Machine learning and deep learning are two of the fastest growing technology trends with a potential to offer plenty of promising opportunities with their diverse applications. Over the years, machine learning capabilities have improved in crunching large volumes of data. Today, they are widely used in various applications such as smartphone apps, email spam, and recommendation engines on e-commerce sites including speech recognition and handwriting recognition. On the other hand, deep learning is gaining more prominence as well, due to some of the recent breakthroughs and discoveries made in this field.

Let’s take a closer look at both these terms and compare the differences between the two technologies to understand them better.

What Is Machine Learning?

• The term “machine learning” was first introduced in 1959 by Arthur Samuel-American pioneer in computer gaming and AI, when the idea of AI had just started evolving in the industry. Based on the key ideas of AI, machine learning gained more recognition during the 1990’s.
• Machine learning involves use of algorithms for parsing data, learning from that data and making use of that data to make informed decisions based on the data. For example – Apps such as Pandora make use of algorithms to learn about the music preferences of individuals and use this information for making predictions about different types of music that their target audience might enjoy.
• Machine learning uses some of the key ideas of AI and lays emphasis on solving real-world problems through neural networks that mimics human decision making. A machine learning software comprises of two major components namely statistical analysis and predictive analysis used for discovering patterns and gather hidden insights based on earlier computations without the need to be programmed.

What is Deep Learning?

• Deep learning existed during the 1960’s, but it was a much overlooked concept during that time and gained wide acceptance in 2005, when this term was introduced by Aizenberg & Aizenberg & Vandewaale in his book-Multi Valued And Universal Binary Neurons, Theory and Learning and Applications.
• Deep learning is basically regarded to be the subset of machine learning and both these fall under the broad category of AI. The most unique aspect of deep learning lies in its ability to learn by itself using its computing brain, somewhat similar to the thinking capacity of humans, for deriving its own conclusions.
• For example – Google came up with a computer program, AlphaGo which was the first of its kind to defeat a professional human Go player by learning to play this abstract board game. By using a deep learning model, it could compete even against the best players of this game requiring sharp human intuition. The machine was able to grasp the complexity of the game and use its neural networks, without being told what specific move to make – this is indeed remarkable in itself.
• Deep learning makes use of a layered structure of algorithms known as artificial neural networks, whose design is based on the biological neural network used by human brains.

Comparison between Machine Learning and Deep Learning

Feature Extraction

Feature extraction assists developers by providing only the most relevant information to the algorithm – this enables the algorithm to come up with the best solution and boost the effectiveness of the program. However, in case of complex issues such as handwriting and object recognition, feature extraction may be quite cumbersome. Contrary to this, in deep learning, raw data may be fed through neural networks, and it does not require manual feature engineering. It can learn on its own by way of processing and learning the high-level features through the raw data.

Error Reduction

In deep learning, there are greater possibilities of learning high level and non-linear features which may assist with more accurate classification. This provides highly accurate results with quicker processing. On the other hand, since machine learning requires manual intervention, it can result in more human errors which may give less accurate results in the process of programming.

Hardware Dependencies

Machine learning algorithms can work on low-end machines, whereas deep learning algorithms require high-end machines. This is because deep learning algorithms need to perform deep matrix multiplication operations which is possible using a GPU, which is built in for this specific purpose.

Execution Time

Usually, deep learning requires longer training time as compared to machine learning as there are many parameters involved that need to be considered in the process. However, when it comes to the execution, it takes less time, whereas the testing time for machine learning algorithms may vary according to the size of the data.

Model Interoperability

Machine learning algorithms such as decision trees and linear regression models provide clear rules and logical reasoning for using them and achieving the specific output. On the other hand, in deep learning algorithms, it’s difficult to get a more realistic picture of how the output was achieved in the first place.

Conclusion

With the increasing trend of using data science and machine learning across diverse industries, machine learning will be required for every business for their survival. However, deep learning is proving to be a more powerful and state-of-the-art technique that holds a promising future for many industries.