FPGAs are the next wave of hardware innovation to transform machine learning. In this session from Build 2018, take a walk through how we are using FPGAs today inside of Microsoft, and how you can start using them today as well.
ML.NET is aimed at providing a first class experience for Machine Learning in .NET. Using ML.NET, .NET developers can develop and infuse custom AI into existing .NET apps through a code-driven and UI driven approach.
ML.NET has been used extensively within Microsoft by Windows, Azure, SQL & Bing for a decade and now these capabilities are available to all .NET developers.
Brandon Rohrer explains autocorrelation and partial autocorrelation, complete with pictures and Python code.
Siraj Raval explains the importance of math to machine learning.
In this episode of the AI Show, machine learning is gently introduced from the standpoint of the algorithm and model. It starts with the simplest machine learning, linear regression, and ends on a dense neural network explaining the similarities in plain terms along the way to bring the audience up to speed on neural networks in a fun way. Watch this episode to discover what machine learning algorithms really are in 10 minutes or less.
Here’s a fun little video explaining logistic regression.
In this talk from NodeConf EU 2017, Nikhila Ravi explains the advantages of client-side machine learning vs. server-side machine learning.
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 I take the DeepLearning.ai classes, that use of mathematics has accelerated.
Given that the math side of Data Science and AI is not going away any time soon, it’s probably best to get good at it.
Fortunately, for all of us Siraj Raval explains how to read math equations in a way that only he can: quickly and awesomely.
The great Brandon Rohrer explains how Supoort Vector Machines (SVMs) work in this video tutorial.