Introduction | Artificial Intelligence and depth of learning

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Last issue we introduced

1.2 Artificial Intelligence and depth of learning

Before entering book learning, artificial intelligence we got off his relationship with the depth of learning, which helps us to better grasp the overall depth study of knowledge.

As early as 1956, the concept of artificial intelligence was that people made. From concept to become a reality, artificial intelligence experienced two ups and downs, regarded as the pearl from then to avoid any rush, which is accompanied by revolutionary technologies: computing and data storage resources become cheaper and became " gold ", which are driving the development of artificial intelligence.

Artificial Intelligence by definition is given artificial machine intelligence, but AI pioneers envisioned given machine independent thinking "strong artificial intelligence" capabilities are not the same now we are talking about artificial intelligence are "weak artificial intelligence." It can like people to realize some of the established tasks, such as face recognition, spam classification, and sometimes even surpass humans.

Deep learning is a kind of artificial neural networks have been proposed by bionics creativity. These people through training the neural network, so well done a lot of machine learning tasks. Therefore, the depth of learning is a major breakthrough in the history of artificial intelligence, it expanded the scope of the field of artificial intelligence and promote the development of artificial intelligence.

Shown in Figure 1.2, AI learning machine wrapped, wrapped deep learning machine learning. Simply put, artificial intelligence is a concept that machine learning is a method of artificial intelligence, and deep learning is a technology of machine learning.

Figure 1.2 Artificial Intelligence and deep learning graph

The method, of course, implement machine learning including but not limited deep learning, reinforcement learning as a conventional machine learning technique or the like. Depth study has recently been receiving more and more attention of researchers, and has successfully been used in many real-world applications.

Deep learning algorithm may automatically extract data by a method wherein the supervised learning, the learning rule of the data and further data from the mass. In contrast, traditional machine learning methods need to manually design features, thus increasing the burden on the user, which makes deep learning beyond the traditional machine learning. We can think of the depth of learning is learning algorithms based on massive amounts of data in machine learning.