Difference between Deep Learning and Machine Learning

Of all the trends of recent times, Artificial Intelligence has created quite a buzz. Since its conception in the late 1950s, technology has evolved and become immensely advanced. The rise of Artificial Intelligence gave birth to the terminologies like Machine Learning and Deep Learning.

These trending technologies brought by Data Science bring a lot of confusion because their concepts are interrelated with one another. We often see people using these terms interchangeably. Although entwined with conceptual similarities, the two technologies have some notable differences. Both ML and DL are part of Artificial Intelligence, However Machine learning is the Specific form of AI, and Deep Learning is the distinctive form. Deep Learning AI is a subset of Machine Learning which in turn, is a subset of Artificial intelligence. 

In this article, we are going to learn how the two technologies are different from each other.

Machine Learning

Machine Learning is a subset of Artificial Intelligence designed to set computers to perform tasks without any need to perform explicit programming. It includes Machine Learning algorithms that learn from the data and then apply it to make informed decisions. The major focus of Machine Learning is on the development of computer programs that access the data and learn from it.

The Learning process of the algorithm is supervised or unsupervised depending on the data. Machine Learning was made possible by Arthur Samuel’s program in 1959 that used a search tree as its main driver. ML has emerged as a great tool to analyze and identify patterns in data. A simple Machine Learning algorithm is as simple as linear regression. ML can predict the value or the class of new data.

Deep Learning

Deep Learning is considered the next frontier of Machine Learning. It is a subset of Machine Learning that imitates the data processing of the human brain by using Artificial Neural Networks and creates similar patterns for decision making. Deep Learning systems can even learn from unstructured or unlabeled data. 

If you have ever used Netflix, you will see that it offers certain recommendations based on your viewing. It is possible because of the Deep Learning technology. The voice recognition and image recognition algorithms in Google are also created using Deep Learning.

A deep neural network has three types of layers-

• The input layer
• The hidden layer
• The output layer

These Neural networks are used to perform classification on data and predict the output. 

Deep Learning vs Machine Learning: Detailed comparison

While Deep Learning and Machine Learning are part of AI, there is a significant difference between both of them. Here we will shed light on the ultimate battle of Deep Learning vs Machine Learning.

Data Dependencies:

Deep Learning and Machine Learning offer different performance with different scales of data. DL algorithms do not perform well with a small amount of data. 

Hardware Requirements:

It is important to note that Deep Learning uses large data and complexity of the algorithms, and thus requires more powerful hardware to run on compared to the lower-end hardware used in machine learning. One such hardware is the Graphical Processing Unit (GPUs). 

Unlike ML, Deep Learning requires more time to train the models and is generally more hard to implement. But then, a complex problem needs some rigorous efforts.

Execution Time:

A significant and most visible difference between Deep Learning and Machine Learning is the execution time. A Deep Learning system can take a lot of time due to complicated mathematical calculations. A DL algorithm could take weeks to execute, while an ML algorithm takes much less time ranging from seconds to few hours.

While DL takes more time in execution, the testing time is comparatively less. In Machine Learning the test time increases on increasing data size. However, this is not the case with every ML algorithm.

Problem Solving Approach:

A Machine Learning algorithm will ask you to break the problem into two parts, only to solve them individually and combine them later to get the desired results. On the contrary, Deep Learning recommends solving the problem end-to-end.

Deep Learning uses an Artificial Neural Network to make decisions by itself.

Feature Engineering:

Feature engineering is the procedure where domain knowledge is used to create feature extractors, which in turn reduces the complexity. It requires a lot of expertise and time. In Machine Learning most of the features are identified by the expert and later hand-coded. This feature includes pixel values, shapes, and position.

The accuracy of the identification and extraction of these features affects the overall performance of the Machine Learning algorithm.

Whereas, Deep Learning learns features from data reducing the task of creating new features and does not require feature engineering.

Conclusion

Both these technologies are a specialized form of Artificial Intelligence and are continuously evolving. Though they have their differences, the amalgamation of the two domains of AI is all set to create a bright future for data science. 

We hope that this article has given you a clear understanding of both Machine Learning and Deep Learning. Feel free to post your queries in the comment section.

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