When it comes to AI, machine learning is seen as a part of it. Machine learning is the computer algorithm study that automatically improves by data usage and experience. Its algorithm generally builds a model based on sample data or known as training data.
Algorithms of machine learning are used in a range of applications for instance email filtering, computer vision, medicine, and speech recognition. Deep and reinforcement learning is two of the algorithms which come under machine learning. In this article, the main focus is on differentiating deep learning and reinforcement learning.
Deep Learning vs Reinforcement Learning
The main difference between deep learning and reinforcement learning is their learning techniques. With the help of a training set and further applying that learning to a new set of data is called deep learning. On the other hand, reinforcement learning can be done through action adjusting based on continuous feedback for maximizing a reward.
Deep learning teaches the computers to do what comes to humans naturally: learn by example. It is a key technology at the back of driverless cars, from a lamppost to distinguish a pedestrian, or enabling them to figure out a stop sign. It is the key in consumers’ devices to voice control like tablets, hands-free speakers, TVs, and phones.
Reinforcement learning is taking suitable action in a particular situation to maximize reward. It is employed by several machines and software to find the best possible path or behavior it should take in a particular situation. The decision is independent in reinforcement learning, so labels are given to sequences of dependent decisions.
Comparison Table Between Deep Learning and Reinforcement Learning
| Parameters of Comparison | Deep Learning | Reinforcement Learning |
| Origin | In 1986 | In the late 1980s |
| Introducer | Rina Dechter | Richard Bellman |
| Also called | Deep structured learning or hierarchical learning | None |
| Data existence | Already existing data set required to learn | As it is exploratory it does not need a current data set for learning |
| Utilization | In speech and image recognition, dimension reduction task, and deep networking pretraining. | In telecommunications, robotics, computer games, elevator scheduling, and health care AI. |
What is Deep Learning?
Deep learning is a type of AI and machine learning that imitates the way humans gain certain kinds of knowledge. When it comes to data science, deep learning is a vital element that consists of predictive modeling and statistics. To data scientists, deep learning is extremely beneficial who are tasked with interpreting collecting, and analyzing data.
Through a combination of data inputs, bias, and weights, deep learning artificial neural networks, or neural networks, attempts to mimic the human brain. The algorithms in traditional machine learning are linear, while algorithms of deep learning are stacked in a hierarchy of increasing abstraction and complexity.
Deep learning using programs of computer go through much the exact process as the toddler learning to identify the cat. In the hierarchy, each algorithm applies a transformation of nonlinear to its input. Then uses what it learns for the creation of a statistical model as output.
Unless the output has reached a level of accuracy which is acceptable until the iterations continue. The layers in deep learning permitted to be heterogeneous as well as to deviate widely from models of biologically informed connectionists for the sake of trainability, efficiency, and understandability.
What is Reinforcement Learning?
Reinforcement learning generally performs the actions to maximize rewards. In simple terms, learning is done by doing something to attain consequences in the best terms. This is just like learning things such as bike riding in which we learn by falling in beginning.
With the user feedback, what failed and what worked overtime to fine-tune action, and grasp to ride a bike. Just like this computers use learning of reinforcement and try distinctive actions, through the feedback, they learn and at last reinforce the worked actions.
For instance, its algorithm is modified and reworked autonomously over many iterations unless decisions are made through which best results are delivered. Robot learning to walk is one of the instances of the algorithm, namely reinforcement learning. At first, a step forward is tried by a robot that is large enough and falls.
The fall outcome is a data point which is a big step the system responds to reinforcement learning. Because the fall is an outcome that worked as negative feedback used to adjust the system to attempt a smaller step. Finally, the robot is capable to move forward.
Main Differences Between Deep Learning and Reinforcement Learning
- When it comes to algorithm teachings, deep learning uses current information to look for pertinent patterns. On contrary, reinforcement learning usually figures out predictions by error and trial.
- Deep learning application is more often on recognition and tasks with area reduction. On the flip side, reinforcement learning is generally linked with the interaction of the environment with optimal control.
- In terms of examples, the Amazon credit card fraud system is the instance for deep learning in which neural networks are built by using the obtained data from purchases of online credit cards. Conversely, a walking robot is an instance of reinforcement learning in which actions are defined by how high it should lift the leg.
- Deep learning is generally less associated with the interaction. In comparison, reinforcement learning is closer to the human brain’s capabilities as through feedback this type of intelligence can be improved.
- Learning techniques included in deep learning are analyzing data that already exists and learning applied to a new data set. In contrast, reinforcement learning techniques include learning from mistakes as well as maximizing rewards.
Conclusion
It can be concluded that deep and reinforcement learning are linked highly with artificial intelligence’s computing learning. The origin can be traced to 1986 of deep learning which was introduced by Rina Dechter. In contrast, the origin of reinforcement learning can be traced the to late 1980s which was introduced by Richard Bellman.
The utilization of deep learning is in speech and image recognition, dimension reduction task, and deep networking pretraining. On contrary, reinforcement learning can be utilized in telecommunications, robotics, computer games, elevator scheduling, and health care AI. Deep learning is also known as hierarchical or deep structured learning, while there is no other term for reinforcement learning.
References
- https://books.google.com/books?hl=en&lr=&id=omivDQAAQBAJ&oi=fnd&pg=PR5&dq=deep+learning+&ots=MNQ_ipnCSR&sig=yeqmpT4zod7fgti0YqbcLj7nmik
- https://books.google.com/books?hl=en&lr=&id=uWV0DwAAQBAJ&oi=fnd&pg=PR7&dq=reinforcement+learning+&ots=mirEv1Z4o6&sig=zsp-E9V5ghtGvtAhaGwlCkbqJCM