The technology wave that is sweeping the globe testifies to the possibilities in domains that were once thought to be impossible. Artificial intelligence has a large presence around the world, as businesses embrace the use of AI to automate their operations and increase productivity. Machine learning is a tiny part of Artificial Intelligence. Machine learning algorithms can simplify life and work by removing duplicate chores and allowing us to work faster and smarter than entire teams of people. There are, however, various types of machine learning, (reinforcement learning and deep learning, for example). The processing potential of AI is also quite strongly linked to both deep and reinforcement learning. It might be difficult to stay up with the latest cutting-edge technologies in the AI business, given the frequent developments. So, let’s dig a little deeper to get some information about deep learning and reinforcement learning and comprehend the differences.
What is Deep Learning?
Deep learning is a self-teaching system in which current data is used to train algorithms to detect patterns, which are then used to make predictions about new data. To put it simply, it is the process of creating machines that think intelligently by analyzing previous data. This entails developing gadgets that function similarly to the human brain. Individuals learn and respond in most circumstances based on prior experiences, whether their own or someone else’s. DL algorithms do this by simulating the network of neurons in our brain with multiple layers of artificial neural networks. This enables the algorithm to go through a series of cycles in order to narrow down patterns and improve predictions with each cycle.
DL bioinformatics is probably one of this algorithm’s most notable breakthroughs; others include car automation, speech recognition, and gameplay, among others. Google has used deep learning to lower power use by a significant amount, saving millions of dollars in the process. DL is also in charge of Google Translate’s metamorphosis or upgrading, as the case may be. It was employed by Facebook for image recognition. Apple’s Face ID is another terrific example of deep learning in action, wherein when you first set up your phone, you scan your face to train the algorithm.
What is Reinforcement Learning?
Reinforcement learning is a self-learning, autonomous system that mostly learns through trial and error. It takes activities in order to maximize rewards, or to put it another way, it learns by doing in order to attain the best results. This is comparable to how kids learn to ride a bike, where they fall off a lot and make too chaotic movements at first, but with time they fine-tune our actions and learn how to ride a bike using the input of what worked and what didn’t. When computers use RL, they try different actions, learn whether that action delivered a better result from the feedback, and then reinforce the actions that worked, i.e. reworking and modifying their algorithms autonomously over many iterations until they make decisions that deliver the best result. It is used by a variety of software and computers to determine the best feasible action or path in a given situation. It is obligated to learn from its experience in the absence of a training dataset.
A robot learning to walk is a good illustration of reinforcement learning in action. Companies working on artificial intelligence are implementing a number of RL projects. The use of reinforcement learning for games by Google’s Deep Mind in the Atari computer games like Break Out is a good example. Deepmind produced AlphaGo, a robot that plays the most difficult board game, Go, using this RL technique. Deepmind also developed AlphaGo Zero, a version of AlphaGo that outperformed AlphaGo.
Also Read: Real-World Applications of Reinforcement Learning
A Comparison – Deep Learning and Reinforcement Learning
Both of these machine learning algorithms are used to harvest data. As discussed above, both algorithms learn on their own and the difference between them is little which stems from their learning methods. DL involves learning from a training set and then applying that knowledge to new data, whereas RL involves dynamically learning by altering actions based on continuous feedback to maximize a reward.
- DL can perform the desired behavior by studying current data and applying what it has learned to new data. RL, on the other hand, can change its behavior by adjusting to continual feedback.
- In general, DL uses data that has previously been collected, while RL relies on trial and error to make predictions.
- RL, as opposed to DL, is more akin to the capabilities of the human brain as it can be improved through feedback.
- DL is frequently used for recognition and area reduction tasks, but RL is usually associated with optimal regulation of the environment interaction.
However, reinforcement learning and deep learning are not mutually exclusive and in some programs, these two types of learning may coexist. Deep learning algorithms have been attempted to be used in a reinforcement learning system, and that is known as deep reinforcement learning.
Deep Reinforcement Learning
Deep reinforcement learning is a machine learning subfield that combines deep learning and reinforcement learning. Deep RL is a solution that allows agents to make decisions based on unstructured input data without having to manually construct the state space. Such algorithms can process very massive inputs and determine what actions to take to achieve a goal. Deep Reinforcement Learning is widely employed in today’s commercial activities, such as supply chain management, demand forecasting, inventory management, and warehousing operations. It is widely utilized in a variety of Natural Language Processing and Computer Vision tasks as well. Robotics, video games, education, transportation, finance, and healthcare are some other applications of deep reinforcement learning.
In Conclusion
Both deep learning and reinforcement learning are self-learning systems that allow computers to develop their own principles for solving problems. And while they do have their own sets of differences, they aren’t as distinct as you might believe.
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