A Developer’s Guide to Launching a Machine Learning Startup Posted on : Aug 16 - 2017

While artificial intelligence (AI), machine learning and deep learning are often thought of as being interchangeable, they do in fact relate to very different concepts. It all began in the 1950s with AI and the idea that a computer could be made to simulate human learning and intelligence.

A subclass of that is machine learning, whereby a computer can take large amounts of data and use it begin to recognize patterns, make predictions on new data, and essentially ‘learn’ for itself. The drawback is that machine learning requires that parameters be set for what the computer needs to recognize, and those inputs can be me-consuming. And so we go one step further, into deep learning.

For example, Ripjar offers a service under the heading of ‘Analysis at the Speed of Thought’ that utilizes deep learning combined with natural language processing to analyze an organization’s internal data, in addition to information from sources like news feeds, web pages, and social media posts. These data streams are captured and monitored in real-time, in more than 160 languages, in order to provide cybersecurity, reputa on management, compliance, etc. Without the capabilities of deep learning, the inputs required to get results would prove incredibly difficult. In essence, deep learning is enabling the practical application on of machine learning. So how does it work?

Inspired by the structure and activity of neurons within the human brain, deep neural networks (DNN) form the basis of deep learning. Through these algorithms, computers are able to identify features in significantly sized datasets and progress that information on through layers of the neural network, refining as it goes. This leads to a hierarchical representation of the problem.

Developer Considerations for Machine Learning

There are many reasons why startups might struggle to fulfill their potential for financial and technological success. Among the many unique challenges they face from initial concept through to expansion, a lack of scalability can be one of the most difficult to overcome. In this section, we’ll focus on the capabilities and practical application of machine and deep learning, the frameworks and technologies you need to know about, and the ways that the community can help from the very beginning. View More