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Architecting, Developing, nixCraft, DevOps, AI/ML, Blockchain

2019 Cloud Native Machine Learning

Airflow and KubeFlow set to ease enterprise deployment of AI/ML

2018 has been an excellent year for machine learning, with frameworks such as PyTorch, Karas, Tensorflow and CNTK maturing in levels of stability and ease of use, sophisticated and academic concepts have become highly accessible to a wide range of developers. Anyone investing a few hours in technical tutorials can build a learner; yet, scaled production integration remains a pain point. However, 2018 saw significant advances in Cloud-Native and DevOps technologies.

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.

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.

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.