Unexplored markets and an almost unlimited potential mean that today’s machine and deep learning-based startups will be tomorrow’s tech pioneers, marketing gurus, financial advisors, and more. But as anyone who has ever been involved in a startup will tell you, it’s never that easy to take a promising concept and channel it into long-term success. The old adage of ‘knowledge is power’ still holds true today, and machine and deep learning provide a unique opportunity for organizations to gain insight, boost innovation, and unlock the potential of their data. In essence, startups can use machine and deep learning algorithms to significantly impact everything within an organization, from IT system stability to the bottom line. But where do we begin?
In this report, we’ll address everything from how to choose a framework and pick the kit you need to get started, to the questions you’ll be asking yourself, and the benefits of immersing yourself in the machine and deep learning communities. We also untangle the jargon and explore what these terms actually mean.
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.
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