Tidyr, dplyr, and ggplot2 are designed to work with tidy data. The dplyr package The dplyr package provides a number of very useful functions for manipulating dataframes in a way that will reduce the self-repetition, reduce the probability of making errors, and probably even save you some typing. Cheet Sheet; Cheat Sheet for readr and tidyr; Cheat Sheet for readr and tidyr. July 21, 2019 Cheet Sheet yongxuanl 0. The following cheat.

Getting started

tidyr functions fall into five main categories: Free script writing software.

• “Pivotting” which converts between long and wide forms. tidyr 1.0.0 introduces pivot_longer() and pivot_wider(), replacing the older spread() and gather() functions. See vignette('pivot') for more details.

• “Rectangling”, which turns deeply nested lists (as from JSON) into tidy tibbles. See unnest_longer(), unnest_wider(), hoist(), and vignette('rectangle') for more details.

• Nesting converts grouped data to a form where each group becomes a single row containing a nested data frame, and unnesting does the opposite. See nest(), unnest(), and vignette('nest') for more details.

• Splitting and combining character columns. Use separate() and extract() to pull a single character column into multiple columns; use unite() to combine multiple columns into a single character column.

• Make implicit missing values explicit with complete(); make explicit missing values implicit with drop_na(); replace missing values with next/previous value with fill(), or a known value with replace_na(). Download teac others driver.

Complete List of Cheat Sheets and Infographics for Artificial intelligence (AI), Neural Networks, Machine Learning, Deep Learning and Big Data.

Content Summary

Neural Networks
Neural Networks Graphs
Machine Learning Overview
Machine Learning: Scikit-learn algorithm
Scikit-Learn
Machine Learning: Algorithm Cheat Sheet
Python for Data Science
TensorFlow
Keras
Numpy
Pandas
Data Wrangling
Data Wrangling with dplyr and tidyr
Scipy
Matplotlib
Data Visualization
PySpark
Big-O
Resources

Neural Networks

Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. The neural network itself is not an algorithm, but rather a framework for many different machine learning algorithms to work together and process complex data inputs. Such systems “learn” to perform tasks by considering examples, generally without being programmed with any task-specific rules.

Neural Networks Graphs

Graph Neural Networks (GNNs) for representation learning of graphs broadly follow a neighborhood aggregation framework, where the representation vector of a node is computed by recursively aggregating and transforming feature vectors of its neighboring nodes. Many GNN variants have been proposed and have achieved state-of-the-art results on both node and graph classification tasks.

Machine Learning Overview

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in the applications of email filtering, detection of network intruders, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task.

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

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.

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.

Python for Data Science

TensorFlow

In May 2017 Google announced the second-generation of the TPU, as well as the availability of the TPUs in Google Compute Engine. 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.

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.

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.

Pandas

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

Data Wrangling

R Tidyr Cheat Sheet

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 with dplyr and tidyr

Scipy

R Dataframe 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.

Matplotlib

Tidyverse Cheat Sheet Pdf

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. 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 Visualization

PySpark

Big-O

Big O notation is a mathematical notation that describes the limiting behavior of a function when the argument tends towards a particular value or infinity. It is a member of a family of notations invented by Paul Bachmann, Edmund Landau and others, collectively called Bachmann–Landau notation or asymptotic notation.

Resources

Big-O Algorithm Cheat Sheet
Bokeh Cheat Sheet
Data Science Cheat Sheet
Data Wrangling Cheat Sheet
Data Wrangling
Ggplot Cheat Sheet
Keras Cheat Sheet
Keras
Machine Learning Cheat Sheet
Machine Learning Cheat Sheet
ML Cheat Sheet
Matplotlib Cheat Sheet
Matpotlib
Neural Networks Cheat Sheet
Neural Networks Graph Cheat Sheet
Neural Networks
Numpy Cheat Sheet
NumPy
Pandas Cheat Sheet
Pandas
Pandas Cheat Sheet
Pyspark Cheat Sheet
Scikit Cheat Sheet
Scikit-learn
Scikit-learn Cheat Sheet
Scipy Cheat Sheet
SciPy
TesorFlow Cheat Sheet
Tensor Flow
Course Duck > The World’s Best Machine Learning Courses & Tutorials in 2020

Tag: Machine Learning, Deep Learning, Artificial Intelligence, Neural Networks, Big Data