Deep learning has the potential to revolutionize fields like medicine and cybersecurity, and it’s already making an impact. From personalizing recommendations on ecommerce platforms to creating new artistic styles, deep learning is transforming our day-to-day lives.
Learn how the powerful tool works, how it fits within the broader scope of machine learning, and how different industries are using it.
What is deep learning?
Deep learning is a subset of machine learning and a type of artificial intelligence that uses artificial neural networks to process information. An artificial neural network takes inspiration from the structure and function of the human brain. They consist of interconnected nodes that act like neurons by processing information and transmitting signals to other nodes.
Deep neural networks use multiple layers of these processing nodes. Each layer learns to recognize increasingly complex patterns from the data on which it’s trained. This is why it’s called “deep” learning—the more layers, the more complex the learning capability.
Deep learning is adept at performing tasks requiring high accuracy in pattern recognition and complex data analysis. Common applications of deep learning include image recognition (e.g., facial recognition in photos), natural language processing (e.g., powering chatbots and machine translation); speech recognition (e.g., voice assistants like Siri and Alexa); and recommendation platforms (e.g., suggesting products you might like on online shopping platforms).
How does deep learning work?
Deep learning systems have three main layers: the input layer, the hidden layer, and the output layer. The depth of hidden layers determines the kind of tasks the deep learning model can perform. Here’s how it works:
Input layer
Before the model can perform tasks, raw data enters the network. The format of the data depends on the task. For example, it could be pixels from an image, words in a sentence, or numerical values representing internet-connected sensor readings. Each element in the data then becomes a numerical value that the network can process.
Hidden Layer
This is where most of the work happens, typically requiring multiple hidden layers stacked on top of each other. As data progresses through hidden layers, it becomes increasingly refined and represents more abstract features. Think of an image that is still a bit blurry or pixelated while a website is taking a while to load.
Output layer
This is the final layer where the system delivers the processed information. The structure of the output layer depends on the network’s purpose. In image recognition, it might assign probabilities to different categories (e.g., cat, dog, airplane). In a language translation task, it might generate the translated sentence in another language.
Deep learning vs. machine learning
Deep learning is a subset of machine learning. Deep learning excels at handling complex data and achieving high accuracy to perform complex tasks. Whereas traditional machine learning approaches require a significant degree of human guidance, deep learning can achieve more on its own.
Here’s what that means: Machine learning often relies on feature engineering, in which human intervention identifies and extracts relevant features from the data that the learning algorithm uses. This can be a time-consuming and knowledge-intensive process. Deep learning can learn features directly from raw data through its multiple hidden layers. This allows deep learning models to perform complex tasks and process high-dimensional data more effectively.
Applications of deep learning
Deep learning has become a powerful tool in many fields due to its ability to identify complex patterns in data. Deep learning models can achieve high levels of accuracy in performing tasks, sometimes even rivaling human intelligence. These models can learn from large datasets without the need for explicit programming at every step. Here are some common applications of deep learning:
Auto
Deep learning techniques are crucial for self-driving cars to distinguish and identify their surroundings. By analyzing camera data, these cars can recognize objects like pedestrians, vehicles, and traffic lights, allowing them to navigate roads.
Ecommerce
Deep learning personalizes a customer’s online experience by suggesting products, movies, music, or other content that might interest them. These product recommendations draw from past behavior and browsing history.
Deep learning also powers voice assistants, allowing you to interact with your device via spoken commands. These models can recognize different accents and background noise, making speech recognition more accurate and user-friendly.
Entertainment
Content creators face difficulties in tailoring content to individual user preferences and keeping audiences engaged. Deep learning can personalize content recommendations or generate realistic special effects in video games and movies.
Finance
Deep learning models can analyze financial transactions to identify patterns indicative of fraudulent activity. This helps banks, credit card companies, and other financial institutions prevent monetary losses.
Medicine
Doctors can use deep learning for medical image analysis, such as analyzing medical scans like X-rays and MRIs to detect abnormalities and aid in disease diagnosis. This data analysis can help doctors identify cancers, evaluate bone fractures, and assess other medical conditions.
Robotics
Traditional robots struggle with tasks requiring fine motor skills and complex object manipulation. Deep learning can improve robot dexterity by enabling them to learn from vast datasets of simulations and real-world interactions. Additionally, deep learning can enhance object recognition for robots, allowing them to identify and interact with objects more accurately.
Deep learning FAQ
What are examples of deep learning?
Deep learning algorithms are useful for image and facial recognition, identifying individuals in photos and videos with high accuracy. Applications include social media tagging, security systems, and unlocking smartphones. Deep learning algorithms can also detect and respond to cyber threats more effectively. By analyzing network traffic patterns, they can identify malicious activity.
What is the difference between machine learning and deep learning?
Deep learning is a complex technology within the field of machine learning. Many basic machine learning algorithms offer better interpretability, meaning you can often understand the factors influencing the model’s decisions. Deep learning models are a bit murkier, and their decision-making processes can be difficult to decipher due to the complex web of information within the hidden layers.
How does deep learning work?
Deep learning performs tasks by mimicking the human brain’s structure and function. Deep learning relies on artificial neural networks, which are loosely inspired by the biological neural networks in the brain. Unlike simpler neural networks, deep learning involves multiple layers of these artificial neurons, often stacked on top of each other like a pyramid.
What is deep learning useful for?
Deep learning is adept at performing tasks requiring high accuracy in pattern recognition and complex data analysis—but it’s not necessary for all machine learning tasks. Common applications of deep learning include image recognition and computer vision (e.g., facial recognition in photos), natural language processing (e.g., powering chatbots and machine translation); speech recognition (e.g., voice assistants like Siri and Alexa); and recommendation platforms (e.g., suggesting products you might like on online shopping platforms).
Why is deep learning important?
Deep learning’s power lies in its ability to learn complex patterns from large amounts of data, enabling it to perform tasks that were once impossible for machines. Deep learning models now often surpass human performance in specific areas, and these models can automate tasks that were previously manual or labor-intensive, such as facial recognition in matters of security.
