Deep Learning vs Machine Learning: Differences in 7 Easy Points

Introduction

Have you ever thought about how Facebook recognizes faces in a photo or how Netflix recommends shows you would love to watch next? It is overwhelming to see the latest advancement in Artificial Intelligence and its relevant domains, such as Machine Learning and Deep Learning. These subdomains are gaining popularity, and all of a sudden, everyone is talking about these streams. There are broader career opportunities residing ahead if aspirants opt for Machine Learning and Deep Learning courses. In this article let us dive into Deep Learning vs Machine Learning.

For most people, Deep Learning and Machine Learning (ML) seem like two interchangeable buzzing words, which is not correct. If an aspirant wants to build his/her career in the field of Artificial Intelligence, they should begin by understanding these terms and the differences between them. If the aspirants opt for Machine Learning and Deep Learning courses, they will learn subjects like statistics, algorithm design, neural network, natural language processing, Data Science, etc.

Arthur Samuel programmed the first computer program with learning capabilities in 1959, where an IBM computer kept getting better at playing checkers. With time, a slow-growing division of AI flourished into ML and Deep Learning. People these days talk about these two terminologies but most of them cannot differentiate between Machine Learning and Deep Learning. However, it is essential to know the difference between the two.

In this article, you will find answers to the following questions –

1. What is Machine Learning and Deep Learning? 
2. What is the difference between Machine Learning and Deep Learning?
3. Is Deep Learning part of Machine Learning?

A) What Is Machine Learning?

Machine Learning is a sub-domain of Artificial Intelligence that provides machines and computer systems the ability to automatically learn and improve their functioning from experience without being explicitly programmed.

With Machine Learning, computer systems gain the ability to learn from the data fed to the machine as input. This way, the system does not require reprogramming. In other words, with the use of Machine Learning algorithms, the computer can continuously enhance their performance on different tasks without human intervention. These advanced and sophisticated algorithms (such as Neural Networks) parse those input data, learn from those data, and then reapply it.

The wide use of Machine Learning ranges from recommendation-based on-demand music streaming and finding the shortest path in cab-apps to intelligent machines playing games against a human. Various domains and industries, like science, arts, finance, healthcare, and entertainment, use Machine Learning. There are several methods through which machines can learn. The training can be as simple as making a primary decision tree. Or, it can be a more complex algorithm containing recurring layers of artificial Neural Networks. There are two popular methods used today to design and write such algorithms – Machine learning with R and Machine Learning with Python.

B) What Is Deep Learning?

There is one common question in the mind of aspirants, is Deep Learning part of Machine Learning? Deep Learning is a sub-domain of Machine Learning that focuses on designing algorithms for mimicking the structures and functioning of the human brain, which is known as Artificial Neural Networks. By using Deep Learning Machine Learning algorithms, we can intend machines to perform tasks at human intelligence.

Deep Learning is a combination of technology and concepts, like Neural Networks, Machine Learning, and algorithms that can learn from enormous amounts of data. We call it ‘Deep Learning’ because there are various deep layers of Neural Networks applied in it to enable a machine to learn. We produce a staggering amount of data every day, and this enormous data acts as fuel to make Deep Learning possible.

In recent years, the capability of Deep Learning has grown to a tremendous level, and every industry is leveraging its potential. AI as a service also pushed small-scale businesses to access and utilize Artificial Intelligence technology and algorithms using deep-learning models to perform various tasks without a large investment. Using Deep Learning algorithms, we can design and allow machines to solve large and complex problems even when the dataset is diverse, interconnected, or unstructured. The more a machine can learn from these datasets, the better it gets trained and performs.

C) What Is the Difference between Deep Learning and Machine Learning?

Now, since you have understood what Deep Learning and Machine Learning is, it is time to understand the various Deep Learning and Machine Learning differences. An aspirant who opts for Data Science or Artificial Intelligence as their career path must understand what Deep Learning vs Machine Learning difference is. In this section, you will learn about Deep Learning vs Machine Learning with examples.

• Human Intervention: In Machine Learning systems, machines need little human intervention to identify and hand-code to apply its features on various datasets. This human intervention is not essentially required in Deep Learning algorithms as the models can learn those features through experience and repetitive approach with additional data as we humans do. The volume of data involved in training such a Deep Learning algorithm is enormous. Hence, with the time and iteration on that data model, Deep Learning algorithms can grow more Neural Networks and train them accordingly.

• Algorithm complexity: Another critical point between Deep Learning vs Machine Learning is, since Machine Learning algorithms require listed data, they are not the ultimate solutions for solving complex queries. That is where Deep Learning algorithms that utilize a tremendous amount of data to learn which experience is valid and which is critical, come into action.

• Hardware usage: It is a common difference between Machine Learning and Deep Learning. Due to large and complex mathematical calculations, Deep Learning systems demand much more robust and powerful hardware than implementing Machine Learning algorithms. It uses a high-powered Graphics Processing Unit (GPU), which are specialized electronic circuits designed to manipulate and accelerate memory to work faster on extensive data. On the other hand, Machine Learning algorithms comparatively require less-powerful hardware and can run on lower-end systems.

• Time: Another Deep Learning vs Machine Learning difference is time. The amount of data affects the time taken by the system to compile a solution. Due to the use of complex mathematical formulae used in Deep Learning systems, datasets take a lot of time (from a few hours to a few weeks) to train their algorithm for precision. Machine learning algorithms, on the other hand, are lightweight and do not take more than a few hours.

• Approach: Both Machine and Deep Learning are subsets of AI and are connected to data to make a representation of a specific form of intelligence. Machine learning algorithms tend to parse data in chunks, analyze them, and then combine those parts to come up with a solution.

• Let us now understand the difference between Machine Learning and Deep Learning with an example. If your Machine Learning algorithm wants to identify a particular object from a set of image data, the algorithm will perform object detection. Here, you have to put similar images to make the algorithm recognize that object. Once it understands which object to detect, it will perform object recognition from the image. On the other hand, Deep Learning algorithms will take input of those images, analyze it within itself, and with training, yield both the identified object along with their location.

• Algorithms used: Apart from understanding all the extreme Deep Learning vs Machine Learning differences, it is also essential for aspirants and professionals to know the different algorithms used in Machine Learning and Deep Learning. Some Machine Learning algorithms are Linear Regression, Decision Tree, KNN, K-Means, SVM, Logistic Regression, Naive Bayes, etc. Some Deep Learning algorithms are Convolutional Neural Network, Multilayer Perceptron Neural Network, Recurrent Neural Network, Backpropagation, Generative Adversarial Network, etc.

Conclusion