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Deep learning is part of a larger group of machine learning methods that are based on artificial neural networks. These systems teach themselves to perform tasks much in the same way a human learns. They do this by examining large pools of data and looking at decision-making criteria in order to apply task-specific rules. Machine learning and deep learning have evolved to the point that they are changing the entire software development life cycle model. 

The standard life cycle model follows a cyclical pattern of user requirement, analysis, design, development, testing, deployment, and maintenance, with many iterations in-between as changes are made to the software. With machine learning, all the focus is on the data. After the problem is defined and the goals established, that’s when the data collection can begin. After collection, the data is prepared and sorted into categories. The model begins “learning” from that sorted data. The model is then deployed and integrated, this is followed by model management. Algorithms are essentially being trained by data. This impacts the way software performs. It is called software 2.0 because of the way the programming has surpassed the ability of humans, now becoming so complex that a computer is required to program another computer. It’s also much easier to set rules and boundaries and collect data than it is to write thousands of lines of code in order to create a program to solve a problem. 

It would be highly improbable, however, to assume that traditional programming models will be disappearing in favor of machine learning. There are too many existing systems created the standard way to ever consider recreating the wheel with most of them. There are, however, ways in which these two paradigms can work together in the future.

There are several ways to do this. One way is to let machines do the prototyping once a software model is created. Smart programming assistants, such as Kite for Python or Codota for Java, can save developers countless hours of having to debug code or comb through endless documentation. They can offer just-in-time recommendations as well as support. During the development phase of the product life cycle, intelligent assistants can identify common errors and flag them immediately. 

Changing with the times and keeping up with technology is a way of life. When software needs to be upgraded, machine learning can help save programmers the time and hassle of updating by automating the process.