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Last Updated: Mar 27, 2024
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Deep Learning vs Supervised Learning

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Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM

Introduction

In artificial intelligence, machine learning is a significant branch. In this technically advancing world, machine learning is making its presence more and more prominent. Machine Learning means the learning process of a machine through which it can make correct decisions in the future. It has different learning techniques, like supervised, unsupervised, reinforcement, etc.

deep learning vs supervised learning

In this article, we will study Deep Learning vs Supervised Learning.

What is Supervised Learning?

Supervised Learning is a machine learning technique in which the data is well-labeled. The input and output of the data are categorized. It means that the data is already mapped to its correct output. The provided data set acts as a teacher or a supervisor; hence the name is Supervised Learning.

Once sufficient data has been fed to the machine, it is tested with test cases to check if it can map the inputs to their correct output categories. It is divided into two categories:- Classification and Regression.

Classification is the situation when the output is a category-like result. Like, we have to classify the input into specific categories. Like, whether the color green or yellow, etc.

Regression is finding relations between the dependent and the independent variables. It helps to fit the data set to predict future values to maximum precision.

Examples of supervised learning algorithms are KNN, Decision Trees, Support Vector Machines, etc.

Advantages of Supervised Learning

Let us look at some benefits of supervised learning.

  • It allows for collecting data from various cases and then deciding based on previous experiments. This way, it optimizes the performance.
     
  • It allows users to perform regression and classification tasks.
     
  • It is entirely up to the trainer how many test cases will be fed to the machine to train it. And this allows the machine to map the inputs to correct outputs accordingly.
     
  • It helps in various real-world applications.

Limitations of Supervised Learning

Now, let us look at some of the limitations of supervised Machine Learning.

  • Sometimes, the process of classification of big data can be challenging.
     
  • The computation time can be more since the machine has to train on a significant amount of data.
     
  • It strictly requires a labeled data set and a training process.
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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.

We can see a basic illustration of an ANN below.

complete ann

Deep Learning can be used for all three machine learning techniques: supervised, unsupervised, and reinforcement 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.

Difference Between Supervised and Deep Learning

Now that we have understood supervised learning and deep learning separately, let us study Deep Learning vs Supervised Learning.

S.No

Supervised Learning

Deep Learning

1

It is used to solve tasks where the tests fed are labeled. The jobs are relatively more straightforward than often; the targets are human-detectable.

E.g., Predicting colors, etc.

It can do many complex tasks.

E.g., Image classification, speech recognition, handwriting transcription, machine translation, etc. 

2

Training models are relatively more flexible. Also, to fine-tune these models and find correct hyperparameters, methods exist. E.g., grid search, etc. Training models are comparatively less flexible. It is because the number of hyperparameters is more. Like, number of neurons per layer, weight initialization, etc.

3

Features are created explicitly. E.g., polynomial functions for regression, etc. The hidden layer automatically works for abstract data representation.

4

It cannot generate something original as it only predicts the output based on fed-labeled data. Once trained, specific types of deep neural networks, like, Generative Neural Networks (GNN), can create new outputs. It is an excellent application of it.

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 Supervised Learning?

Supervised Learning is a machine learning technique in which the data is well-labeled. The input and output of the data are categorized. The provided data set acts as a teacher or a supervisor; hence the name is supervised Learning. It helps in correctly predicting the results.

What is Unsupervised Learning?

Unsupervised Learning is a machine learning technique in which the dataset is not labeled. Its objective is to find patterns in the dataset. There is no defined mapping of input to output. A critical study of the dataset is done in it.

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.

Conclusion

Machine learning is on the rise in today’s world. Whole Globe is set to advance in the era of artificial intelligence. Hence, it is essential to study various critical aspects of machine learning. In this article, we learned about Deep Learning vs Supervised Learning. We started with their definition, advantages, disadvantages, and applications separately. Then, we noted some essential points on Deep Learning vs Supervised Learning.

If you wish to enhance your learning in this field, we recommend you read the following articles:-

 

To learn more about DSA, competitive coding, and many more knowledgeable topics, please look into the guided paths on Codestudio. You can also consider our Machine Learning Course to give your career an edge over others.

 

Happy Coding!

Topics covered
1.
Introduction
2.
What is Supervised Learning?
2.1.
Advantages of Supervised Learning
2.2.
Limitations of Supervised Learning
3.
What is Deep Learning?
3.1.
Advantages of Deep Learning
3.2.
Limitations of Deep Learning
4.
Difference Between Supervised and Deep Learning
5.
Frequently Asked Questions
5.1.
What is Machine Learning?
5.2.
What is Supervised Learning?
5.3.
What is Unsupervised Learning?
5.4.
What is Deep Learning?
6.
Conclusion