Machine learning and deep learning are two fundamental concepts within the broad field of artificial intelligence. These two terms are often used interchangeably, but they actually aren’t the same thing. While machine learning and deep learning are each a different subset of artificial intelligence, they have their differences.
Today, we’re going to explore machine learning and deep learning and establish their differences. Let’s get started!
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Before we dive deeper into machine learning and deep learning, let’s take a quick look at the branch they both fall under: artificial intelligence (AI). To put it simply, AI is a field that combines computer science with large, robust sets of data to help with problem-solving. There are many different use cases for AI. Let’s take a look at some popular ones in use today:
There are two main types of AI: weak AI and strong AI. Weak AI is designed to perform specific tasks, and it’s what enables applications like self-driving cars and Amazon Alexa to function. Strong AI has no practical applications in use today, but it’s a field that’s being researched and explored. It centers around machines that have human intelligence and consciousness, with the ability to learn, make plans, and solve problems.
Machine learning is a subfield of artificial intelligence. Machine learning algorithms parse data, learn from it, and apply their knowledge to make informed decisions. The goal of these machine learning models is to optimize computers to perform tasks without the need for human interference or specific programming.
To begin the process, computers are fed training data. They use this data to learn how to act on that data in the future. Once these models are programmed, computers can accept new data and act on it without our help. With time, the computer may begin recognizing unlabeled data.
The field is broken down into three subsets of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Let’s learn more about them!
This subset of machine learning uses labeled datasets to train algorithms. The goal is to train these algorithms to independently classify data and accurately predict outcomes. A very practical application of supervised learning is spam detection in your mail inbox.
Supervised learning focuses on solving two types of problems: regression and classification. Regression has output variables that are real values, such as someone’s age or weight. The main model used for these problems is linear regression. Classification has output variables that are categories, like “mammal” or “amphibian”. The main models used for these problems are decision trees, logistic regression, and random forests.
Unsupervised learning uses clusters of unlabeled datasets. These machine learning algorithms help discover hidden patterns or groups of data. A common application of unsupervised learning is image recognition. Unsupervised learning models include clustering, neural networks, anomaly detection, and more.
With reinforcement learning, you train models to make a sequence of decisions. Think of this as a trial and error game. To get the machine to do what we want it to do, we give it rewards or penalties based on its actions. In the end, we want it to learn how to maximize rewards. A real-world example of this would be Facebook’s Horizon, which uses reinforcement learning to do things like personalize suggestions and deliver more meaningful notifications to users.
Deep learning is a subset of machine learning. You can think of it as an evolution of machine learning or even deeper machine learning.
Deep learning models are meant to analyze data with a similar logical structure to how humans make decisions and draw conclusions. These models are modeled after the human brain, and they enable data to be passed between nodes that mimic neurons. They layer algorithms to create an artificial neural network (ANN) that can learn and make decisions on its own. This design makes deep learning models more capable than standard machine learning models.
Typically, deep learning systems require large datasets to be successful, but once they have data, they can produce immediate results. Once it’s set up, the need for human intervention is very low. An important advancement in the field of deep learning is called transfer learning, which involves the use of pre-trained models. These pre-trained models help fulfill the need for large training datasets.
Let’s take a look at a few examples of deep learning algorithms.
Convolutional neural networks (CNNs)
This is a type of neural network that has multiple layers. These layers analyze and extract features from data. CNNs are mainly used for computer vision, image processing, and object detection.
Recurrent neural networks (RNNs)
RNNs use sequential data or time-series data for ordinal or temporal problems. They use training data to learn. Some common use cases of RNNs include Google Translate, image captioning, and Siri.
Autoencoders use neural networks for representation learning. They replicate data from the input layer to the output layer and are used to solve unsupervised learning problems. They’re used for things such as image processing and pharmaceutical research.
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We learned that deep learning is a subset of machine learning, and both types of learning are subfields of artificial intelligence. Many say that deep learning is machine learning. While the two are closely related, they have their differences. Let’s discuss!
Human interference: While machine learning models become better at their specified tasks, they still require our guidance. On the other hand, deep learning algorithms use their neural networks for decision-making and analysis.
Complexity: While both machine learning and deep learning are complex systems, machine learning algorithms have simpler structures, like decision trees or linear regression. Since deep learning is modeled after the human brain, the structure of the ANN is much more complex and interconnected.
Algorithmic differences: Machine learning algorithms are detected by data scientists and analysts, while deep learning algorithms are mainly self-depicted.
Data representation: Machine learning algorithms typically require structured data, whereas deep learning algorithms rely on layers of artificial neural networks.
Scalability: Machine learning is not as well-suited for solving complex problems with large datasets, but deep learning is.
It’s common to mix up machine learning with deep learning and vice versa. These two types of learning fall under the broad category of artificial intelligence, and they’re very closely related.
If we were to give you some key takeaways from this article, we want you to remember that deep learning is a type of machine learning. The goal of machine learning is to optimize computers to think and act with less human interference. The goal of deep learning is to optimize computers to think and act using structures based on the human brain.
Both machine learning and deep learning are in-demand skills, so spending more time on these topics will put you ahead of the curve. There’s still so much more to learn, such as:
To get started learning these concepts, check out Educative’s course Introduction to Deep Learning. In this course, you’ll cover the basic and intermediate aspects of deep learning. By the end of the course, you’ll have a comprehensive understanding of the fundamental components of deep learning.
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