What’s the Difference between Deep Learning and Machine Learning?


Within the broad topic of artificial intelligence, machine learning and deep learning are two basic concepts. These two names are frequently used interchangeably, however they are not interchangeable. While machine learning and deep learning are both subsets of artificial intelligence, they have distinct advantages and disadvantages.

We’re going to look at the distinctions between machine learning and deep learning today. Let’s get going!

We’ll talk about:

  1. What is artificial intelligence and how does it work?
  2. What is machine learning, and how does it work?
  3. What is deep learning, and how does it work?
  4. Machine learning and deep learning have several key distinctions
  5. Conclusions and next actions



What is artificial intelligence and how does it work?

Let’s take a brief look at the branch of artificial intelligence (AI) that both machine learning and deep learning belong to before we go any further. Simply described, AI is a field that integrates computer science with huge, reliable data sets to assist in problem solving. AI has a wide range of applications. Let’s take a look at some of the most popular ones today:

  • ▪ Image recognition
  • ▪ Image classification
  • ▪ Natural language processing
  • ▪ Speech recognition
  • ▪ Facial recognition
  • ▪ Treatments for healthcare that are more effective
  • ▪ Computer vision
  • ▪ Etc.


There are two types of artificial intelligence: weak AI and strong AI. Weak AI is meant to accomplish specific tasks, and it’s what makes self-driving vehicles and Amazon Alexa possible. Strong AI does not yet have any practical uses, but it is a topic that is being investigated and examined. It revolves around machines having human-like intellect and consciousness, as well as the ability to learn, plan ahead, and solve problems.



What is machine learning, and how does it work?

Artificial intelligence has a subfield called machine learning. Algorithms for machine learning parse data, learn from it, and apply what they’ve learned to make better decisions. These machine learning models aim to improve computers’ ability to accomplish tasks without the need for human involvement or specific programming.

Computers are supplied training data to start the process. They use this information to figure out how to act on it in the future. Computers can receive new data and operate on it without our help after these models have been programmed. With time, the computer may be able to recognize data that hasn’t been tagged.


Machine learning has several different types

Machine learning is divided into three categories: supervised learning, unsupervised learning, and reinforcement learning. Let’s get to know them better!

Supervised learning

To train algorithms, this type of machine learning uses labelled datasets. The goal is to teach these algorithms to identify data independently and anticipate outcomes correctly. Spam detection in your email inbox is a fairly practical application of supervised learning.

Regression and classification are the two types of problems that supervised learning focuses on solving. Regression has real-valued output variables, such as a person’s age or weight. The most common model used to solve these issues is linear regression. The output variables of classification are categories, such as “mammal” or “amphibian.” Decision trees, logistic regression, and random forests are the most common models used to solve these problems.

Unsupervised learning

Clusters of unlabeled datasets are used in unsupervised learning. These machine learning techniques aid in the discovery of hidden patterns or data groups. Image recognition is a frequent use of unsupervised learning. Clustering, neural networks, anomaly detection, and other unsupervised learning models are among them.

Reinforcement learning

You train models to make a series of decisions using reinforcement learning. Consider it a game of trial and error. We give the machine rewards or penalties based on its activities to get it to achieve what we desire. Finally, we want it to figure out how to maximize rewards. Horizon, a Facebook app that employs reinforcement learning to personalize suggestions and send more meaningful notifications to users, is a real-world example of this.



What is deep learning, and how does it work?

Machine learning includes deep learning as a subset. You can think of it as a step forward in machine learning, or perhaps a step deeper.

Deep learning models are designed to examine data in the same way that humans do when making judgments and drawing conclusions. Data can be transmitted between nodes that replicate neurons in these models, which are fashioned after the human brain. They build an artificial neural network (ANN) that can learn and make decisions on its own by layering algorithms. Deep learning models are more capable than normal machine learning models because of their design.

Deep learning systems often require big datasets to be successful, but once they have data, they can produce results almost immediately. Once it’s set up, there’s virtually little need for human intervention. Transfer learning, which incorporates the use of pre-trained models, is a significant achievement in the field of deep learning. The necessity for huge training datasets is met by these pre-trained models.

Let’s look at a couple deep learning algorithms in action.


Algorithms for deep learning

Convolutional neural networks (CNNs)

A neural network with numerous layers is known as a multilayer neural network. These layers examine and extract data features. Computer vision, image processing, and object detection are all applications for CNNs.

Recurrent neural networks (RNNs)

For ordinal or temporal problems, RNNs use sequential or time-series data. To learn, they rely on training data. Google Translate, image captioning, and Siri are all examples of RNN applications.


For representation learning, autoencoders use neural networks. They are used to tackle unsupervised learning problems by replicating data from the input layer to the output layer. They’re employed in image processing and pharmacological research, among other things.



Machine learning and deep learning have several key distinctions

Deep learning is a subset of machine learning, and both types of learning are artificial intelligence subfields, as we discovered. Many people believe that deep learning is the same as machine learning. While they are closely related, they are not identical. Let’s talk about it!

  • ▪  Human involvement: While machine learning models improve at their assigned tasks, they still need our help. Deep learning algorithms, on the other hand, make decisions and analyze data using neural networks.
  • ▪  Sophistication: While both machine learning and deep learning are complicated systems, machine learning algorithms, such as decision trees or linear regression, have simpler structures. The structure of the ANN is substantially more complicated and interwoven since deep learning is fashioned after the human brain.
  • ▪  Differences in algorithms: Data scientists and analysts discover and detect machine learning algorithms, whereas deep learning algorithms are mostly self-described.
  • ▪  Representation of data: Deep learning techniques rely on layers of artificial neural networks, whereas machine learning algorithms often require structured input.
  • ▪  Scalability: Machine learning isn’t as good as deep learning at tackling complicated issues with large datasets.



Conclusion and next actions

Machine learning and deep learning are frequently confused, and vice versa. These two methods of learning are closely related and belong under the umbrella of artificial intelligence.

We want you to remember that deep learning is a type of machine learning. Machine learning aims to improve computers’ ability to reason and respond without the need for human assistance. Deep learning aims to improve computers’ ability to reason and behave using frameworks inspired by the human brain.

Because machine learning and deep learning are in-demand expertise, devoting more time to them will help you be successful in this field. There’s still a lot to learn, including:

  • ▪  Gradient descent
  • ▪  Functions of activation
  • ▪  Generative adversarial networks
  • ▪  Plus a lot more!


Check out Hong Kong Coding Club’s Deep Learning course to get started on  the fundamentals and intermediate components of deep learning. You’ll have a thorough understanding of the core components of deep learning at the end of the program.

Learning is fun!