Is Machine Learning And Deep Learning The Same Thing?

a group of machine learning engineers work on a project

Machine learning and deep learning are two very hot topics right now in the world of technology, particularly in the world of audio classification.

But what exactly are machine learning and deep learning? And are they the same thing?

Machine learning and deep learning are both forms of AI. (Artificial intelligence). Deep learning is a subset of machine learning. Machine learning needs labelled input user data to make decisions, whereas deep learning can work data out on its own to identify patterns.

Machine learning is constantly improving as it gains more labelled data to work with, but deep learning can potentially become much more powerful than machine learning as it doesn’t require the same amount of data to function.

In this article, I will cover the basics so we can understand the differences between machine learning and deep learning, covering:

  • Are machine learning and deep learning the same thing?
  • What are the differences between machine and deep learning?
  • Can we learn deep learning without machine learning?
  • When would you use machine learning or deep learning?

Are Machine Learning And Deep Learning The Same Thing?

Machine learning and deep learning are not the same things. Deep learning is a subset or form of machine learning, and they vary fundamentally in how they operate.

Both machine learning and deep learning are forms of AI. (Artificial intelligence).

Machine learning comprises a class of algorithms that solve AI problems. Deep learning, being a subset of machine learning, comprises a specific class of machine learning algorithms called neural networks.

The following image shows how both machine learning and deep learning sit, relative to each other, in the world of artificial intelligence.

What Are The Differences Between Machine And Deep Learning?

The fundamental difference between machine and deep learning algorithms is human input.

With machine learning, a programmer will feed pre-labelled data to an algorithm and “train” the algorithm on what outcome is required.

The more data the algorithm gets, the better it gets at making decisions and giving a desired result.

While a machine learning algorithm can train and improve, it still needs structured input data and human intervention if things go wrong.

Deep learning, on the other hand, can input vast amounts of unstructured data, learn on its own through repetition, and improve its outcomes without the need for human intervention.

The algorithms that drive deep learning are called neural networks, which are inspired by the human brain.

Just like our brains can pass vast amounts of unstructured data between the biological neurons in our brain, deep learning uses artificial neurons, grouped within layers, to process data through a web of interconnected non-linear algorithms.

I like to think of deep learning as the evolution of machine learning.

The following table shows a very simple overview of some fundamental differences between machine and deep learning algorithms.

Machine LearningDeep Learning
Input data must be structuredCan work with unstructured data
Easier to build modelsMore complex
Less accurateMore accurate
Can train on smaller amounts of input dataLarge amounts of data needed
Good for simple correlationsGood for complex, non-linear correlations and picking patterns.
Moderate processing power neededHigh processing and GPU (graphics processing unit) needed
Human input neededCan learn on its own
This Table Shows How Machine And Deep Learning Compare

Can We Learn Deep Learning Without Machine Learning?

In theory, you can jump straight into deep learning without a machine learning background, but this is not advisable.

Since deep learning is a subset of machine learning, it is logical and inevitable that you must understand the principles of machine learning before fully grasping the concepts of deep learning.

When Would You Use Machine Learning Vs Deep Learning?

It can always be tricky to understand which algorithm is best suited for a particular application.

Which type of algorithm you use will depend greatly on your application, what time and resources you have available and what question you are trying to answer.

As a general rule, if you do not have a powerful PC, GPU or machine, and/or only have small amounts of data, then it is best to stick with machine learning.

On the other hand, if you have excellent hardware in the form of a powerful GPU and lots of data, then deep learning could be the better approach.

In a nutshell, which one you pick depends on your data and the type of problem you are trying to solve, assuming that hardware and time limitations are not an issue.

When it comes to audio classification, deep learning is often chosen.

To identify the differences between audio files, an audio input (in the form of .wav files, for example), are converted to spectrograms. These spectrograms are then classified just like any other image classification deep learning algorithm.

The spectrogram, which is a visual representation of the sound, can tell us a lot about the frequency and amplitude of the original sound, which can then be processed using deep learning.

Final Thoughts

Machine learning and deep learning are two powerful types of algorithms that you can use for a variety of tasks.

While machine learning is good for understanding relationships between data points, deep learning can identify patterns in unstructured data.

If you have limited resources or data, machine learning would be the better option. However, if you have more processing power and data available, then deep learning would be a better choice.

For audio classification, we often choose deep learning because it can identify patterns in spectrograms, which is a visual representation of the sound. Ultimately, which algorithm you choose depends on your application and what resources you have available.

Engineer Your Sound

We love all things audio, from speaker design, acoustics to digital signal processing. If it makes noise, we are passionate about it.

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