Machine learning and deep learning are frequently mentioned in discussions about technology, artificial intelligence, and data science, but despite their similarities, they are not synonymous. Consider them as related concepts with distinct differences.
In this blog, we’ll explain the key differences between machine learning and deep learning in a clear, understandable manner. By the end, you will understand how each technology works and where it is used in real-world scenarios.
What is Machine Learning?
Let’s get started with machine learning. In simple words, machine learning is like teaching computers to learn from data without giving them specific instructions for every task. Think of showing a child lots of pictures of cats and dogs and then asking them to tell the difference. With enough examples, they’ll get better at recognizing each one. That’s essentially how machine learning works.
Machine learning allows computers to make predictions or decisions based on data using algorithms, which are highly accurate recipes. For example, when Netflix recommends a film or show that you might be interested in, it is using machine learning to look at the films you have watched and match patterns with what other people have watched to recommend new material to you.
What’s interesting about machine learning is that it improves over time as it processes more data. To ensure that the system runs as efficiently as possible, humans are usually required to fine-tune parameters or select the most important pieces of data for a task.
What is Deep Learning?
Now, let us dig deeper with an explanation of deep learning. Deep learning is also a subset of machine learning but is slightly advanced. If machine learning was like a child learning through examples, deep learning could be compared to that same child going to college to study further and dive deep into complex subjects.
It relies on neural networks which basically mimic the nature through which the human brain operates in processing information. A neural network consists of layers that progressively analyze data; this is why it’s referred to as “deep” in deep learning. It works on specific aspects of data, making it perfect for tasks such as image recognition, speech processing, and more.
For example, voice assistants such as Siri or Alexa are based on deep learning. They could understand and respond to all your commands accurately. Another technology behind facial recognition is the same deep learning, through which your face can be identified in a crowd of people or even in your photos with amazing accuracy.
Deep learning over machine learning mainly provides the advantage of the deep learning model identifying automatically what are the important features in data, much less involving human intervention. It’s like a student who can study by themselves without needing a teacher.
When to use machine learning vs deep learning. Here is a very fast guide to know if to use machine learning or deep learning based on your own case.
Key Differences Between Machine Learning and Deep Learning
| Feature | Machine Learning | Deep Learning |
| Data Requirements | Works well with smaller datasets | Requires large datasets for optimal performance |
| Human Intervention | Needs human input to select features and tune models | Can learn and select features automatically |
| Complexity | Simpler algorithms like linear regression, decision trees, etc. | Complex models such as CNNs and RNNs |
| Computational Power | It can run on regular computers or laptops | Requires powerful GPUs for processing |
| Applications | Suitable for tasks like spam filtering, fraud detection, etc. | Ideal for tasks like image classification, speech recognition, etc. |
Relationship Between Machine Learning and Deep Learning
Think of deep learning as a subset of machine learning. All deep learning is machine learning, but not all is deep learning. Both belong to the same family of technologies but serve purposes depending on the problem that you want to solve or the amount of data at your disposal.
Final Thoughts
Machine and deep learning are powerful technologies changing industries worldwide. It doesn’t mean that one is better than the other but that they complement each other, depending on the problem and data at hand. The differences will help you figure out which method suits your needs: whether to build a recommendation system, train a chatbot, or create an intelligent AI application.
You need not be a machine learning expert to appreciate the power of these tools. Whether you’re simply curious or you want to delve into this field, you need to know its basics. Next time that someone mentions “machine learning” or “deep learning,” you will know your stuff and can impress that person with some insight, too!