Therefore, there is a lot of software has been developed to support human in order to be able to manage and protect their systems effectively. Initially, these software has been developed to handle some operations like mathematical calculations which seem very complex for human being. And then we need more. Next step was extending the ability of software using artificial intelligence and machine learning techniques. As technology advances, huge amount of data is being produced to be processed every day and every hour. Finally, the concept of “Big Data” was born and people began to need more intelligent system for processing and getting make sense of these data. For this purpose there are a lot of algorithms have been developed until today. These algorithms are used for many research area such as; image processing, speech recognition, biomedical area, and of course cyber security domain.
Beside all of these, basically the main purpose of Machine Learning techniques is providing decision mechanism to software as people do. Cyber security domain is one of the most important research area worked on. The Centre for Strategic and International Studies in 2014 estimated annual costs to the global economy caused by cybercrimes was between $375 billion and $575 billion. Although sources differ, the average cost of a data breach incident to large companies is over $3 million. Researchers have developed some intelligent systems for cyber security domain with the purpose of reducing this cost.
Machine Learning techniques are used wide range of application area in globe. So that every human use countless intelligent systems which are developed using machine learning techniques countless time in a single day. When using mobile phone, surfing on the internet, buying something on the internet, we are facing a lot of intelligent systems. Companies that develop technology have spent huge amount of money for developing more intelligent systems. Almost all machines will be intelligent in the future, because intelligent systems make life easier. And of course, people love applications that make life easier.
Application Areas in Daily Life
Another well known machine learning application area is the activity recognition. The main purpose of this type of application is detecting which activity performed by user at certain time. This process can be done on the mobile phone or some external devices such as smartwatch. Big mobile phone producers research on this topic heavily. Such big companies Apple and Samsung has mobile application for activity recognition which is one of the default application their phones. For the develop intelligence system for activity recognition, it is needed informations which is produced by sensors. Accelerometer, gyroscope and GPS sensors are most commonly used sensor in this area. It is used machine learning techniques to detect which activity performed by user. This type of applications can give us informations about burned calorie, how many kilometer walked or how healthy the user’s daily life is.
Machine learning can also be used for prediction about future. For example, in weather forecasting applications current weather data and past data processed and gathering information about future weather conditions. Another example of prediction is atm cache optimization. The money which is located on atm is not useful for a bank when that money is not being used by customers. In this situation money neither useful for customer nor bank. If it is developed an intelligent system to predict optimum money for atm weekly or monthly, banks can use that money for other purposes. In a recent study, banks can double the number of ATMs without changing the total amount of money in overall ATM’s using an intelligent system that estimates the optimum amount of money in ATMs. Some other example is house price prediction. In this type of problem, system try to predict actual value for house using information about house, house location, knowledge of nearby transportation vehicles or land value like informations. There are so many other examples of forecasting about future.
What is Deep Learning?
Startup Deep Genomics, which is backed by Bloomberg Beta and True Ventures among others, has fed deep learning machines tons of existing cellular information in order to teach machines to predict outcomes from alterations to the genome, whether naturally occurring or through medical treatment. The technology could provide the most precise understanding of an individual’s specific disease or abnorm ality and how that person’s well being can best be advanced.
A more devices become internet-enabled, hackers have an increasing number of entry points to infiltrate systems and cloud infrastructure. The best cybersecurity practices not only create more secure systems but can predict where the next attack will come from. This is critical since hackers are always on the hunt for the next vulnerable endpoint, so protecting against cyber attack requires “thinking” like a hacker. Companies like Israel-based and Blumberg Capital-based Deep Instinct aim to use deep learning in order to recognize new threats that have never been detected before and thus keep organizations one step ahead of cyber criminals.
There are already plenty of cars on the road with driver-assistance capabilities, but these cars still rely on users to take over when an unforeseen event occurs that the car isn’t programmed to respond to. As Sameep Tandon of startup Drive.ai notes, the challenge with self-driving cars is handling the “edge cases,” such as weather. This is why, using deep learning, Drive.ai plans to help the car build up experience through simulations of many kinds of driving conditions. Nvidia is also working on self-driving car technology. Nvidia says it has used deep learning to train a car to drive on marked and unmarked roads and along the highway in various weather conditions, without the need to program every possible “if, then, else” statement. In this sector, Google and Many of the big companies in the automobile industry are doing research on driverless cars.
Since deep learning has already seen widespread experimentation and refinement for textual analysis, it’s no surprise that Google, the leader in search, has made widespread deep learning-based updates to its search technology. Google’s deep learning-based RankBrain technology was added to how Google manages and fills search queries back in 2015. The technology helps handle queries that have not been seen before.
Machine learning problems can be divided into three main categories according to the characteristics of the problem. These are supervised learning, unsupervised learning and reinforcement learning. Supervised learning techniques divided into two subcategories as Classification and Regression. In classification problem, we have completely divided classes and main work is defining test sample to find the class which actually belongs to. When our dataset classes are not separate, so it means we have continuous data, this type of problems are called regression problems.
Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Generally this type of techniques are used in robotic application areas.
Finally there is one more thing we want to explain about learning. Learning can be done at once (batch learning) or can be done continuously (incremental learning).