Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

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 (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.


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


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.


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 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.


The name ‚ÄėPandas‚Äô is derived from the term ‚Äúpanel data‚ÄĚ, an¬†econometricsterm for multidimensional structured data sets.

Data Wrangling

The term ‚Äúdata wrangler‚ÄĚ 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 ‚ÄúSteve Woodward, our data wrangler‚ÄĚ.

Data Wrangling with dplyr and tidyr


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¬†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¬†‚Äúpylab‚ÄĚ 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



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.


Big-O Algorithm Cheat Sheet:

Bokeh Cheat Sheet:

Data Science Cheat Sheet:

Data Wrangling Cheat Sheet:

Data Wrangling:

Ggplot Cheat Sheet:

Keras Cheat Sheet:


Machine Learning Cheat Sheet:

Machine Learning Cheat Sheet:

ML Cheat Sheet::

Matplotlib Cheat Sheet:


Neural Networks Cheat Sheet:

Neural Networks Graph Cheat Sheet:

Neural Networks:

Numpy Cheat Sheet:


Pandas Cheat Sheet:


Pandas Cheat Sheet:

Pyspark Cheat Sheet:

Scikit Cheat Sheet:


Scikit-learn Cheat Sheet:

Scipy Cheat Sheet:


TesorFlow Cheat Sheet:

Tensor Flow:

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