Deep Learning vs. Machine Learning: What’s the Difference?

In recent years, the field of artificial intelligence (AI) has experienced rapid growth, driven by several factors including the creation of ASIC processors, increased interest and investment from large companies, and the availability of big data. And with OpenAI and TensorFlow available to the public, many smaller companies and individuals have decided to join in and train their own AI through various machine learning and deep learning algorithms.

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If you are curious about what machine learning and deep learning are, their differences, and the challenges and limitations of using them, then you’re in the right place!


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What Is Machine Learning?

Machine learning is a field within artificial intelligence that trains computers to intelligently make predictions and decisions without explicit programming. Depending on the training algorithm, machine learning may train a model through simple if-then rules, complex mathematical equations, and/or neural network architectures.

Many machine-learning algorithms use structured data to train models. Structured data is data organized in a specific format or structure such as spreadsheets and tables. Training a model with structured data allows faster training times, and lesser resource requirements, and provides developers with a clear understanding of how the model solves problems.

Machine learning models are often used in various industries such as healthcare, e-commerce, finance, and manufacturing.

What Is Deep Learning?

Deep learning is a subfield of machine learning that focuses on training models by mimicking how humans learn. Since tabulating more qualitative pieces of information is not possible, deep learning was developed to deal with all the unstructured data that needs to be analyzed. Examples of unstructured data would be images, social media posts, videos, and audio recordings.

Since computers have a hard time accurately identifying patterns and relationships from unstructured data, models trained through deep learning algorithms take longer to train, need huge amounts of data, and specialized AI training processors.

The use of artificial neural networks also makes deep learning hard to understand because the input goes through a complex, non-linear, and high dimensional algorithm where it becomes hard to ascertain how the neural network arrived at its output or answer. Deep learning models have become so hard to understand to the point that many started referring to them as black boxes.

Deep learning models are used for complex tasks that normally require a human to execute, such as natural language processing, autonomous driving, and image recognition.

The Difference Between Machine Learning and Deep Learning

Machine learning and deep learning are two important fields within artificial intelligence. Although both methodologies have been used to train many useful models, they do have their differences. Here are a few:

Complexity of Algorithms

One of the main differences between machine learning and deep learning is the complexity of their algorithms. Machine learning algorithms typically use simpler and more linear algorithms. In contrast, deep learning algorithms employ the use of artificial neural networks which allows for higher levels of complexity.

Amount of Data Required

Deep learning uses artificial neural networks to make correlations and relationships with the given data. Since each piece of data will have different characteristics, deep learning algorithms often require large amounts of data to accurately identify patterns within the data set.

On the other hand, machine learning will require significantly smaller amounts of data to make fairly accurate decisions. Since machine learning algorithms are often simpler and require fewer parameters, models trained through machine learning algorithms could make do with a smaller data set.


Machine learning requires structured data as well as close developer intervention to make effective models. This makes machine learning easier to interpret as developers are often part of the process when training the AI. The level of transparency plus the smaller data set, and fewer parameters makes it easier to understand how the model functions and makes its decisions.

Deep learning uses artificial neural networks to learn from unstructured data such as images, videos, and sound. The use of complex neural networks keeps developers in the dark when it comes to understanding how the model was able to arrive at its decision. This is why deep learning algorithms are often considered to be “black box” models.

Resources Required

As discussed earlier, machine learning and deep learning algorithms require different amounts of data and complexity. Since machine-learning algorithms are simpler and require a significantly smaller data set, a machine-learning model could be trained on a personal computer.

In contrast, deep learning algorithms would require a significantly larger data set and a more complex algorithm to train a model. Although training deep learning models could be done on consumer-grade hardware, specialized processors such as TPUs are often employed to save a significant amount of time.

Types of Problems

Machine learning and deep learning algorithms are better suited to solve different kinds of problems. Machine learning is best suited for simpler and more linear problems such as:

  • Classification: Classify something based on features and attributes.
  • Regression: Predict the next outcome based on previous patterns found on input features.
  • Dimensionality reduction: Reduce the number of features while maintaining the core or essential idea of something.
  • Clustering: Group similar things together based on features without knowledge of already existing classes or categories.

Deep learning algorithms are better used for complex problems that you would trust a human to do. Such problems would include:

  • Image and speech recognition: Identify and classify objects, faces, animals, etc., within images and video.
  • Autonomous systems: Autonomously control/drive cars, robots, and drones with limited or no human intervention.
  • AI game bots: Make AI play, learn, and improve strategies in winning competitive games such as chess, Go, and Dota 2.
  • Natural language processing: Understand human language in both text and speech.

Although you could probably solve simple and linear problems with deep learning algorithms, they are best suited for machine learning algorithms as they require fewer resources to run, have smaller data sets, and require minimal training time.

There Are Other Machine Learning Subfields

You now understand the difference between machine learning and deep learning. If you’re ever interested in training your own model, keep in mind that deep learning is just one domain within machine learning, but there might be other machine learning subdomains that would better fit the problem that you’re trying to solve. If so, then learning other machine learning subdomains should increase your efficiency to solve a problem.

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