Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic image priors from a large number of example images. In this paper, we show that, on the contrary, the structure of a generator network is…
Kevin Hughes' blog
By today’s standards, the Artificial Intelligence in Street Fighter 2 World Warrior isn’t very sophisticated. These days, when most people talk about AI they’re talking about mach…
Ever since Deep Learning hit the scene in speech recognition, word error rates have fallen dramatically. But despite articles you may have read, we still don’t have human-level speech recognition. Speech recognizers have many failure modes. Acknowledging these and taking steps towards solving them is critical to…
A guide to Natural Language Processing (NLP): how to analyze text, extract information and understand documents written in a natural language
Python Programming tutorials from beginner to advanced on a massive variety of topics. All video and text tutorials are free.
A detailed analysis
An extensive tutorial on how to implement a neural network in JavaScript with deeplearn.js to perform machine learning in JavaScript. You can bring neural networks to your browser by using the performance benefits of WebGL. Deeplearn.js makes it possible to implement a WebGL powered neural network ...
How we beat the state of the art to build a real-life A.I. app.
In the fall of 2016, I was a Teaching Fellow (Harvard's version of TA) for the graduate class on "Advanced Topics in Data Science (CS209/109)" at Harvard University. I was in-charge of designing the class project given to the students, and this tutorial has been built on top of the project I designed for the class.
Interest in machine learning may be at an all-time high. Per Google Trends, people are searching for machine learning nearly five times as often as five years ago. And at the University of California San Diego (UCSD),
Reinforcement Learning
Principal component analysis (PCA) is a technique used to emphasize variation and bring out strong patterns in a dataset. It's often used to make data easy to explore and visualize.
After seeing the poor feedback of Watson in Bridge Crew, I decided to take my four digital assistants for a spin. After 21 questions across four assistants, I learned that Alexa cannot give basic information about Amazon Prime videos, none of them can properly understand which movie you're looking f
Patrick Winston is one of the greatest teachers at M.I.T., and for 27 years was Director of the Artificial Intelligence Laboratory (which later became part of CSAIL ).
You may (or may not) have heard of or seen the augmented reality Invizimals video game or the Topps 3D baseball cards. The main idea is to render in the screen of a tablet, PC or smartphone a 3D mo…
Bayesian inference is a way to get sharper predictions from your data. It’s particularly useful when you don’t have as much data as you would like and want to juice every last bit of predictive strength from it.
For many of us, Bayesian statistics is voodoo magic at best, or completely subjective nonsense at worst. Among the trademarks of the…
