Last year, I shared my list of cheat sheets that I have been collecting and the response was enormous. Nearly a million people read the article, tens of thousands shared it, and this list of AI Cheat Sheets quickly become one of the most popular online!

*However there was a couple of problems…*

Many you stated that some of the cheat sheets were hard to read, that the files sizes weren’t large enough and that there was no downloadable option.

Over the past few months, we totally redesigned the cheat sheets so they are in high definition and downloadable. **The goal was to make them easy to read and beautiful so you will want to look at them, print them and share them.**

If you like these cheat sheets, you can let me know here.

Without further ado, let’s begin.

# Part 1: Neural Networks Cheat Sheets

# Neural Networks Basics

An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science.

## Basically, there are 3 different layers in a neural network :

- Input Layer (All the inputs are fed in the model through this layer)
- Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers)
- Output Layer (The data after processing is made available at the output layer)

# Neural Networks Graphs

Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.

# Part 2: Machine Learning Cheat Sheets

## >>> If you like these cheat sheets, you can let me know here.<<<

# Machine Learning with Emojis

# Machine Learning: Scikit Learn Cheat Sheet

Scikit-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 is a simple and efficient tools for data mining and data analysis. It’s built on NumPy, SciPy, and matplotlib an open source, commercially usable — BSD license

# 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.

>>If you like these cheat sheets, you can let me know here.

# Machine Learning: Scikit-Learn Algorythm for Azure Machine Learning Studios

# Part 3: Data Science with Python

# Data Science: TensorFlow Cheat Sheet

TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks.

If you like these cheat sheets, you can let me know here.

# Data Science: Python Basics Cheat Sheet

Python is one of the most popular data science tool due to its low and gradual learning curve and the fact that it is a fully fledged programming language.

# Data Science: PySpark RDD Basics Cheat Sheet

“At a high level, every Spark application consists of a *driver program* that runs the user’s `main`

function and executes various *parallel operations* on a cluster. The main abstraction Spark provides is a *resilient distributed dataset* (RDD), which is a collection of elements partitioned across the nodes of the cluster that can be operated on in parallel. RDDs are created by starting with a file in the Hadoop file system (or any other Hadoop-supported file system), or an existing Scala collection in the driver program, and transforming it. Users may also ask Spark to *persist* an RDD in memory, allowing it to be reused efficiently across parallel operations. Finally, RDDs automatically recover from node failures.” via Spark.Aparche.Org

# Data Science: NumPy Basics Cheat Sheet

NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

*If you like these cheat sheets, you can let me know **here.*

# Data Science: Bokeh Cheat Sheet

“Bokeh is an interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of versatile graphics, and to extend this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.” from Bokeh.Pydata.com

# Data Science: Karas Cheat Sheet

Keras is an open-source neural-network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible.

# Data Science: **Padas Basics Cheat Sheet**

Pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license.

> If you like these cheat sheets, you can let me know here.

# Pandas Cheat Sheet: Data Wrangling in Python

# 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 Science: Data Wrangling with Pandas Cheat Sheet

# “Why Use tidyr & dplyr

- Although many fundamental data processing functions exist in R, they have been a bit convoluted to date and have lacked consistent coding and the ability to easily
*flow*together → leads to difficult-to-read nested functions and/or*choppy*code. - R Studio is driving a lot of new packages to collate data management tasks and better integrate them with other analysis activities → led by Hadley Wickham & the R Studio team → Garrett Grolemund, Winston Chang, Yihui Xie among others.
- As a result, a lot of data processing tasks are becoming packaged in more cohesive and consistent ways → leads to:
- More efficient code
- Easier to remember syntax
- Easier to read syntax” via Rstudios

# Data Science: Data Wrangling with ddyr and tidyr

If you like these cheat sheets, you can let me know here.

# Data Science: Scipy Linear Algebra

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]

# Data Science: Matplotlib Cheat Sheet

Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented APIfor 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.

# Data Science: **Data Visualization with ggplot2 Cheat Sheet**

## >>> If you like these cheat sheets, you can let me know here. <<<

# Data Science: Big-O Cheat Sheet

Originally Published url: https://becominghuman.ai/cheat-sheets-for-ai-neural-networks-machine-learning-deep-learning-big-data-science-pdf-f22dc900d2d7

# About Stefan

Stefan is the founder of BecomingHuman.Ai and Chatbot’s Life, two of the most popular publications on Bots & AI with over 6 million views annually. Chatbot’s Life 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.