In supervised learning, the most prevalent, the data is labeled to tell the machine exactly what patterns it should look for. Think of it as something like a sniffer dog that will hunt down ...

Digital assistants, like Siri, Cortana, and Alexa, are some of the most common examples of deep learning. They use NLP to respond to questions and adapt to user habits.

It would not only recognize what an image of a brain or a heart looks like, ... Nervana, meanwhile, runs deep- learning algorithms in the cloud and essentially offers deep learning as a service.

To today’s deep learning algorithms, they see just combinations of shapes and textures, not the presence of a battery indicating a portable power source and the combination with an LED ...

But with AlphaGo Zero, instead of having an algorithm look at lots of data from other players, Google taught the system the rules of the game and let the algorithm learn how to improve on its own ...

Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. Which algorithm works best depends on the problem.

As machine-learning algorithms grow more sophisticated, artificial intelligence seems poised to revolutionize the practice of science itself. In part, this will come from the software enabling ...

Deep learning finally allows machines to tackle problems of similar complexity to those humans can solve, and has been responsible for impressive AI achievements in recent years.

The book and materials look like a solid introduction to learning about the ideas behind these deep learning algorithms for anyone even mildly curious about the topic, and you can’t complain ...

Deep learning is a specific kind of ML, which leverages artificial neural networks – as opposed to algorithms – to glean patterns from complex or unstructured datasets.