Facet Data

Sklearn datasets are included as part of the scikit-learn (sklearn) library, so they come pre-installed with the library.

16 Best Sklearn Datasets for Building Machine Learning Models

Facial recognition-based authentication to verify a user in a web application is discussed in a beginner-friendly manner using FaceIO APIs. 

How to Authenticate a User via Face Recognition in Your Web Application

For the past few years now, the increasing digitalization of customer journeys and the exponential improvement of cloud technologies and computing capacities have invited media groups to rethink the way they do business. If it sounds like I’m using long words to say “digital disruption”, trust your instinct. Many of these disruptions have been centered around the mountains of data media groups have access to, and what Artificial Intelligence (AI) (and machine learning more specifically) could do with it. Indeed, while artificial intelligence has been fully embraced by a plethora of pure players (Spotify, Netflix, Buzzfeed, Disney…) traditional actors are still lagging, and now see the technology as a shortcut to a much-needed renewed growth.

How AI and Machine Learning Are Infiltrating the Media Industry

Trying Clean Architecture on Golang

Hardware acceleration for data processing has a long history. It’s not new. When I was a child, my dad outfitted our 12 MHz 286 system with the optional 80287 Floating Point Unit (FPU) coprocessor, which ran at a whopping 4.7MHz.

A gentle introduction to hardware accelerated data processing

Since the 2016 US presidential election, the term “fake news” has become part of our daily vernacular. Fake news has become so prevalent in our society that even the most sophisticated of us fall for it sometimes. It has reached such epidemic proportions that French President Macron recently commented, “we must fight against the virus of fake news.”

The Fake News Arms Race

GPU databases are the hottest new thing in the database world, and it’s the most innovative trend since Hadoop showed up over 5 years ago.

Which GPU database is right for me?

Unicorn Mode as demonstrated in my <a href="https://medium.com/@njvoss299/afl-unicorn-fuzzing-arbitrary-binary-code-563ca28936bf" target="_blank">previous article</a> is not overly useful on the surface. It has lots of limitations that make it clumsy and slow to employ against most real-world situations. For example, what if the binary you want to emulate calls an imported library function that is likely to call into the kernel such as malloc() or printf()? What if the code you want to fuzz is highly stateful, and requires lots of memory regions (heap allocations, stack pointers, global variables, etc.) that aren’t known until run-time? In fact, just about the only straightforward use of it that I’ve found is when working with flat embedded run-time system memory snapshots recovered from firmware by a debugger.

afl-unicorn: Part 2 — Fuzzing the ‘Unfuzzable’

Empower your business team to explore transaction data in your CDW, combine with business data from your CRM to help to diagnose and solve revenue problems, without SQL or escalating to Engineering

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