What’s the difference between machine learning and deep learning?
Can you tell the difference between machine learning and deep learning? If your answer was that one starts with the word ‘machine’ and the other has ‘deep,’ you’re not wrong. But this is not the full picture.
Indeed, many people equate machine learning and deep learning – two popular buzzwords in the world of AI. However, this comparison overlooks the unique characteristics that set them apart. We invite you to discover what makes them similar and what takes them apart in our article.
Machine learning
According to the Oxford Dictionary, machine learning is a type of artificial intelligence in which computers use huge amounts of data to learn how to do tasks rather than being programmed to do them. In short, machine learning is about training a computer to perform tasks without detailed instructions.
These systems use algorithms to process large amounts of data, find patterns, and make predictions based on the information they’ve analyzed. The more data they chew through, the better they become at making decisions. It’s a bit like teaching a child to ride a bike: the more they practice, the less likely they are to end up in a bush!
Three types of machine learning models
The learning process of these algorithms can be supervised, unsupervised, and reinforcement. It depends on the data that is used to feed the algorithms.
Types of ML models
Supervised learning uses labeled data to train a machine to match inputs to specific outputs. For example, to teach an algorithm to recognize apples, you give it labeled pictures of apples. The algorithm learns by using this known data.
When the output is unknown, we deal with unsupervised learning. As the name suggests, this works without human guidance, using unlabeled data to find patterns. The algorithm categorizes data on its own based on shared traits. For example, if you give it pictures of apples and bananas, it will figure out which is which without being told.
Reinforcement learning works differently. The algorithm interacts with its environment and gets feedback — rewards or penalties. For example, in a game of chess, the algorithm learns to make moves that lead to winning the game over time. It receives positive feedback for good moves and negative feedback for poor choices. This helps to improve its strategy.
Spotify is a familiar example of a machine learning algorithm in action. It learns your music taste by tracking the songs you listen to or save. Each time you interact with a song, the algorithm adjusts to offer better recommendations next time.
Recommendations in Spotify
Do you want to learn more about the ML model types?
Read our machine learning guide and discover how ML models work and how your company can benefit from it.
Deep learning – a sophisticated evolution of machine learning
Deep learning is a subset of machine learning, but much more sophisticated and mathematically complex. It takes machine learning to the next level, using neural networks modeled after the human brain. Imagine if your brain were on overdrive, crunching data at lightning speed – that’s deep learning!
While traditional ML algorithms often need human adjustments when they produce incorrect results, deep learning algorithms learn on their own through repetition, improving their performance without human intervention.
What is a neural network?
To understand how deep learning works, let’s describe the term neural network. Neural networks, also known as artificial neural networks, are a key component of deep learning algorithms. They’re called “neural” because they imitate the way neurons in the brain communicate and learn.
Neural networks are structured in layers: an input layer, one or more hidden layers, and an output layer.
- Each layer consists of nodes, which are like artificial neurons.
- Each node connects to others.
- Every connection has a weight that determines the strength of the signal passed between them.
There’s also a threshold value for each node that decides whether it activates based on the input it receives.
How does it work?
Input layer
The process begins at the input layer, where data enters the neural network. This layer consists of multiple nodes that receive raw data, which can be an image, text, or sound. Think of this as the front door of a house: it’s where everything comes in before it gets sorted out.
Hidden layers
Next are the hidden layers, where the real analysis occurs. Each hidden layer processes the information from the input layer, focusing on different features. For example, if the task is to identify an animal in an image, one layer might examine its size, while another looks at its fur pattern. This teamwork helps the network make better sense of the data.
Output layer
Finally, the output layer delivers the results. If the task is a simple yes or no question, like whether an image contains a cat, the output layer might have just two nodes.
Deep learning models
The best time to implement AI in your business is now!
Explore our AI consulting and development services and see how we can help you innovate and grow.
What made deep learning necessary?
Deep learning was created to reduce the need for constant human involvement in machine learning.
As we’ve stated before, traditional machine learning relies on humans to manually define features – a process called feature engineering. For example, if you’re teaching an ML model to recognize cats and dogs, you’d have to manually specify things like the shape of ears, tails, or noses.
Deep learning helps achieve this ultimate goal of ML – it cuts out the need to manually label data at every stage.
Although deep learning has been around for a while, it wasn’t widely used until the early 2000s. Back then, the problem wasn’t the theory – it was the lack of large datasets and affordable computing power. Fast forward 20 years, and with the rise of data and cheaper processing, deep learning has finally become a very popular practical solution for many businesses.
What do ML and deep learning have in common?
From a bird’s-eye view, both ML and deep learning find patterns in data. They use datasets to train algorithms based on complex mathematics. During training, these algorithms learn how inputs relate to outputs, which helps them predict results from new data. This learning process mostly happens automatically unlike traditional programming.
Here are some more touch points:
AI techniques
Both ML and deep learning are part of the data science and AI family. They tackle tricky tasks that would take forever with traditional programming.
Statistical foundations
Deep learning and ML lean on statistical methods to train their algorithms. They use techniques like regression analysis and decision trees. So, if you want to be an expert in these fields, brushing up on stats is a must!
Dependence on large datasets
Both ML and deep learning rely on large, high-quality datasets to boost accuracy. An ML model typically needs around 50 to 100 data points per feature. Deep learning models often require thousands.
Diverse application
These two technologies effectively address complex problems in many industries. Traditional programming would take much longer to solve these challenges.
High computational demand
Running ML algorithms needs some serious computing power, and deep learning is even hungrier for resources. Luckily, thanks to tech advancements, they’re now more accessible to everyone.
Learn how we helped an American retailer implement AI-based behavior analysis and sales forecasting
Thanks to our solution, the customer achieved a 7% increase in visitors-to-buyers conversion rate, a 15% rise in data volume collected, and a 35% reduction in monthly infrastructure costs!
What are the key differences between ML and deep learning?
We’ve already highlighted the basics: deep learning goes beyond ML with its complex neural networks and reduced need for human input. While ML uses supervised, unsupervised, and reinforcement learning, deep learning processes data through multiple layers, needing far more computing power.
But beyond these, there are a few more differences that aren’t on the surface:
Types of data
The choice between ML and deep learning often depends on the data you’re working with. ML works best with structured data, like classification tasks or recommendation systems. For example, a business might use ML to predict customer churn based on historical data.
Deep learning requires a higher level of abstraction and is more suited for unstructured data. This makes it ideal for tasks like image recognition or natural language processing, where extracting complex features is key. For example, deep learning could be used to analyze social media posts and determine the overall sentiment.
Training time
Deep learning takes way longer to train than ML. Its networks have to sift through tons of data, which makes it powerful but slow. Think of it like training for a marathon versus a short sprint – deep learning needs more prep time before it’s ready to go.
Interpretability
ML models are easier to explain. You can follow the logic in decision trees or regression models like a simple flowchart. With deep learning, it’s a bit like a “black box”—the more layers there are, the harder it is to figure out how it reached a conclusion.
Performance
Both ML and deep learning shine in different areas. For simpler tasks, like filtering spam emails, ML typically performs faster and more efficiently. But when it comes to more complex problems, like medical image analysis, deep learning takes the lead. Its ability to spot hidden patterns and details allows it to outperform ML in tasks requiring higher accuracy.
Let’s summarize!
Machine learning and deep learning comparison
Get started with machine learning
Have you already chosen the right technology for your business? If not, Aristek is here to help. As part of our ML consulting services, we will analyze your business challenges and needs, audit your data, and recommend the best solution tailored to you. If you’ve already made your choice, our skilled engineers are ready to assist with the development and implementation process.
Book a free consultation, share your thoughts, and let’s get started!
