Stakeholders want to understand what you are selling and how your approach is different. However, the level of detail they want varies greatly among your different customers. A simple whitepaper can be useful enough, in most situations. But, occaisionally, a layman will want to know exactly what you are doing and will not be content until you very succinctly explain it to them. This post will explain two methods for approaching such obstacles.
Working with Matrices and Tensors in PyTorch
The Artificial Intelligence field is moving from single-function libraries to frameworks for building different network models. TensorFlow was one of the first, and has strong production capabilities, such as process optimizations. However, its syntax is unintuitive, and the library has a reputation for being difficult for testing new models. This led to many organizations adopting PyTorch, with underlying Numpy, for designing network models. This post describes the basic data structures for working with Matrices and Tensors in PyTorch.
A Letter to Students: Math is the Basis for Competitive Edge in Every Field
Math is the greatest tool humans have for solving problems. However, it is incredibly expensive to get the experience of using it in the real-world. This is magnified by most teachers, and many university professors, not having any experience applying math anyplace other than academic contexts. This post shows the author’s trying experiences with learning maths in school, as well as the excitement of applying them to solve actual industry problems.
Building Math from the Ground-Up
After completing basic high school mathematics, one might think that math stands on its own. To understand and know that 1+1=2 is obvious. But math can go much deeper. In fact, we can build new concepts from math as well as build a math, itself. This is important because it effects how we think about other subjects, and is especially useful in helping us think about language and abstract concepts, such as in programming.
Communication Workflows with Hugo, Jupyter, and Friends
Static Site Generators are quite popular replacements for traditional server-based methods of delivering HTML. One of the most popular is Hugo because of its compilation speed. Combining Hugo with Jupyter notebooks, and other tools, can provide for a speedy workflow for pushing your ideas to the web, books (online or pdf), and more. This post will discuss why you may want to do this and provides tools for doing it.
Introduction to LaTeX
LaTeX is the document preparation and typesetting system used for scientific publications. LaTeX is based on the idea that ‘it is better to leave document design to document designers’, so the user can focus on the content. It requires a greater technical understanding than typical simple note-taking systems; however, because it is incredibly descriptive and produces aesthetically-appealing output, it is becoming more popular. This post provides a reference to configuration and syntax.
A Data Scientist's Guide to Modern Java(8+) and the JVM
Most data scientists avoid the Java Virtual Machine (JVM) like the plague. The features that make developers and data engineers so productive as a team, such as access modifiers and powerful IDEs, feel slow, old, and cumbersome to individuals interacting with data and prototyping new algorithms. However, the JVM and its supporting system matured to an indominatble force since its inception. It is ubiquitous in the most popular open-source projects; espectially in Big Data and distributed systems, such as Hadoop and Kafka.
Introduction to Groovy for Data Science on the JVM
Groovy is a scripting language that compiles to the Java Virtual Machine. It would be more-aptly named JavaScript if that name were not already taken, but it has many similarities to Python. In fact, some of the libraries implemented for Groovy, such as TableSaw, attempt to take it in the same Data Science direction as Python. This post will provide an introduction to the Groovy language using Jupyter notebook with BeakerX, and provides specific applications to Data Science.
The Cost of Internal Investment
There is one question on every economist’s mind: Where has inflation gone? It is imperative to understanding how actors make decisions and allocate limited resources. The fact that the inflation rate is so low for so long is incredibly disconcerting for many reasons (with the fear of deflation being just one reason). Because of low growth and a lack of inflation the US government and Federal Reserve, as well as nearly every nations’ central Bank, are pouring fuel onto the economy and getting a paltry return.
The Linux Command Line with Fish Shell
The Linux terminal (typically implemented with the Bourne Again Shell Script, BASH) can be intimidating for beginners and annoying for experienced programmers. But, it is a necessity for most users of Linux; and, in many situations, it can also be a huge productivity enhancer over graphical interfaces. Fish is a commandline tool that is a great complement to BASH. It is highly configureable and provides nice features such as syntax coloring and TAB command completion, out-of-the-box.