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Linear Algebra: Matrices 1

Linear Algebra Crash Course for Programmers Part 2a

This article on matrices is part two of an ongoing crash course on programming with linear algebra, demonstrating concepts and implementations in Python. The following examples will demonstrate some of the various mathematical notations and their corresponding implementations, easily translatable to any programming language with mature math libraries. This series began with Linear Algebra: Vectors Crash Course for Python Programmers Part 1. Vectors are the building blocks of matrices, and a good foundational understanding of vectors is essential to understanding matrices.

Linear Algebra: Vectors

Crash Course for Python Programmers Part 1

This article on vectors is part of an ongoing crash course on linear algebra programming, demonstrating concepts and implementations in Python. The following examples will demonstrate some of the algebraic and geometric interpretations of a vector using Python. A vector is an ordered list of numbers, represented in row or column form. This small series of articles on linear algebra is meant to help you prepare for learning the deeper concepts related to Machine Learning and math that drives the higher level abstractions provided by many of the libraries available today.

FaaS on Kubernetes

Kubeless, Python and Elasticsearch

FaaS or Function as a Service also known as Serverless computing implementations are gaining popularity. Discussed often are the cost savings and each implementations relationship to the physical and network architecture of a specific platform or vendor. While many of the cost and infrastructure advantages of FaaS are compelling, its only one of many advantages. Below, I hope to demonstrate how easy it is to develop and deploy FaaS components into a custom Kubernetes cluster.

Python Data Essentials - Matplotlib and Seaborn

A beginners guide.

There is an overwhelming number of options for developers needing to provide data visualization. The most popular library for data visualization in Python is Matplotlib, and built directly on top of Matplotlib is Seaborn. The Seaborn library is “tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels.” This article is only intended to get you started with Matplotlib and Seaborn.

Python Data Essentials - Pandas

A data type equivalent to super-charged spreadsheets.

Pandas bring Python a data type equivalent to super-charged spreadsheets. Pandas add two highly expressive data structures to Python, Series and DataFrame. Pandas Series and DataFrames provide a performant analysis and manipulation of “relational” or “labeled” data similar to relational database tables like MySQL or the rows and columns of Excel. Pandas are great for working with time series data as well as arbitrary matrix data, and unlabeled data. Pandas leverage NumPy and if you are not familiar with this fundamental library for working with numbers, then I suggest you take a look at Python Data Essentials - NumPy to get a decent footing.

Python Data Essentials - Numpy

Powerful N-dimensional array objects.

Python is one of The Most Popular Languages for Data Science, and because of this adoption by the data science community, we have libraries like NumPy, Pandas and Matplotlib. NumPy at it’s core provides a powerful N-dimensional array objects in which we can perform linear algebra, Pandas give us data structures and data analysis tools, similar to working with a specialized database or powerful spreadsheets and finally Matplotlib to generate plots, histograms, power spectra, bar charts, error charts and scatterplots to name a few.

Essential Python 3

Programming in Python

This article is a quick tour of basic Python 3 syntax, components and structure. I intend to balance a cheat sheet format with hello world style boilerplate. If you are already a software developer and need a quick refresh on Python then I hope you benefit from my notes below. I am a professional software developer for a software development company, and for that reason, I work with a lot of languages, expert in some, and ok in others.