The impressive machine dispatched the reigning (living and breathing) Go champion 4-1 in the best-of-5 series. The Go board game, which originated in China, requires complex strategic thinking with the number of possible outcomes dwarfing that in chess. AlphaGo's win demonstrates the emergence of intuition with the abstract strategic thinking not mastered in previous artificial intelligence ventures. AlphaGo's systems include'deep learning' methods, allowing the machine to run thousands of simulated scenarios to build its "experiences" to use when playing the game for real. The use of neural networks allows problem-solving without any prior programming.
In machine learning, regularization is a approach used to combat high variance -- in other words, the issue of your model learning to reproduce the data, rather than the underlying semantics about your problem. In an analogous way to humans learning, the idea is to construct your homework problems to test and build for knowledge, rather than simply rote learning: for example, learning multiplication tables as opposed to learning how to multiply. This kind of phenomenon is especially prevalent in learning by neural networks -- with great learning capacity comes a large likelihood for memorization, and it is up to us practitioners to guide deep learning models into soaking up our problem, not our data. Many of you will have come across these methods in the past, and may have developed your own intuition for how different regularization methods affect the outcome. For those of you who don't (and even for those who do!) this article provides a visual guide for how neural network parameters are shaped by regularization.
I thought so too until I had no option but to adopt it and appreciate it's capabilities. Deep Learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in Artificial Intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as Deep Neural Learning or Deep Neural Network. Evolution of taking decisions -- a comparison of no-system based decisions, rule-based decisions, machine learning based decisions and deep learning based decisions.
Are you are a person who is bad at creating algorithms or logic to solve a particular problem? Neural Network is one of the most fascinating and mysterious concepts in machine learning. In a way, we can say that it can come up with the logic or algorithm on its own give the observations as input to it. If that is not enough let me tell you it is also known as the Black Box!! how spooky . It may be a little, overexaggerating.