Advances in deep learning are picking up tremendous momentum—from the development of specialized software to major breakthroughs in hardware capabilities. This ebook looks at what deep learning has accomplished so far and where it’s likely to go from here.

From the ebook:

Deep learning is a subset of machine learning. It uses some ML techniques to solve real-world problems by tapping into neural networks that simulate human decision-making. Deep learning can be expensive and requires massive datasets to train itself on. That’s because there are a huge number of parameters that need to be understood by a learning algorithm, which can initially produce a lot of false-positives. For instance, a deep learning algorithm could be instructed to “learn” what a cat looks like. It would take a massive data set of images for it to understand the minor details that distinguish a cat from, say, a cheetah or a panther or a fox.

In March 2016, a major AI victory was achieved when DeepMind’s AlphaGo program beat world champion Lee Sedol in four out of five games of Go using deep learning. The deep learning system worked by combining “Monte-Carlo tree search with deep neural networks that have been trained by supervised learning, from human expert games, and by reinforcement learning from games of self-play,” according to Google.

Deep learning also has business applications. It can take a huge amount of data—millions of images, for instance—and recognize certain characteristics. Text-based searches, fraud detection, spam detection, handwriting recognition, image search, speech recognition, Street View detection, and translation are all tasks that can be performed through deep learning. At Google, deep learning networks have replaced many “handcrafted rule-based systems.”