Issue 216
— Insight —
Data Science and the Art of Persuasion
In spite of all the success stories, many organizations don't get nearly the value they could from their data science efforts. This article from the Harvard Business Review explores the issues and, in particular, describes how communication disconnects between data science teams and decision-makers are often to blame. This is a good read with well-considered, specific suggestions.
A New Approach to Understanding How Machines Think
Been Kim, a research scientist at Google, describes a new technique for understanding how much of a specific input went into a machine learning's decision-making process.
Explore the core dimensions of AI technologies from a business perspective in this online program from MIT Sloan and MIT CSAIL. 6-week online program. Learn more.
— Sponsored Link —
MIT Sloan Artificial Intelligence: Implications for Business Strategy online program
Awesome introduction to TensorFlow that shows how to build it's core components from scratch. By the end of this tutorial, you'll have an intuitive understanding of TensorFlow's inner workings and will be able to use TensorFlow with confidence. This is very well written and easy to follow. If you're looking for portfolio ideas, this is a great post about what really matters. Nice tool for quickly exploring a dataset with SQL. Supports multiple data sources (e.g. PostgreSQL, MySQL, Redshift, etc), variables and email alerts. All queries are tracked and it works with your existing authentication system. Vettery specializes in tech roles and is completely free for job seekers. Interested? Submit your profile, and if accepted onto the platform, you can receive interview requests directly from top companies growing their data science teams. // sponsored
— Tools and Techniques —
Understand TensorFlow by mimicking its API from scratch
The cold start problem: how to build your machine learning portfolio
blazer - Business intelligence made simple
Find A Data Science Job Through Vettery
Is data wrangling in JavaScript something you would really want to do? Maybe. Python and R already do a great job with data wrangling but if you're working with data in node.js or a browser, you won't have those options. This interactive guide isn't new but it's a great resource and has been recently updated. This collection of web-based projects introduces machine learning to kids! Projects are based on a widely used educational coding platform called "Scratch" which was designed at the MIT Media Lab for kids ages 8-16. Projects cover tasks in image recognition, text classification, and simple game mechanics.
— Resources —
Learn JS Data
Machine Learning for Kids
This post from the Uber Engineering Blog introduces a new internal tool for debugging machine learning models. Called "Manifold," the tool leverages visual analytics to help machine learning practitioners optimize their models and identify trouble spots. This post describes the thinking behind Manifold's visual design and how it works. For anyone interested in data visualization, Uber's team is one of the most innovative around. Nice introduction to the open-source Plotly library. Make great-looking, fully-interactive plots with "a single line of Python."
— Data Viz —
Manifold: A Model-Agnostic Visual Debugging Tool for Machine Learning at Uber
The Next Level of Data Visualization in Python
Sign up for Free
and join more than 30,000 data lovers today.
No spam, ever. We'll never share your email address and you can opt out at any time.
No spam, ever.