Data Mining vs Deep Learning

Deep Learning

In the article “data mining vs deep learning”, now we will discuss what is deep learning. It is just a subset of machine learning that is based on Artificial Neural Network Architecture which works the same way on the machine, like how a brain processes information. Deep learning is generally used for supervised, unsupervised learning as well as reinforcement learning.

Training the model with deep learning takes more time compared to machine learning, and deep learning requires a high-performance computer with GPU.

Architectures used in Deep Learning

Here are the following architectures that are used in deep learning:

Feedforward Neural Networks (FNN):

FNNs are the simplest type of Artificial Neural Network(ANN). FNNs are used for the tasks such as image classification, speech recognition, and NLP (Natural Language Processing).

Convolutional Neural Networks (CNN):

This type of Neural Network is used for image and video recognition tasks.

Recurrent Neural Networks (RNN):

It is a type of neural network that is able to process sequential data like time series and language processing.

Pros of Deep Learning

• The deep learning algorithms used for image recognition and natural language processing are very high in accuracy.
   
• The deep learning models can be easily applied to different ranges of tasks, which shows flexibility.
   
• Deep learning models also improve performance as more data becomes available.
   
• The deep learning models can scale to handle large and complex datasets, which shows scalability.

Cons of Deep Learning

• Deep learning models require the high computational resources to train and optimize.
   
• The deep learning models are treated as the black boxes, which makes it difficult to understand how models work.
   
• Sometimes, deep learning models require a large amount of labeled data for training.

Difference between Data Mining and Deep Learning

In the article “data mining vs deep learning”, now we will discuss the important part, the difference between data mining and deep learning:

Frequently Asked Questions

What is Data Mining?

Data Mining consists of some techniques and tools which are used by scientists to find out the properties of datasets and produce insightful information which can be used by organizations.

What is Deep Learning?

Deep Learning is just a subset of machine learning based on Artificial Neural Network (ANN) Architecture which works the same way on the machine, like how a brain processes information.

What are the types of data mining?

Here are the following types of data on which data mining can be performed are Relational Databases, Data Warehouses, Object-Relational Databases, and Transactional databases, etc.

What are the types of deep learning?

Here are the following architectures that are used in deep learning are Feed forward Neural Networks (FNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN).

Conclusion

Both Data Mining and Deep Learning are areas inspired by each other, yet they have different ends. Data mining is used for producing the insightful information that can be used by the organization for identifying sales patterns or trends. On the other hand, deep learning is just a subset of machine learning that is based on artificial neural networks (ANN). 

In the article “data mining vs deep learning”, we discussed what is data mining along with its types, pros, and cons, what is deep learning with its types, pros, and cons, and the difference between data mining and deep learning.

Here are more articles that are recommended to read:

• What is Data Science?
• Introduction to Artificial Intelligence
• Complete Introduction to Data Mining
• Applications of Data Mining
• Classification in Data Mining

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