We only understand a sliver of how the brain works, but we do know that it often learns through trial and error. We’re rewarded when we do good things and punished when we do the wrong ones; that’s how we figure out how to live. Reinforcement Learning puts computational power behind that exact process and lets us model it with software.
Source: Deep Ideas
If you remember anything from Calculus (not a trivial feat), it might have something to do with optimization. Finding the best numerical solution to a given problem is an important part of many branches in mathematics, and Machine Learning is no exception. Optimizers, combined with their cousin the Loss Function, are the key pieces that enable Machine Learning to work for your data.
This post will walk you through the optimization process in Machine Learning, how loss functions fit into the equation (no pun intended), and some popular approaches. We’ll also include some resources for further reading and experimentation.
The loss function is the bread and butter of modern Machine Learning; it takes your algorithm from theoretical to practical and transforms neural networks from glorified matrix multiplication into Deep Learning.
This post will explain the role of loss functions and how they work, while surveying a few of the most popular of the past decade.
Twitter isn’t just my favorite way to waste time when my boss isn’t looking––it’s also a powerful data source for understanding what your customers are saying about you. Getting access to Twitter data for analysis is easy with the Algorithmia platform, and this post will walk you through how to do just that.
Many of the exciting applications in Machine Learning have to do with images, which means they’re likely built using Convolutional Neural Networks (or CNNs). This type of algorithm has been shown to achieve impressive results in many computer vision tasks and is a must-have part of any developer’s or data scientist’s modern toolkit.
This tutorial will walk through the basics of the architecture of a Convolutional Neural Network (CNN), explain why it works as well as it does, and step through the necessary code piece by piece. You should finish this with a good starting point for developing your own more complex architecture and applying CNNs to problems that intrigue you.