Machine Learning and Deep Learning both uses the algorithms fed into them. While in the first, the algorithm needs to be told how to make accurate prediction, in the latter, the algorithms are fed via neural networks, making the operation similar to a human brain and involving lower chances of mistakes as compared to Machine Learning. While Machine Learning gives result for a numerical and text field, Deep Learning also enables face, voice and handwriting recognition. Also, with new data fed into the system, the accuracy rates by Deep Learning are much more than by Machine Learning. Although Deep Learning is anyday better than Machine Learning, Machine Learning plays a vital role in the existing economy. Read more at https://www.analyticsindiamag.com/understanding-difference-deep-learning-machine-learning/

At the point when we plan the memory subsystem, we consider the design of human brains. So in some ways people aren't an incredible model for an analytic framework, and that is why current-day information researchers overlook the way our memory framework works. Designing the memory subsystem for an advanced analytics solution, we have to consider the engineering of our brains. The layer that we put on our operational data can be simple. We could put in an all-out operational systematic layer for transient choices taking into account current information. It needs time to see how our brains store and recover information and utilize that data as a model for creating the next logical framework. For more read the article written by John Weathington (author) : http://www.techrepublic.com/article/get-smarter-about-analytics-by-studying-memory-design/