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

Here is the entire collection of cheat sheets with descriptions. Let’s have a look-

1.Machine Learning Overview

Machine Learning: Scikit-learn algorithm

This Machine Learning cheat sheet will assist you with finding the correct estimator for the job which is the most difficult part. The flowchart will assist you in checking the documentation and rough guide of every estimator that will assist you in knowing more about the issues and how to solve it.

Scikit-Learn 

Scikit-learn (in the past 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 intended to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

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MACHINE LEARNING: ALGORITHM CHEAT SHEET

This machine learning cheat sheet from Microsoft Azure will assist you in picking the proper machine learning algorithms for your predictive analytics solution. To begin with, the cheat sheet will get some information about the data nature and afterward recommends the best algorithm for the job.

Python for Data Science

TensorFlow

In May 2017 Google declared the second-generation of the TPU, as well as the accessibility of the TPUs in Google Compute Engine. The second-generation TPUs convey 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 help Keras in TensorFlow’s core library. Chollet clarified that Keras was considered to be an interface rather than an end-to-end machine-learning framework. It shows a more elevated level, a more intuitive set of abstractions that make it simple to configure neural networks regardless of the backend scientific computing library.

Numpy

NumPy focuses on the CPython reference implementation of Python, which is a non-optimizing bytecode interpreter. Mathematical algorithms composed of this version of Python regularly run much slower than compiled equivalents. NumPy addresses the slowness issue partly by giving multi-dimensional arrays, capacities and operators that work efficiently on arrays, requiring rewriting some code, generally inner loops utilizing NumPy.

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Data Wrangling

The term “data wrangler” is beginning to infiltrate pop culture. In the 2017 movie Kong: Skull Island, one of the characters, played by actor Marc Evan Jackson is presented as “Steve Woodward, our data wrangler”.

Data Wrangling with dplyr and tidyr

Scipy

SciPy builds on the NumPy array object and is a part of the NumPy stack which incorporates tools like Matplotlib, pandas, and SymPy, and a growing set of scientific computing libraries.

This NumPy stack has similar users to different applications, for example, MATLAB, GNU Octave, and Scilab. The NumPy stack is also in some cases referred to as the SciPy stack.

Matplotlib:

Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API to insert plots into applications utilizing general-purpose GUI toolbox like Tkinter, wxPython, Qt, or GTK+. There is also a procedural “pylab” interface dependent on a state machine (like OpenGL), designed to closely resemble that of MATLAB, however, its utilization is discouraged. SciPy utilizes matplotlib. 

pyplot is a matplotlib module which gives a MATLAB-like interface. matplotlib is intended to be as usable as MATLAB, with the capacity to utilize Python, with the favorable position that it is free.

Data Visualization

PySpark

Big-O:

Neural Networks:

Neural Networks Graphs

I hope that by now you have had an overview of AI & Machine learning. Before you enroll in ZaranTech’s certification course on AI&ML, do check out the Machine Learning with Python tutorial for Beginners: