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

It is now fully obvious how quickly the development of Artificial Intelligence in the future will transform the world of inventions and laborious work in the context of recent ground-breaking and revolutionary breakthroughs. Only through integrating superior machine learning and deep learning technologies into practice is this feasible.

The term artificial intelligence was coined way back in the 1950s, at the same time when Alan Turing was ridiculed for his groundbreaking ideas, which even today serve as the 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 indispensable parts of technological studies.

Today, this specialization is 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 about the different aspects of Deep Learning vs. Machine Learning in a simple yet verifiable manner.

Read more about the classifications in Machine Learning.

Understanding Machine Learning

Machine learning makes machines predict 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 for it.

This machine recognizes the patterns and produces 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 where the machine is trained with a set of ‘labeled’ data inputs, where the machine is fed with input that is already tagged with the desired outcome. But, with Unsupervised Machine Learning, the machine is not trained using a labeled dataset; rather, it 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 on a graph.

For example, you can predict the sales for a particular month if you have information about previous sales and their corresponding months. 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 the resulting slope, you can find the sales prediction for any given month.

While the above example is meant to explain the basic statistical methods 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. 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 patterns. They can also be further classified based on similarities.

☀ Machine learning can be achieved even with a simple statistical model like linear regression.

Understanding Deep Learning

Deep learning is a subset of machine learning, where complex algorithms are designed to mimic a human brain and draw accurate conclusions. It is a more sophisticated and mathematically complex form of machine learning, which 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, 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 sets trained, deep learning algorithms' perform better.

A typical ANN model consists of an input layer, an output layer, and multiple hidden layers in between. The hidden layers in the network define the capability of the model. Greater the number of hidden layers model, deeper the capability of machine learning. 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 of a defect tracking system in a production line. The purpose of this system is to identify defective products. 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 be inputted into the algorithms, whereas in Deep Learning, the system forms a pattern after a while and identifies the pattern that doesn’t suit the desired model.

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 developments 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 analyzes the images and assesses every element in them.

☀ Recurrent neural networks – these systems have a built-in feedback loop that helps the algorithm remember past dataset points. These memories of past events help this system 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 networks form 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 in more data.

☀ Deep Learning models require very little human intervention. Since it is capable of rectifying the error it encounters by itself.

Deep Learning vs Machine Learning - Key Differences

Now let us compare Deep Learning on the grounds of various aspects. To begin with, Deep Learning is way more sophisticated and complex compared to other machine learning models due to their ANN algorithm, self-regulation, and data requirements.

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

Deep learning requires a massive amount of data 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 an accurate interpretation.

With machine learning, the algorithm needs to be instructed on how to make a prediction based on the information that is being fed. In deep learning, algorithms will learn to predict on their own by learning from the dataset. Let’s 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 a long 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 gained from 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, the first set of layers would usually contain less significant features, and the last set of layers would have highly significant features closer to the domain function.

By repurposing the final layers of the neural network, you can impart new feature 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.

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, according to various accounts. But its power and essence have already been 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 number of career opportunities in the coming future. Studies show that by 2025, the 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.

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