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Introduction
Machine Learning is an essential part of artificial intelligence. It is becoming more and more prominent these days. With its help, the world is enhancing and advancing towards a much more advanced world where machines learn, interpret, and make decisions based on their experiences. Machine learning has different learning techniques: supervised, unsupervised, and reinforcement.
In this article, we will study Deep Learning vs Reinforcement Learning.
What is Deep Learning?
Deep Learning is a sub-part of machine learning based on artificial neural networks. The artificial neural network comprises different nodes in different layers that work together to process, handle, and learn from the input.
Now, these ANNs require complex computations and learning methods for correct simulations. Here, Deep learning is the field of Machine Learning which comes into play. Deep Learning architecture comprises Convolutional Neural Networks (CNN), Deep Belief Networks(DBN), Recurrent Neural Networks (RNN), etc. Also, it requires a large set of training data for efficient learning.
Its applications are in computer vision, NLP, game playing, robotics, etc.
Advantages of Deep Learning
Let us discuss some benefits of deep learning.
It is highly reliable if and when applied correctly. It has a high accuracy rate in fields like image recognition, NLP, etc.
Deep learning algorithms help the machine learn new and relevant features from the input data without manual engineering.
Deep Learning algorithm models are scalable and can manage complex datasets. Also, it is flexible to handle a wide range of tasks and data types like text, speech, audio, image, etc.
As and when more diverse datasets are fed, the model becomes more efficient and effective.
Limitations of Deep Learning
Let us note down some disadvantages of deep learning.
It requires high levels of computation power. Also, it requires a large amount of datasets to train with.
Deep Learning models are often considered black boxes, making it difficult to tell how they concluded and predicted outcomes.
These models may sometime overfit the data. It results in inefficient results on newer datasets.
What is Reinforcement Learning?
Reinforcement learningis a machine learning technique that is based on feedback. It involves taking actions to maximize the output reward in a situation. It means finding the best path in a specific scenario based on rewards or adverse outcomes from previous experiences.
It is a trial-and-error learning technique. Positive or negative feedback is given for every decision made; based on that feedback, the machine learns.
We can understand it with the help of the diagram below:
Here, we can see that Reinforcement Learning System has different elements: Model, Immediate Reward, Value Function, and Policy. Let us discuss these.
Model:- A Model is used for planning. It is the understanding and representation of the environment.
Immediate Reward:- It is the primary reward definition of the task. It gives a numerical value based on the decision made in the situation.
Value Function:- It denotes what is good in the long run. It is the total amount of the reward the machine can get.
Policy:- It is the agents’ learning behavior for a period of time. It is like a mapping from inferred states to the actions taken in those situations.
Applications of reinforcement learning are in the field of robotics, games, industrial automation, etc.
Advantages of Reinforcement Learning
Let us look at some of the advantages of reinforcement learning.
It is used to solve complex problems not solvable by conventional algorithms. It can solve problems that require decision-making and optimization.
It can correct any errors unidentified during the model's training.
It can handle non-deterministic environments where outcomes are not always predictable. It is instrumental in real-world applications where consequences are often unknown.
It follows a flexible approach that can be integrated with other techniques like deep learning.
Limitations of Reinforcement Learning
Let us note down some points of the disadvantages of reinforcement learning.
It is not advised to use reinforcement learning for simple problems.
It needs a lot of computation time and large amounts of data.
It depends on the feedback quality, i.e., rewards or adverse outcomes. If it is poorly designed, the technique is not practical.
It is challenging to debug because it is unclear why the agent behaves in a certain way making it difficult to diagnose issues.
Differences Between Deep Learning and Reinforcement Learning
Now that we have discussed deep learning and reinforcement learning separately, let us discuss Deep Learning vs Reinforcement Learning.
S.No.
Deep Learning
Reinforcement Learning
1
It was introduced by Rina Detcher in 1986.
Richard Bellman developed it in the late 1980s.
2
It applies to picture recognition, speech recognition, dimension reduction, etc.
It is applicable in robotics, gaming, healthcare, etc.
3
It requires an existing dataset for training purposes.
It does not require an existing dataset since it explores it for learning.
4
It is focused on recognition mostly. Thus, although it is comparable to learning of a human brain, but less when compared to reinforcement learning.
The user's feedback enhances it. Thus, it is more comparable to a human brain since we often learn via trial and error.
5
Deep Learning analyzes the previous datasets and then applies its gained knowledge to the current situations.
Reinforcement learning alters its approach with every feedback it is given.
Frequently Asked Questions
What is Machine Learning?
Machine Learning is the field of artificial intelligence that involves developing algorithms and models that help a machine learn. It helps improve the machine’s performance by learning from the fed dataset without being explicitly programmed. The machine is then expected to predict and work accordingly.
What is Reinforcement Learning?
Reinforcement learning is a machine learning technique that is based on feedback. It involves taking actions to maximize the output reward in a situation. It means finding the best path in a specific scenario based on rewards or adverse outcomes from previous experiences.
What is Deep Learning?
Deep Learning is a sub-part of machine learning based on artificial neural networks. The artificial neural network comprises different nodes in different layers that work together to process, handle, and learn from the input.
What are some benefits of Reinforcement Learning?
Some benefits of reinforcement learning are that it can solve complex problems unsolvable by conventional techniques. It can handle non-deterministic environments, and it is a flexible approach.
Conclusion
Machine Learning is an inseparable part of artificial intelligence. It is an essential part which enables machines to learn and make decisions on their own based on previous experiences and decisions made. And there are different machine learning techniques. In this article, we studied Deep Learning vs Reinforcement Learning. We started with understanding these techniques separately, with their definitions, advantages, disadvantages, and applications. Then, we looked at Deep Learning vs Reinforcement Learning.
To dive deep into this field, read the following articles:-
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