Cheat Sheets for AI, Machine Learning, Neural Networks, Big Data & Deep Learning
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I have been collecting AI cheat sheets for the last few months, and I’ve been sharing them with friends and colleagues from time to time. Recently, a lot of inquiries concerning the same sheets have been made, and so I’ve decided to organize and share the entire collection of the sheets. In this article, I have added descriptions and excerpts to contextualize and make things more interesting.
Below is the comprehensive list that I have compiled on this topic with Big-O provided at the end of the article.
Machine Learning Overview
Machine Learning Cheat Sheet
Machine Learning: Scikit-learn algorithm
The machine learning cheat sheet helps you get the right estimator for the job which is the most challenging part. The flowchart helps you check the documentation and rough guide of each estimator which assists you to discover more information about related problems and their ultimate solutions.
Scikit-learn (previously know as 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. The software is designed to inter-operate with the Python numerical and scientific libraries NumPy and SciPy.
Scikit-Learn Cheat Sheet
MACHINE LEARNING: ALGORITHM CHEAT SHEET
The below machine learning cheat is from Microsoft Azure. It will help you choose the appropriate machine learning algorithms for your predictive analytics solution. To start with, the cheat sheet will ask you about the nature of the data and then suggest the best algorithm for the job.
A cheat sheet for Neural Networks Graphs
Neutral Networks Cheat Sheet
Python for Data Science
Python Data Science Cheat Sheet
Big data cheat sheet
Numpy targets the CPython reference implementation of Python, which is a non-optimizing bytecode interpreter. Mathematical algorithms written for this version are often slower compared to compiled equivalents. Numpy solves the slowness problem partially by providing multidimensional arrays and functions and operators that operate efficiently on arrays, requiring rewriting some codes, in most cases, inner loops using Numpy.
Numpy Cheat Sheet
Google announced the second-generation of the TPU as well as the availability of the TPUs in Google Compute Engine in May 2017. The second-generation TPUs deliver up to 180 teraflops of performance. When organized into clusters of 64 TPUs, they provided up to 11.5 petaflops.
TensorFlow Cheat Sheet
This term ‘Pandas’ is coined from the term “panel data” which is an econometrics term meaning multidimensional structured data sets.
Pandas Cheat Sheet
After Google’s TensorFlow team decided to support Keras in TensorFlow’s core library in 2017, Chollet explained that Keras was conceived to be an interface rather than an end-to-end machine-learning framework. Keras, therefore, presents a more advanced, more intuitive set of abstractions which make it easy to configure neural networks despite the back-end scientific computing library.
Keras Cheat Sheet
The term “data wrangler” has started to gain popularity in the pop culture. In the 2017 movie Kong: Skull Island, Marc Evan Jackson as a character is introduced as “Steve Woodward, our data wrangler.”
Data Wrangling Cheat Sheet
Data wrangling with dplyr and tidyr
Data wrangling with dplyr and tidyr cheat Sheet
Matplotlib refers to 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. SciPy makes use of matplotlib.
Pyplot is a matplotlib module which provides a MATLAB-like interface. 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
Data Visualization Cheat Sheet
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.
PySpark Cheat Sheet
Big-O Algorithm Cheat Sheet
Big-O Algorithm Complexity Chart
BIG-O Algorithm Data Structure Operations
Big-O Array Sorting Algorithms