki/Data_wrangling
Ggplot Cheat Sheet: https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf
Keras Cheat Sheet: https://www.datacamp.com/community/blog/keras-cheat-sheet#gs.DRKeNMs
Keras: https://en.wikipedia.org/wiki/Keras
Machine Learning Cheat Sheet: https://ai.icymi.email/new-machinelearning-cheat-sheet-by-emily-barry-abdsc/
Machine Learning Cheat Sheet: https://docs.microsoft.com/en-in/azure/machine-learning/machine-learning-algorithm-cheat-sheet
ML Cheat Sheet:: http://peekaboo-vision.blogspot.com/2013/01/machine-learning-cheat-sheet-for-scikit.html
Matplotlib Cheat Sheet: https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet#gs.uEKySpY
Matpotlib: https://en.wikipedia.org/wiki/Matplotlib
Neural Networks Cheat Sheet: http://www.asimovinstitute.org/neural-network-zoo/
Neural Networks Graph Cheat Sheet: http://www.asimovinstitute.org/blog/
Neural Networks: https://www.quora.com/Where-can-find-a-cheat-sheet-for-neural-network
Numpy Cheat Sheet: https://www.datacamp.com/community/blog/python-numpy-cheat-sheet#gs.AK5ZBgE
NumPy: https://en.wikipedia.org/wiki/NumPy
Pandas Cheat Sheet: https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.oundfxM
Pandas: https://en.wikipedia.org/wiki/Pandas_(software)
Pandas Cheat Sheet: https://www.datacamp.com/community/blog/pandas-cheat-sheet-python#gs.HPFoRIc
Pyspark Cheat Sheet: https://www.datacamp.com/community/blog/pyspark-cheat-sheet-python#gs.L=J1zxQ
Scikit Cheat Sheet: https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet
Scikit-learn: https://en.wikipedia.org/wiki/Scikit-learn
Scikit-learn Cheat Sheet: http://peekaboo-vision.blogspot.com/2013/01/machine-learning-cheat-sheet-for-scikit.html
Scipy Cheat Sheet: https://www.datacamp.com/community/blog/python-scipy-cheat-sheet#gs.JDSg3OI
SciPy: https://en.wikipedia.org/wiki/SciPy
TesorFlow Cheat Sheet: https://www.altoros.com/tensorflow-cheat-sheet.html
Tensor Flow: https://en.wikipedia.org/wiki/TensorFlow