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

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 Cheat Sheet

Neural Networks Graphs



Neural Networks Graphs Cheat Sheet




Neural Network Cheat Sheet

Machine Learning Cheat Sheet

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.


Machine Learning Cheat Sheet

Scikit-Learn

Scikit-learn (formerly scikits.learn) is a free software machine learning library 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.


Scikit-Learn Cheat Sheet



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.

MACHINE LEARNING ALGORITHM CHEAT SHEET

>>> If you like this list, you can let me know here. <<<

Python for Data Science




Python Data Science Cheat Sheet


Big Data Cheat Sheet

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.


TesorFlow Cheat Sheet

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.


Keras Cheat Sheet

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.


Numpy Cheat Sheet

Pandas

The name ‘Pandas’ is derived from the term “panel data”, an econometrics term for multidimensional structured data sets.


Pandas Cheat Sheet

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 Cheat Sheet


Pandas Data Wrangling Cheat Sheet


Data Wrangling with dplyr and tidyr Cheat Sheet


Data Wrangling with dplyr and tidyr Cheat Sheet

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]


Scipy Cheat Sheet

Matplotlib

matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like 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.


Matplotlib Cheat Sheet
>>> If you like this list, you can let me know here. <<<

Data Visualization



Data Visualization Cheat Sheet


ggplot cheat sheet

PySpark



Pyspark Cheat Sheet

Big-O



Big-O Algorithm Cheat Sheet


Big-O Algorithm Complexity Chart
BIG-O Algorithm Data Structure Operations


Big-O Array Sorting Algorithms





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