Learning and growing was only human nature until Artificial Intelligence had not arrived. But since the advent of Artificial Intelligence and its subfields like Machine Learning and Deep Learning the idea of Machine learning and growing has become common. We have moved from a stage to machines performing only the autonomous tasks to machines surpassing human intelligence. There are 2 terms when we talk about learning in Machines-Deep Learning and Reinforcement Learning which are very prominent in contemporary time and have their usage in almost each of the tasks performed by machines. We will try to cover these two terms of Machine Learning here and will try to understand in depth of what these leanings are and what differentiates them with each other. The article is divided into 3 parts-
- What is Deep Learning?
- What is Reinforcement Learning?
- What differentiates Deep Learning from Reinforcement Learning?
What is Deep Learning?
Any machine which tends to learn to solve tasks that require human intelligence in it requires some set of the training model. Example sets are provided to machines to help it understand what particular thing is about. Suppose you are trying to build an algorithm that lets your machine to identify a lion. To make your machines identify a lion you will have to produce some example images of a lion to it to make it familiar with the picture. After some example sets the machine using deep neural networks generates learning to identify a lion. The machine will identify images which are not of a lion in the future by looking at the example datasets and the learning gathered from those datasets. This is deep learning, where the machine is provided with the data for learning.
What is Reinforcement Learning?
Reinforcement learning is the learning by which humans learn the majority of his lifetime. In the majority of the life we are always taking adventures to learn things, sometimes fail, sometimes succeeds. Our hardest failures give us a life-changing motivation for not repeating that mistake again. This is the idea behind Reinforcement learning in machines. Machines tend to learn from the rewards given to it and accordingly learn if the same response is to be repeated or not. Machines using Reinforcement learning decide to repeat the set of responses in future if the rewards given to it matches the definition of ‚??being right‚?? and avoids those responses which are given bad responses by the instructor.
What differentiates Deep Learning from Reinforcement Learning?
The main difference behind Deep Learning from Reinforcement Learning is that while Deep learning uses example sets to achieve the desired results, Reinforcement Learning learns after every reward given to it. Machine using Reinforcement Learning learns from real-time feedback while deep learning makes use of the already given example datasets given to the machines.