The numerous leading-edge that lay within the ambit of artificial intelligence or AI as we may call it is garnering a lot of attention lately. Since the amount of data, we generate is ever-increasing, our AI sensibleness and the prospective of AI solving these problems too is growing with it. This remarkable computing power and the data that is now available for a considerable cost is the factor making reinforcement learning and deep learning possible and boosting the incredible growth in AI technologies. It can be challenging to keep up with the latest pioneering technologies in the AI industry as the changes are rapid. In this article, I am trying to provide simple definitions of reinforcement learning and deep learning that will help you to differentiate between these two concepts.
However, both reinforcement learning, as well as deep learning, are machine learning tasks that belong to a wider set of AI tools. The thing that makes this two concepts fascinating is that they authorize a computer to develop rules by itself to solve the problems.
What is deep learning?
Deep learning is the sine qua non self-teaching and autonomous system wherein you utilize the prevailing data to train algorithms and then find the patterns to make further predictions about the data.
For instance, you may perhaps train a deep learning algorithm to identify dogs on a photograph. That will be possibly done by feeding the deep algorithm millions of images that will or will not contain dogs. The program will then determine the patterns by clustering and classifying the image data (e.g. distances between the shapes, colors, edges, shapes, etc.). These patterns will then notify a prognostic model that is able to observe the new set of images and predict if they contain dogs or not, as per the model it has generated by using the training data.
Deep learning algorithms perform this by using various layers of artificial neural networks which imitate the neural network of human brains. Thus, allowing the algorithm to execute various cycles to track down the pattern precisely and enhance the predictions with each cycle.
Reinforcement learning is a self-teaching and autonomous system that basically learns by trial and error method. It performs the task with the goal of boosting it rewards or in other terms, it is learning by doing to achieve the best results of its actions. It resembles the way we learn things like riding a bicycle wherein we fall off a lot and make heavy and erratic moves, but over the course of time we analyze our actions and learn how to ride the bicycle.
The same logic applies when computers use reinforcement learning. They experiment with different actions and through the feedback they learn whether that particular action delivered a better result or not. After analyzing they reinforce the actions that worked and yielded a satisfying result, i.e. altering, and revising its algorithm independently over various rehearsal until it makes the decision that delivers the best result.
Difference between deep learning and reinforcement learning
Reinforcement learning and deep learning are both systems that learn independently. The only difference between them is that reinforcement learning is animatedly learning by fine-tuning actions based in continuous feedback to yield best results, while deep learning is learning from a training set and then applying that learning to a new data set.
However, these both concepts aren’t equally complete. As a matter of fact, we might end up using deep learning in a reinforcement learning system, which is known as deep reinforcement learning— a topic that I will cover in my next article.