In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions. Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.
First, some intuition
Let’s say you had to determine whether a home is in San Francisco or in New York. In machine learning terms, categorizing data points is a classification task.Since San Francisco is relatively hilly, the elevation of a home may be a good way to distinguish the two cities. Based on the home-elevation data to the right, you could argue that a home above 240 ft should be classified as one in San Francisco.
Adding another dimension allows for more nuance. For example, New York apartments can be extremely expensive per square foot. So visualizing elevation and price per square foot in a scatterplot helps us distinguish lower-elevation homes. The data suggests that, among homes at or below 240 ft, those that cost more than $1776 per square foot are in New York City. Dimensions in a data set are called features, predictors, or variables.
You can visualize your elevation (>242 ft) and price per square foot (>$1776) observations as the boundaries of regions in your scatterplot. Homes plotted in the green and blue regions would be in San Francisco and New York, respectively.
Identifying boundaries in data using math is the essence of statistical learning. Of course, you’ll need additional information to distinguish homes with lower elevations and lower per-square-foot prices. The dataset we are using to create the model has 7 different dimensions. Creating a model is also known as training a model. On the right, we are visualizing the variables in a scatterplot matrix to show the relationships between each pair of dimensions.
There are clearly patterns in the data, but the boundaries for delineating them are not obvious.
And now, machine learning
Finding patterns in data is where machine learning comes in. Machine learning methods use statistical learning to identify boundaries. One example of a machine learning method is a decision tree. Decision trees look at one variable at a time and are a reasonably accessible (though rudimentary) machine learning method.
What you will find in the full article:
- Finding better boundaries
- Your first fork
- The best split
- Growing a tree
- Making predictions
- Reality check
To check out all this information, and play with a few cool interactive visualizations, click here.
The Netflix API is based on a dynamic scripting platform that handles thousands of changes per day. This platform allows our client developers to create a customized API experience on over a thousand device types by executing server side adapter code in response to HTTP requests. Developers are only responsible for the adapter code they write; they do not have to worry about infrastructure concerns related to server management and operations. To these developers, the scripting platform in effect, provides an experience similar to that offered by serverless or FaaS platforms. It is important to note that the similarities are limited to the developer experience (DevEx); the runtime is a custom implementation that is not designed to support general purpose serverless use cases. A few years of developing and operating this platform for a diverse set of developers has yielded several DevEx learnings for us…
In Part 1 of this series, we outlined key learnings the Edge Developer Experience team gained from operating the API dynamic scripting platform which provides a serverless or FaaS like experience for client application developers. We addressed the concerns around getting code ready for production deployment. Here, we look at what it takes to deploy it safely and operate it on an ongoing basis…
GeekyAnts has been working with React Native since its launch in 2015 and with many awesome apps to our credit, we are proud to say that we are one of the top React Native companies out there! 🎉 🎆
Over the years, we have noticed a few issues in React Native apps that make us want for “something more”.
Our search for this “something more” has led us to Google’s Flutter!
It’s no surprise that cloud computing has literally taken the world by storm. For most businesses and enterprises, gone are the days of struggling with complicated on-premise server rooms and complicated networking. Over the past decade, cloud computing has become more cost-efficient, secure, and reliable. The major providers in the industry are now investing heavily in their hardware, software, and global networking infrastructure to obtain more market share, which has resulted in unparalleled performance. Healthy competition is always a win for consumers and partners as this drives costs down and requires them to constantly innovate to stay ahead.
Typically when we think of cloud computing providers we are referring to the three giants in the industry: Azure, Google Cloud, and AWS. Today we’re going to compare just two of them, Google Cloud vs AWS. We exclusively utilize Google Cloud Platform here at Kinsta, but we’ll try to keep this article as unbiased as possible and explain everything in layman’s terms. There are definitely some advantages and disadvantages to both providers. Trust us, we’ve had our own share of challenges! No matter which provider you choose, you’ll always encounter issues at some point along the way.
…Now, eight years after the first blockchain was built, people are trying to apply it to procedures and processes beyond merely the moving of money with varying degrees of success. In effect, they’re asking, What other agreements can a blockchain automate? What other middlemen can blockchain technology retire?
Can a blockchain find people offering rides, link them up with people who are trying to go somewhere, and give the two parties a transparent platform for payment? Can a blockchain act as a repository and a replay platform for TV shows, movies, and other digital media while keeping track of royalties and paying content creators? Can a blockchain check the status of airline flights and pay travelers a previously agreed upon amount if their planes don’t take off on time?
If so, then blockchain technology could get rid of Uber, Netflix, and every flight-insurance provider on the market…
Today we’re excited to launch Cloud Firestore, a fully-managed NoSQL document database for mobile and web app development. It’s designed to easily store and sync app data at global scale, and it’s now available in beta.
Key features of Cloud Firestore include:
- Documents and collections with powerful querying
- iOS, Android, and Web SDKs with offline data access
- Real-time data synchronization
- Automatic, multi-region data replication with strong consistency
- Node, Python, Go, and Java server SDKs
And of course, we’ve aimed for the simplicity and ease-of-use that is always top priority for Firebase, while still making sure that Cloud Firestore can scale to power even the largest apps.
Super stoked to share the latest version of our JS client for the Slack Web API! You can find the source code here and the npm distribution here.
Speed is 🔑
Apps built for Slack by their nature are real time. All facets of speed are critical factors for creating a great user experience. And with performance as our guide I am very pleased to say we support the entire Slack Web API in a 7kb (not gzip’d) payload that has been solidly tested for all LTS versions of Node and modern evergreen browsers.
Being so tiny means this library loads super fast which makes it perfect for AWS Lambda and browser-based applications where cold start responsiveness is critical.