This tutorial works through a step-by-step example of how to implement the Levenshtein distance in Python for word autocorrection and autocompletion.
Casimir Wierzynski talks to us about privacy-preserving machine learning and the future of data privacy in general.
This tutorial works through a step-by-step example of how the Levenshtein distance is calculated using dynamic programming.
Casimir Wierzynski talks to us about connectomics, automated brain slicing, and how we're setting out to map the mind.
From bank fraud to preventative machine maintenance, anomaly detection is an incredibly useful and common application of machine learning.
Terry Sejnowski talks to us about how artificial intelligence is shaping the future of education.
AdaBoost is a very popular boosting technique. Here we'll cover the AdaBoost algorithm, its pros and cons, and implement it in Python using scikit-learn.
Terry Sejnowski talks to us about machines dreaming, the birth of the Boltzmann machine, the inner-workings of the brain, and how we recreate them in AI.
Yoshua and Samy Bengio, Yann Lecun, Rich Sutton and Sergey Levine talk about the future of machine learning and how unsupervised learning methods will likely get us to human-level intelligence in machines.
