Machine Learning is about making predictions. This post will give an introduction to Machine Learning through a problem that most businesses face: predicting customer churn.
ML can help predict which of your customers are at risk for leaving in advance, and give you an edge by pre-empting with action.
Serverless architecture is making cloud deployment even easier by removing the need to design your own server-side systems. Integrated properly, this paradigm can get your applications out the door faster and free up company resources to build more.
In a nutshell, serverless, also called Functions as a Service (FaaS), is a further abstraction on what cloud computing platforms like AWS already do—making it easier than ever to get your applications up and running at scale. Serverless takes the power of a hosted cloud to a software level – it abstracts away the entire concept of the server. Instead, you just write functions. The provider takes care of how and where to run those functions, ensuring that you focus on code and not the hardware and systems that operationalize that code.
If you’re trying to create value in your company through machine learning, you need to be using the best hardware for the task. With CPUs, GPUs, ASICs, and TPUs, things can get kind of confusing.
For most of computing history there was only one type of processor. But the growth of deep learning has led to two new entrants into the field: GPUs and ASICs. This post will walk through the different types of compute chips, where they’re available, and which ones are the best to boost your performance.
Source: Frontiers in Psychology
You expect employees to have high levels of emotional intelligence when interacting with customers. Now, thanks to advances in Deep Learning, you’ll soon expect your software to do the same.
Research has shown that over 90% of our communication can be non-verbal, but technology has struggled to keep up, and traditional code is generally bad at understanding our intonations and intentions. But emotion recognition – also called Affective Computing – is becoming accessible to more types of developers. This post will walk through the ins-and-outs of determining emotion from data, and a few ways you can get some emotion recognition and running yourself.
The best businesses understand sentiment of their customers – what people are saying, how they’re saying it, and what they mean. Sentiment Analysis is the domain of understanding these emotions with software, and it’s a must-understand for developers and business leaders in a modern workplace.
As with many other fields, advances in Deep Learning have brought Sentiment Analysis into the foreground of cutting-edge algorithms. Today we use natural language processing, statistics, and text analysis to extract, and identify the sentiment of text into positive, negative, or neutral categories.