Artificial Intelligence (AI) is the new thing on the street. Everyone is talking about it, and everyone wants to be part of it in one way or another. From chatbots that can hold conversations to self-driving cars and systems that can help detect diseases, AI is becoming increasingly integrated into our everyday lives.
But have you ever stopped to ask yourself: What actually makes Artificial Intelligence intelligent?
I like to think that the answer to this question lies in its subfields.
Artificial Intelligence is a broad field of computer science focused on building systems that can perform tasks that typically require human intelligence. Like deciding whether or not that apple is still safe to eat or if it has gone bad. These tasks include reasoning, problem-solving, learning, and understanding language. Yep, it's really cool.
Within this broad field, we have several related areas, including Natural Language Processing (NLP), Computer Vision, Robotics, and Expert Systems, among others. Each of these explores different approaches to building intelligent systems.
Today, however, we're focusing on two terms that are often used interchangeably, sometimes even by people working in tech: Machine Learning (ML) and Deep Learning (DL).
Deep Learning took off in the 2010s because three things came together: much more data, powerful GPUs, and better training techniques. A big moment was 2012, when a deep neural network called AlexNet won the ImageNet image-recognition competition by a wide margin. After that, Deep Learning kept improving results in speech, language, and image-related tasks. This is why Deep Learning is such a huge topic in the tech space.
Are they the same thing? If not, what makes them different? And why does everyone seem to be talking about Deep Learning?
Let's break it down.
First, we need to define Machine Learning and Deep Learning.
1. What Is Machine Learning?
Machine Learning is a subfield of Artificial Intelligence that allows computers to learn patterns from data and use those patterns to make predictions or decisions without being explicitly programmed with fixed rules for every possible situation.
Basically, we're giving a computer a bunch of examples of, for example, houses, their details, and their prices, and allowing it to look for patterns in that data.
Like the example I mentioned, predicting house prices based on features such as location, size, and number of bedrooms.
Actually programming this, including all the rules, would be a lot of work. Imagine the number of conditions and scenarios you would have to account for just to produce a model that is somewhat accurate. It would be a lot of work, and that's one of the reasons ML exists.
Instead of manually writing every rule, we allow an algorithm to learn relationships from examples. The resulting model can then use what it has learned to make predictions about houses it hasn't encountered before.
Pretty cool, right?
2. What Is Deep Learning?
Deep Learning is a specialized branch of Machine Learning that uses artificial neural networks with multiple layers to learn increasingly complex patterns from data.
Each layer transforms its input into a representation that helps the network learn useful features. For example, in a Convolutional Neural Network (CNN) trained on images, earlier layers might learn to detect edges and simple textures, while deeper layers may learn to recognize more complex shapes and object parts.
It's called Deep Learning because of the multiple layers in these neural networks.
Deep Learning was loosely inspired, in part, by the structure and functioning of the human brain, though artificial neural networks are a hugely simplified version of the real thing.
HOW COOL IS THAT? Honestly, I need a second to appreciate Deep Learning.
Anyway, now that I've finished fangirling, let's get back to business.
Deep Learning uses these artificial neural networks to learn patterns from data, sometimes with very little manual feature engineering.
A good example is classifying fish species from photographs. Imagine having thousands of photographs of different fish species.
We want to build a model that can identify the species in a photograph it has never seen before.
With Deep Learning, we can train a neural network to learn useful visual features from the images and use them to classify the fish.
In practice, you'd usually start from a pretrained model rather than training from scratch.
Instead of manually telling the model which edges, colours, textures, or shapes to look for, the network can learn useful combinations of these features during training.
And that is one of the things that makes Deep Learning so interesting!
Of course, it doesn't mean we can simply throw data at a neural network and expect magic. We still need suitable data, a carefully designed model, training, and evaluation. The model's performance depends on several factors, including the quality of the data and how well the model is trained.
3. What's the Difference?
Now that we understand both concepts, let's talk about their differences.
First, remember this: Deep Learning is a type of Machine Learning. They aren't two completely separate fields competing against each other.
Think of Artificial Intelligence as a big umbrella. Under that umbrella, we have Machine Learning, and under Machine Learning, we have Deep Learning.
Yeah, we have an umbrella in an umbrella which is also in a bigger umbrella.
But before we dive in, we need to define feature engineering.
Feature engineering means choosing or creating the inputs a model learns from. For house prices, that could be turning year built into age of house, or calculating price per square metre.
Traditional Machine Learning often relies heavily on this step. Deep Learning can reduce the need for manual feature engineering when working with data like images, audio, and text. However, on tabular data, well-engineered features combined with simpler models can often still perform very well.
So, what actually makes them different?
A. How They Learn From Data
One major difference is how they learn useful features from data.
Traditional Machine Learning approaches often rely on people to select or engineer the features that a model uses.
For example, if we wanted to predict house prices, we might provide a model with the house's size, location, number of bedrooms, and age.
We decide which information is relevant and give the model that information to learn from.
The house-price example is called supervised learning, because the model learns from examples with known answers. There are other types, but we'll leave those for another post.
Deep Learning can go a step further by learning useful features directly from less-processed data.
Remember our fish classification example? Instead of manually extracting features from each photograph, a Deep Learning model can learn useful visual representations from the images themselves.
This doesn't mean traditional Machine Learning cannot process images, or that Deep Learning never needs feature engineering. The difference is in how much of the feature-learning process the model can handle.
B. Data Requirements
Another difference is the amount of data they typically benefit from.
Many traditional Machine Learning algorithms can perform well on relatively small datasets, particularly when the data is structured and the relevant features are available.
Deep Learning models often benefit from larger datasets because they may need many examples to learn useful representations and generalize well to new data.
However, this isn't a strict rule. The amount of data required depends on the problem, the model, and other factors.
Techniques such as transfer learning can also allow Deep Learning models to perform well when the available dataset is relatively small.
C. Computational Resources
Let's talk about computing power.
Traditional Machine Learning models, such as decision trees and logistic regression, can often be trained with relatively modest computing resources.
Deep Learning models can be much more computationally expensive, particularly when they have many parameters or are trained on large datasets.
This is one reason why GPUs are commonly used to train Deep Learning models. They can perform the large number of mathematical operations involved in neural network training efficiently.
This is because GPUs are good at running many calculations in parallel, especially the matrix mathematics neural networks rely on.
And yes, this can also mean longer training times and higher costs.
D. Examples of Algorithms
Machine Learning includes several approaches, such as:
- Linear and logistic regression
- Decision trees
- Random forests
- Support Vector Machines (SVMs)
- Gradient Boosting, such as XGBoost
Deep Learning uses neural network architectures such as:
- Convolutional Neural Networks (CNNs): Commonly used in image-related tasks.
- Recurrent Neural Networks (RNNs): Designed to process sequential data.
- Transformers: Widely used in modern language models and other applications.
There is a lot more to explore within these categories, but we'll save that for another day.
For now, here's a quick comparison:
| Feature | Traditional Machine Learning | Deep Learning |
|---|---|---|
| Relationship | A subfield of AI | A subfield of ML |
| Feature extraction | Often involves manually selecting or engineering features | Can learn features directly from raw or minimally processed data |
| Data requirements | Can work well with smaller datasets | Often benefits from larger datasets |
| Computing power | Often requires fewer resources | Can require substantial computing resources |
| Interpretability | Some models are relatively easy to interpret, such as linear models and small decision trees | Complex neural networks can be difficult to interpret |
| Examples | Decision trees, random forests, logistic regression | CNNs, RNNs, Transformers |
These are general tendencies rather than absolute rules. The best approach depends on the problem you're trying to solve.
4. Can Machine Learning Work Without Deep Learning?
YES. Absolutely!
Remember when I said that Deep Learning is a branch of Machine Learning? That means Machine Learning existed as a field of study long before modern Deep Learning became popular.
You can build useful Machine Learning systems without using a single neural network.
For example, imagine you're building a system to predict whether a customer is likely to leave a service.
You could use logistic regression, a decision tree, or a random forest to learn patterns from historical customer data and predict which customers are at risk of leaving.
You don't necessarily need a Deep Learning model for this.
In fact, using a complex neural network when a simpler model can solve the problem effectively may introduce unnecessary computational costs and complexity.
The goal isn't to use the most complicated algorithm available. The goal is to solve the problem effectively.
Deep Learning is powerful, but it isn't automatically the best choice for every Machine Learning task.
Sometimes, a simpler model is all you need.
5. When Should You Use Each?
So, how do you decide whether to use traditional Machine Learning or Deep Learning?
It depends on the problem, the data you have, the resources available to you, and the results you need.
Consider Traditional Machine Learning When:
- You're working with structured data, such as spreadsheets or databases.
- You have a relatively small dataset.
- You need a model that is easier to interpret.
- You have limited computational resources.
- Simpler algorithms can achieve the performance you need.
Consider Deep Learning When:
- You're working with complex data, such as images, audio, or natural language.
- You have enough data or access to a suitable pretrained model.
- The problem may benefit from learning complex representations.
- You have the computational resources needed to train or run the model.
- Traditional approaches aren't achieving the performance you need.
Again, these are guidelines, not strict rules. Traditional Machine Learning can work with images and text, while Deep Learning can work with structured data.
The important thing is to understand the problem before deciding which approach to use.
And please, don't use Deep Learning just because it sounds more impressive. Nobody gets bonus points for making a simple problem unnecessarily complicated!
Final Thoughts
Artificial Intelligence is a huge field, and there's so much to explore within it.
Machine Learning allows us to build systems that learn patterns from data, while Deep Learning takes this further by using multilayered neural networks to learn complex representations.
Neither is universally better than the other. They have different strengths, limitations, and use cases.
If you're just getting started with AI, my advice is to build a solid foundation in Machine Learning before diving deep into Deep Learning.
Understanding concepts such as training, testing, overfitting, loss functions, and model evaluation will make your Deep Learning journey much easier.
And remember, you don't have to use the most complex model to solve a problem well. You just need to understand your problem well enough to choose the right tool.
Now, go forth and build something cool.
See you next time!
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