Machine learning is the study of machine algorithms which can improve automatically by the application of statistical data and through human experience. It is regarded as a sub-field of artificial intelligence. It was used for tasks like speech recognition, language processing and pattern identification. The applications of machine learning are wide and varied. It helps businesses to make decisions by identifying, modeling and managing the business data in a systematic manner.
The Machine learning technique is generally supervised one, which means it uses some sort of a labeled algorithm or a database to help it decide an outcome. This may be a decision based on some kind of labeled data or it could also be a supervised one where it randomly makes a decision. In both the cases, the system has to adapt to the current situations. Deep reinforcement is used for the supervised learning. The deep reinforcement is basically trained using a reinforcement learning scheme.
The two main types of machine learning are – supervised learning and unsupervised machine learning. In supervised machine learning the data that is fed to the system is labeled with relevant keywords so that the system can make an educated guess at the right keyword. And in unsupervised learning, the system doesn’t receive any kind of information and it just makes guesses at the relevant labels.
The supervised learning gives good results with more accuracy and a faster time. But the drawback with supervised learning is that the system can’t make any mistakes even if it receives bad and unlabeled training data. And also it takes more time to train a system than in unsupervised learning. That’s the reason why many machine learning researchers are trying to find a way to combine the two.
The deep learning algorithm learns independently from the data it is given and this is why it’s faster than supervised learning. In deep learning the machine learns how to make an intelligent decision based on the output it receives from multiple previously defined rules or procedures. It’s the ability of deep learning algorithms to learn new things without being guided by any kind of parameters. Deep neural networks have recently fascinated many Machine Learning Researchers due to its capacity of learning on its own. This ability of deep neural networks give it the potential to generalize previous inputs and create a completely unique output.
But what kind of self-driving cars would be possible with this kind of intelligence? Researchers have already created very complex systems with a high level of self-learning ability that could drive itself on streets without any human intervention. Such technology could revolutionize the transportation industry, as it would let a machine follow the traffic lights, detect red flags, and prepare the road for the next driver on the same system. Such a system would not only reduce traffic accidents, but it could also minimize or eliminate the need for human drivers.
Although such technologies may seem to be far fetched, computer scientists have already used semi-supervised and unsupervised learning methods to train a machine to recognize handwritten numbers, complete with its own complex calculations, and to solve basic image recognition tasks. The key to these methods was the development of what is called an Artificial Intelligence (AI) computer which was trained through a supervised learning process. In semi-supervised and unsupervised learning, the machine learning algorithm learns through trial and error from receiving data, while the artificial intelligence program improves through a supervised learning procedure.
The Machine Learning research has also provided much-needed momentum for reinforcement learning works. Basically, reinforcement learning works by giving positive reinforcement to the right behavior of the user or a model, if it behaves appropriately. A simple example is if a toddler spilled milk on the floor; the parents would most likely give a food item to the toddler in order to stop the action. The system monitors the behavior of the user or the model and, if it acts appropriately, gives a treat. With further training, the system can learn to anticipate the appropriate reward, which saves the user or the model a lot of labor.