The Internet might be filled at the brim with a lot of information about training as well as evaluating the recommenders, but too much info could overwhelm you. Are you looking to get a clear understanding of the ways to beat different challenges revolving around a full-scale system? If yes, then start reading:
- Dynamic Prediction
When you are dealing with a large user-base dimensionality, it could be a big deal to pre-compute every recommendation.
Even though cats and dogs are different animals, what is the point of distinction between them? You might term the ability to distinguish between a cat and a dog to be common sense, but it is described as deep learning. People are not programmed to recognize different attributes in an object by inputting external information. These capabilities are inherent and cannot be induced through external stimuli, and thus are unnoticeable for us humans.
Computers, on the other hand, need gradual feeding- in the form of deterministic algorithms- to detect even the most simple judgments. Despite the surge in machine learning and connectivity, a computer cannot do what a toddler does unintentionally. The following are the developments in deep learning:
- Over the past six years, deep learning, which is a branch of artificial intelligence, has made tremendous progress, taking inspiration from the neural networks of the human brain. Facebook’s AI lab has built a system that can answer basic questions, to which it has never been exposed to. Amazon’s smart speaker, Echo uses deep learning as well. And three years back, Microsoft’s chief research officer took everyone by surprise at a lecture in China where the machine used deep learning to translate his English into Mandarin with the output in his own voice and an error rate of only 7%. Continue reading