by Nick Locascio – 4 min read
Apple rolled out iOS 11.1 on November 1st, releasing a multitude of issues and bugs. But nothing quite matched the magnitude, attention, and hysteria that the A [?] bug has received this past week.
Diary of a Data Scientist at Booking.com
by Nishikant Dhanuka – 6 min read
I joined Booking.com as a data scientist about two and a half years back, straight after a 3 years consulting gig in Dubai. Moving from consulting to a pure data science role was a big shift in my career and in hindsight I’m very happy I made that choice.
Regularization in Machine Learning
by Prashant Gupta – 7 min read
One of the major aspects of training your machine learning model is avoiding overfitting. The model will have a low accuracy if it is overfitting.
Essential Algorithms Every ML Engineer Needs to Know
by Christopher Dossman – 4 min read
Machine learning as a field has been around for a long time before deep neural networks took over the scene. Here are a list of the algorithms you need to know, so you can tackle any problem that comes your way.
Hinton++
by Anthony Repetto – 10 min read
Geoffrey Hinton is onto something. His model of machine intelligence, which relies upon neuron-clumps that he calls ‘capsules‘, is the best explanation for how our own brains make sense of the world, and thus, how machines can make sense of it, too.
Understanding objective functions in neural networks.
by Lars Hulstaert – 9 min read
Blog posts often explain optimisation methods such as stochastic gradient descent or variants thereof, but little time is spent explaining how objective functions are constructed for neural networks. Why are the mean squared error (MSE) and cross entropy log loss used as objective functions for resp.
InfoGAN – Generative Adversarial Networks Part III
by Zak Jost – 6 min read
In Part I the original GAN paper was presented. Part II gave an overview of DCGAN, which greatly improved the performance and stability of GANs.
The 10 Deep Learning Methods AI Practitioners Need to Apply
by James Le – 13 min read
Interest in machine learning has exploded over the past decade. You see machine learning in computer science programs, industry conferences, and the Wall Street Journal almost daily.
Robots Are Wrong Too – Confusion Mapping for the Worst Case
by Chris Butler – 8 min read
When was the last time a calculator didn’t do what you wanted it to? When was the last time that a person did? Algorithms like machine learning are in between these two from a deterministic standpoint.
