Deep Learning vs Machine Learning - Differences Explained

By Jagadish Jaganathan

Last updated on Mar 28 2022

Deep Learning vs Machine Learning - Differences Explained

Deep Learning vs. Machine Learning - What's the difference?

The term artificial intelligence was coined way back in the 1950s, the same time when Alan Turing was ridiculed for his groundbreaking ideas, which even today serve as a basis for Artificial Intelligence. From then to now, it is nothing short of a revolution, how this particular field has evolved.

From solving basic computational problems to IBM’s Deep Blue defeating world chess champion Gary Kasparov, Machine Learning and Artificial Intelligence have thrived and made themselves an indispensable part of technological studies. And today this specialization is a thing of the essence in fields like Information technology, Big Data, Research and development, and so on.

Deep Learning and Machine Learning, being the keywords in the field of Artificial Intelligence are often used interchangeably. While there are a few grey areas, Deep Learning and Machine Learning are two very distinct fields, and understanding the difference is of utmost importance. This article will help you learn different aspects of Deep Learning vs. Machine Learning in a simple yet veritable manner.  

Read more about the classifications in Machine Learning.

 

Understanding Machine Learning

Machine learning is making machines predict the outcomes based on data and experience. This is usually achieved by computer algorithms. Algorithms are a set of procedures or the step-by-step process involved in a particular function. Basically, every task around us can be broken down to compute its algorithm. And these algorithms are fed into the machine and made to interact with a set of data, the result is a smart machine that can think by itself, it can recognize the patterns and produce the desired results even if the input data changes. This is machine learning in a nutshell.

Machine learning is all about machine learning algorithms and these algorithms can be either supervised or unsupervised. Supervised machine learning is a process when the machine is trained with a set of ‘labeled’ data input, which means the machine is fed with input that is already tagged with the desired outcome. In unsupervised machine learning the machine is not trained using a labeled dataset, rather the machine is made to study the pattern of the input data and produce an outcome. 

There are a lot of machine learning algorithms like linear regression, logistic regression, decision tree, Naïve Bayes algorithm, and so on. Machine learning algorithms are not just classified based on learning types like supervised and unsupervised; they are also classified based on similarities like regression-based, decision tree, clustering, and so on.

Linear regression is one of the most basic traditional machine learning algorithms. It is a supervised machine learning technique where the relationship between one independent variable and a dependent variable can be predicted by plotting a straight line in a graph. For example, you can predict the sales for a particular month if you have the information about previous sales and their corresponding month. Here the sales months are trained data as they are labeled with their outcome. You can plot a graph with this table of data and with the help of a resulting slope; you can find the sales prediction for any given month.

While the above example is to explain the basic statistical method involved in machine learning, the main aim of machine learning is to achieve prediction without actual programming and with the help of pattern identification and data interaction. These are the key aspects of machine learning,

  • Machine learning is achieved by combining the principles and studies of computer science and statistics. Where the computer is made to learn not by programming but through pattern identification and external inference.
  • Machine learning algorithms are mainly classified into supervised and unsupervised based on their learning pattern. They can also be further classified based on similarities.
  • Machine learning can be achieved even by a simple statistical model like linear regression.

AI and Machine Learning

 

Understanding Deep Learning

Deep learning is a subset of machine learning, where complex algorithms are designed to mimic how the human brain functions and draw accurate conclusions.  It is a more sophisticated and mathematically complex form of machine learning, and machine learning in turn falls under the umbrella of artificial intelligence.

In deep learning algorithms, data and logic structures are computed in the fashion of the human brain, this happens through both supervised and unsupervised learning. To achieve this phenomenon, deep learning machines use multi-layered structures of algorithms called artificial neural networks (ANN). ANN is designed based on the human brain to accelerate the learning process and exceed the capabilities of traditional machine learning models. The difference between deep learning and other machine learning algorithms is that with more data set training deep learning algorithms' performance will increase.

A typical ANN model consists of an input layer, output layer, and multiple hidden layers in-between. The hidden layers in the network define the capability of the model, the more the number of hidden layers, the deeper the learning capability. ANN needs to have two or more hidden networks to qualify as a deep neural network.

To understand how the ANN computational algorithm works, consider an example for a defect tracking system in a production line, the purpose of this system is to identify the defective product, to achieve this the programmers would have to input data on the different aspects of the product called the features, like lines, edges, and other dimensions. In traditional machine learning each of the features needs to up inputted in the algorithms whereas, in Deep Learning, the system after a while forms pattern and identifies the pattern that doesn’t suit the desired model. And ANN will also learn from their errors and continuously improve their performances. Hence it requires very little human interaction.

While deep learning models are highly valuable they require a large amount of Data and high computational power to operate. Due to this reason, this field has been slow to evolve, but thanks to recent development in cloud computing and Big Data these problems are tackled efficiently and deep learning models are integrated into various infrastructures.

There are a lot of deep learning models but the most commonly used ones are,

  • Convolutional neural networks – these algorithms are specifically designed for object detection and image processing. It is achieved through the process of convolution, which analyses the images and assesses every element in them.
  • Recurrent neural networks – these systems have a built-in feedback loop that helps the algorithm to remember the past dataset points. These memories of past events help this system to process the current data and predict the future.
  • Feedforward neural networks – it is one of the most simple types of artificial neural networks. In this type of network, the information flows in one direction. Here the input data is put through numerous hidden neuron layers, and each of these layers is interconnected. The last layer is also known as the output layer, which gives the prediction.

 And here are the key aspects of deep learning,

  • Deep learning is a subset of machine learning, which is in turn a subset of artificial intelligence.
  • Complex algorithm structures called artificial neural network forms the basic block of deep learning.
  • It requires a huge amount of data and computational power to achieve deep learning and performance will increase as you feed more data.
  • Deep Learning models required very less human intervention. Since it is capable of rectifying the error it encounters by itself.

 

Deep Learning vs Machine Learning - Key Differences

We have already established that deep learning is a type of machine learning. Now let us compare them on the grounds of various aspects. Deep learning is way more sophisticated and complex compared to other machine learning models due to their ANN algorithm, self-regulation, and data requirement.

Traditional machine learning systems employ simple statistical models like regression, decision tree, and so on. The ANN employed in deep learning is multi-layered, complex, and intertwined. Since the feature extraction is accomplished in the neural networks itself, deep learning requires less human intervention. In machine learning, feature extraction needs to be manually coded.

Deep learning requires a massive amount of dataset to function, many times more than the amount of data required for traditional machine learning models. Due to the complex structure of the ANN, it requires a huge data set to draw the pattern with fewer errors and get accurate interpretation.

While in machine learning the algorithm need to be instructed on how to make a prediction based on information that is being fed, in deep learning the algorithms will learn to predict on their own, by learning from the data set. In the following table let us compare various aspects of Deep Learning vs. Machine Learning.

 

Transfer Learning

It is a known fact that training deep learning models requires a large amount of data, high computational power, and high execution time. In the absence of any of these resources, you can utilize the transfer learning technique to achieve deep learning. Transfer learning simply means applying the knowledge of solving one problem to solve similar problems. It can be easily implemented in deep learning models as they have multi-layered neural networks.

In the neural networks of the deep learning model usually, the first set of layers would contain less significant features and the last set of layers would have high-significant features closer to the domain function. By repurposing the final layers of the neural network you can impart new features identification with less resource.

For example, if you have a model that recognizes dogs, you can readjust the model using transfer learning and make it recognize cats, coyotes, horses, and other animals.

 

AI and Machine Learning

Conclusion

A lot of AI applications use machine learning and deep learning algorithms, though deep learning is a part of machine learning, selecting either deep learning or machine learning depends upon the complexity of the problem, data availability, and other resources. Deep learning is still in its nascent stage on various accounts. But its power and essence are already realized by a lot of big organizations. In recent times it has aided significantly in the development of biomedical signal analysis, image recognition, driverless cars, and so on.

Artificial Intelligence is an exciting development that has resulted in a lot of progress in the field of science. And the field of artificial intelligence and machine learning engineering is expected to produce a huge amount of career opportunities in the coming future. Studies show that by 2025 AI industry is expected to create 2.3 million job prospects. And in the last three years, the requirements for artificial intelligence engineers have tripled.

While there are a lot of ways to break into this field, a professional certification will not just equip you with the necessary knowledge and skills to succeed in this field; it will also add credibility and make you stand out in the crowd. Check out SprintZeal’s AI and machine science master’s program.

SprintZeal is ATO (Accredited Training Organization) providing industry standards professional certification training. We have a track record of aiding the upgrade of more than 300,000+ professional careers.

 

About the Author

Sprintzeal   Jagadish Jaganathan

Jagadish Jaganathan is a Content Writer at Sprintzeal. An avid reader and passionate about learning new things, his works mainly focus on E-Learning and Education domain.

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