Amazon’s MXNet Deep Learning Framework

Amazon’s MXNet Deep Learning Framework

Siraj Raval takes a look at how Amazon's MXNet Deep Learning framework stacks up to other deep learning frameworks. It's got an imperative programming API... Details
Logistic Regression Explained

Logistic Regression Explained

Here's a fun little video explaining logistic regression. Details
The AI that Sings

The AI that Sings

In keeping with today's musical theme, Two Minute Papers covers the paper "A Neural Parametric Singing Synthesizer" which talks about creating believable singing given a... Details
Living with a mind-controlled robot arm

Living with a mind-controlled robot arm

Rapidly, science fiction is becoming science fact. The first person to live with a mind-controlled robotic arm is Johnny Matheny—a man who lost his original... Details
Pruning Neural Networks

Pruning Neural Networks

Two Minute Papers covers a paper on pruning neural networks to make them faster and more accurate. [Link to research paper] Details
Can Dubai Use Robots to Police?

Can Dubai Use Robots to Police?

Robocop was a classic 80s action movie and now, it seems somewhat prophetic. BBC Click takes a closer look. Details
The Code Behind Deep Learning for Music Generation

The Code Behind Deep Learning for Music Generation

In this episode of the AI show Erika follows up on her previous episode by showing the actual code behind training and using the music... Details
Client-side Machine Learning vs Server-side Machine Learning

Client-side Machine Learning vs Server-side Machine Learning

In this talk from NodeConf EU 2017, Nikhila Ravi explains the advantages of client-side machine learning vs. server-side machine learning. Details
Real Time Data Science with Azure Cosmos DB

Real Time Data Science with Azure Cosmos DB

Here's a great talk on how to use CosmosDB in conjunction with Apache Spark. Details
How to Read Math Equations

How to Read Math Equations

Since I started doing Data Science a few years ago, I have used more advanced mathematics than I ever thought I would have. And, as... Details