When it comes to machine learning vs. deep learning, both technologies are subsets of artificial intelligence (AI), but one is more complex than the other and requires more computational power and data. So, how do deep learning and machine learning differ, and how can they power innovation in numerous areas of work and life?
What is Machine Learning?
Machine learning (ML) is a type of AI that enables computers to make decisions based on data without explicit, step-by-step instructions — as found in many traditional computer programming languages.
Definition and Core Principles
ML allows computers to detect patterns and learn to improve performance on different tasks based on experience using algorithms and models like decision trees or linear regression to learn from data and make decisions.
How Machine Learning Works
Rather than explicit, traditional computer programming using languages like JavaScript or HTML/CSS, machine learning uses neural network models and ML algorithms that use training data to make decisions. IBM engineer Arthur Samuel developed a computer program for playing checkers in the 1950s, which resulted in the minimax algorithm, and called his project “machine learning” in 1952.
Common Applications of Machine Learning
From investment advice and decision-making to chatbots that can provide counseling and advice, machine learning is deeply embedded in daily life around the world today. Using models to understand and interpret data, it powers assistive technology in cars (up to and including self-driving vehicles), IoT devices, marketing and advertising analytics, chatbots, and fraud detection in the finance industry. Other common machine learning uses include:
- Sales data analysis
- Personalized web and map search results
- Product recommendations
- Learning management systems
- Natural language processing
For instance: Any time you use Google Maps while en route to a destination and the map provides you with an ETA, you are benefiting from machine learning that has learned from traffic conditions in the past, road sensors, and weather conditions.
What is Deep Learning?
Deep learning is another subset of artificial intelligence, which uses neural networks to analyze and learn from large amounts of data. Neural networks are intended to emulate the way human brains are structured, representing a key difference between machine learning and deep learning.
Definition and Core Principles
While machine learning and deep learning have similarities, deep learning diverges from machine learning in its structure and scope. Machine learning can use ordinary central processing units (CPUs), but deep learning requires much greater computational complexity through graphics processing units (GPUs) or tensor processing units (TPUs) and massive amounts of data.
How Deep Learning Works
Deep learning can analyze visual, sound, and many other forms of data — then learn from it and make predictions and decisions. However, in order to learn, deep learning machines need vast volumes of data, with millions of examples, to perform well in complex tasks. Deep learning can handle large amounts of unstructured data and learn without manual intervention.
Common Applications of Deep Learning
How many television shows, movies, and games have you seen where a computer searches real-time video footage on a giant screen and helps law enforcement to capture a criminal before they can commit a crime? What used to be exaggerated fiction is now real, courtesy of convolutional neural networks (CNNs), a type of deep learning neural network. CNNs enable deep learning networks to recognize faces, objects, or patterns. The same technology supports facial recognition in your smartphone as well as the ability of autonomous vehicles to stop at a stop sign or avoid hitting pedestrians.
Other common uses of deep learning include:
- Real-time translation of languages
- Sophisticated customer service chatbots
- Smart home devices that respond to commands
- Movie, music, and product recommendations
- Medical diagnoses using MRI scans and other data
- Content creation (including art, writing, and video)
- Fraud detection and cybersecurity
Generative adversarial networks (GANs) and diffusion models are alternative methods to create content such as writing or art and videos. Natural language processing (NLP) enables deep learning models to understand the context of language at a much deeper level than traditional ML models.
Key Differences between Machine Learning and Deep Learning
Machine learning represents the broad field of AI involving computers that can use algorithms to work with data to make decisions and predictions or take actions. Deep learning is a more specialized, powerful, and complex subset of AI that uses neural networks to model complex patterns in data. Explore more specific distinctions between the two below:
Feature Engineering and Data Processing
- Machine Learning – ML needs human involvement to select the most relevant features of a given task for the model to use. The technology uses techniques like linear and logistic regression, decision trees, and support vector machines to perform various tasks. All these techniques require human intervention for learning to occur.
- Deep Learning – Deep learning’s multilayered approach of deep neural networks (DNNs), convolutional neural networks (CNNs), or recurrent neural networks (RNNs) can automatically extract features from raw data without human intervention — learning and making decisions independently.
Computational Power and Infrastructure
One of the primary differences between ML and DL is the amount of power and infrastructure each technology requires.
- Machine Learning – ML can run on moderate computing resources such as standard CPUs, which are designed to perform generalized computing tasks.
- Deep Learning – Deep learning requires high-performance GPUs or TPUs to handle the large amounts of data involved and the complex, multilayered neural network architecture.
The infrastructure required to operate and advance deep learning is significant. In 2024, the CEO of AI company Anthropic told TIME magazine that the next generation of AI systems would cost $1 billion. Additionally, Google’s Deep Mind CEO said that a $100 billion supercomputer planned for 2025/2026, built in cooperation with Open AI, was just the start of these companies’ planned future investments. In late 2024, xAI’s Colossus, with 100,000 NVIDIA GPUs located in Memphis, Tennessee, was built and put into operation in 19 days at an estimated cost of $3 billion to $4 billion.
Training Time and Data Requirements
Many hours of work and massive amounts of data are required to train the largest and most sophisticated AI models that leverage deep learning strategies. Costs for machine learning and deep learning vary because of their computing power, data, and training requirements.
- Machine Learning – Available for a number of years, ML models for specific purposes use small to medium-sized datasets and cost less to develop. ML projects for specific business applications can cost anywhere between $10,500 and $80,500 for training datasets, $15,000 to $20,000 for exploratory teams of engineers, and $20,000 to $100,000 to fully develop a specific ML solution (such as a fitness mirror that guides users through personal training tasks).
- Deep Learning – DL projects need large datasets for optimal performance and have longer training periods with many iterations. Different AI companies use different models for how they train and design direct learning systems — with some systems using smaller training models as an additional layer of more traditional deep learning multilayered architecture.
Model Complexity and Accuracy
- Machine Learning – ML works well for structured data, and organizations often use the technology in some way to conduct daily business. ML can use ordinary CPUs or servers and doesn’t require the massive computing power of deep learning AI models. Machine learning isn’t very good at dealing with unstructured input like images and videos.
- Deep Learning – Deep learning can excel in recognizing images, viewing videos, and processing and responding to speech because of its ability to process large amounts of data and recognize complex patterns on its own. The way that deep learning uses multilayered neural networks, similar to the way our brains process information, allows it to direct autonomous vehicles and create art and videos based on verbal prompts.
Applications in Real-World Scenarios
Machine learning is used for purposes like financial forecasting, fraud detection, and price prediction. It can inform bank or credit union decisions about loan and credit pre-approvals. ML also powers the function of your email inbox that learns from your use of the application as well as filters your email. Early versions of Apple’s Siri are an example of machine learning (not deep learning or neural networks).
ChatGPT is the most famous example of deep learning and neural network technology, capable of conversing and responding to a wide range of user requests. In addition, deep learning powers autonomous vehicles, facial recognition for personal security and law enforcement, and AI-created content — from writing to artwork, videos, and language translations.
Machine Learning vs. Deep Learning: Similarities
Machine learning and deep learning share similarities in their use of algorithms and silicon-based processing units. Furthermore:
Both are Subsets of AI
ML and DL are subsets of the larger field of artificial intelligence, and both benefit from the work of computer engineers, math researchers, and AI professionals.
Both Require Large Amounts of Data
Machine learning requires approximately 10 times more data samples than model parameters, and deep learning requires much more data. For example, the multilayered structure of deep learning algorithms relies upon millions of data points.
Both Use Mathematical Models to Recognize Patterns
Machine learning and deep learning both leverage mathematical models to recognize patterns and learn from them, but the models required for deep learning are much more complex than those used in ML applications.
Both Can Be Supervised, Unsupervised, or Reinforcement-Based
Both ML and DL applications can be supervised or unsupervised, and both also use reinforcement learning or training.
How to Know when Each Should Be Used
What job needs to be done? Deep learning is best suited for large, complex tasks with sizable, unstructured sets of data. If data is structured, such as a bank or credit union’s group of credit card accounts or loan customers, then machine learning is better suited to applications serving them.
Learn More about Deep Learning vs. Machine Learning at Texas A&M University
In the realm of AI, one size doesn’t fit all — and machine learning and deep learning represent different approaches to solving problems in nearly every industry and aspect of life. Elevate your career and prepare yourself to meet the demands of the future with an Online Master of Science in Artificial Intelligence from Texas A&M University. Request more information or apply today!