Deep Learning vs Machine Learning

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Introduction

Machine Learning is the popular buzz word across popular media, industries and working professionals. But then, there is something known as Deep Learning which is also gaining too much attention. So is there any difference between deep learning and machine learning or is it same? Since at times people are referring to both machine learning and deep learning interchangeably it can become confusing. Don’t worry, in this post we will do a thorough comparison of deep learning vs machine learning to clear all your confusions.

So, let us get started.

Definition

Machine Learning

Machine Learning is the field of study to make computers learn and make decisions like humans. It is a very huge field and is supported by various popular algorithms like logistic regression, linear regression, svm, decision trees, artificial neural network, knn etc.

Deep Learning

Deep Learning is specialized sub branch of machine learning which deals only with the study of artificial neural network based algorithms. The popular algorithms are feed forward neural network, convolutional neural network, recurrent neural network, deep belief network etc.

Similarities

So, as you can see from the definition, since deep learning is just a sub branch of machine learning, they both essentially can be used for regression, classification, predictive analysis and unsupervised learning tasks.

Differences between Deep Learning and Machine Learning

Data and Performance

The basic ingredient of any ML model is data. The more data we have, the model will show good accuracy.

But, the models created from traditional machine learning model works well with huge data only till up to a certain extent and then plateaus out even with more suppl of data.

On the other hand, deep learning models are always hungry for more data. The more data you give to neural network, it will show much greater accuracy than other machine learning models.

Computational Hardware

Training deep neural networks on huge amount of data requires a vast and expensive computational resources to achieve training in practical time. Currently the state of art deep learning models are trained on GPUs (Graphical Processing Unit) and even on TPUs (Tensor Processing Units).

The traditional machine learning algorithms are suited for smaller data size only. Hence working with these models do not need a huge computational hardware which is needed by deep learning. It can be done merely on CPUs efficiently.

Explainability

Deep Learning models works like a black box and it’s results cannot be explained easily even by the engineers who trained the network. One of the reason behind this is that neural networks take care of the feature selection part on it’s own. This means that even if you don’t do feature selection, even then neural network would be able to learn if a particular feature is important or not, and reduces the corresponding weights to almost zero.

In traditional machine learning algorithm, feature selection plays an important role. A good amount of data analysis, domain knowledge goes into feature selection process. This means that the engineer who is building the model will be able to interpret the results.

Advantages of Deep Learning over Machine Learning

Deep Learning has been able to achieved astounding level of accuracy in areas like computer vision, natural language processing, recommendation systems, reinforcement learning in last few years that could never be achieved by traditional machine learning algorithms. The traditional machine learning algorithms can only show limited accuracy and cannot touch the high accuracy of deep learning.
We are living in era of big data where we are generating huge amount of digital data at exponential rate. At the same time, we now have advancements in hardware to store and compute big data more efficiently than ever before. Since neural networks work really well with huge amount of data, we are at the correct point in time to leverage deep learning in the big data era. On the other hand, traditional machine learning algorithms do not work at par with big data.
Deep Learning has been able to enter creative and artistic field with the recent advancements in GANs, Neural Style transfer. With traditional machine learning approaches we cannot even think of doing such marvelous things.

Advantages of Machine Learning over Deep Learning

Simpler problem needs simpler machine learning models and not something as complicated as deep learning. When a problem can be solve with something as simple as logistic regression then we should not go for deep learning.
When data is not much or you lack good hardware resources, then deep learning would not be a good idea. In this case traditional machine learning algorithms would be the most obvious choice for building the models.
One of the biggest issues with deep learning is that neural networks are like black box whose decisions cannot be explained easily to stakeholders. And this cannot be accepted in high risk industries like healthcare and finances where transparency is essential. On the other, hand traditional machine learning models can be explained more better.

Applications

Traditional machine learning models are useful for simpler tasks of predictive analysis, risk analysis, customer segmentation, market basket analysis, anomaly detection etc.
Deep Learning models are useful for much more complex task like, natural language processing, computer vision, recommendation systems etc. They can also be used for simpler tasks, but it is always recommended to use traditional ML models for those type of work for better explainability.

Also Read- Brief History of Deep Learning from 1943-2019 [Timeline]

In The End…

So, I hope this article gave you a clear idea of what is deep learning and what is machine learning, how they differ and how they are similar. In case you want to clear confusion in other area, you can check some more article in this series –

Also Read- Regression vs Classification – No More Confusion !!
Also Read- Supervised vs Unsupervised Learning – No More Confusion !!
Also Read- Data Science vs Machine Learning – No More Confusion !!

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Introduction