A Deep (But Jargon Free) Dive Into Deep Learning
Learn what exactly deep learning is, how it works, and about its growing and innovative applications in healthcare, finance, retail, and more with this illustrated guide.
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By Dataiku
Image from: An Introduction to Deep Learning
Deep learning has been around for a while, so why has it just become a buzz topic in the last 5 years? Well, deep learning returned to the headlines in 2016 when Google’s AlphaGo program crushed Lee Sedol, one of the highestranking Go players in the world.Before there wasn’t a good way to train deep learning neural networks, but now with advancements in machine learning (ML) algorithms and deep learning chipsets, deep learning is being more actively implemented. Deep learning is being applied in healthcare, finance, and retail, and the global deep learning market is expected to reach $10.2 billion by 2025.
But what is it?
Put very simply, deep learning is a subset of machine learning that involves multiple layers of representations that allow a computer to learn and deduce outputs from data. While this sounds basic enough, what’s going on behind the scenes is anything but.
In fact, some of the controversy surrounding deep learning (and more particularly surrounding artificial intelligence, or AI) is the fear of the “black box.” That is, how can anyone base a service or product on deep learning and trust the decisions being made if no one knows how they’re being made? And what if the computer goes rogue (insert obligatory Terminator reference here)?
Whether you’re a business executive, a data scientist, or just a regular person, deep learning likely affects your life or has something innovative and beneficial to offer. Deep learning is a relevant and growing part of all our lives, but there aren’t that many basic guides on the topic. So we created one! You can get it here.
An Introduction to Deep Learning
In this guidebook, you’ll learn:
 What exactly deep learning is and why its growing
 Why it can be more powerful than classical machine learning (ML)
 How its implemented (through examples and infographics)
 Relevant reallife applications
 Trends and the future of the industry
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