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

  • PF

  • Mar 29, 2023

Machine Learing vs Deep Learning

There are a lot of trending buzzwords right now such as artificial intelligence, machine learning, deep learning, neural networks, self-learning machines, and many more. All these latest keywords sometimes create a lot of confusion for people that are new to these concepts. And as AI is progressing, understanding the latest advancements can seem overwhelming. But to make it easy for the newbie, AI innovations all come down to two major concepts, machine learning, and deep learning. Many posts over the internet use both terms interchangeably when defining examples for artificial intelligence of how Tesla makes self-driving cars and how Netflix knows your watch list. But there are slight differences between these concepts. 

This blog will cover all the significant differences between Deep learning and machine learning and explain how these two concepts dominate conversations on artificial intelligence. Let’s get started.

Machine Learning vs Deep Learning – An Overview

The first step in understating the difference between machine learning and deep learning is to identify that both these concepts are closely related to each other. When diving into more detail we find out that deep learning is the evolution of machine learning, which means that deep learning is a sub-part of machine learning. In deep learning algorithms, engineers utilize the programmable neural network that provides the machine with the potential to make more refined predictions without any help from humans. For newbies, let’s start from scratch.

What is Machine Learning?

Machine learning is a subfield of artificial intelligence that focuses on teaching computer systems to learn and improve without being explicitly programmed. Doesn’t it seem crazy?

It’s essentially the same as teaching your computer to think for itself, spot patterns, and make predictions based on facts. Instead of a teacher at the front of the classroom, algorithms and statistical models handle the hard lifting.

It’s not only for sci-fi movies or technological geniuses, machine learning is increasingly being used in daily applications such as speech recognition, spam filtering, and picture identification. Thus, whether you use Siri or Alexa, you’re already reaping the benefits of machine learning technology without even recognizing it.

How ML Works?

Machine learning operates at its heart by processing massive amounts of data and using statistical models to uncover patterns and correlations. These models are then utilized to create predictions or judgments based on previously unseen facts by the system.

But before you can have any fun, you must first gather some data. And lots and lots of data. This massive amount of data is then divided into two groups: training and testing. The training set is used to construct and train the model, whereas the testing set is used to assess the model’s performance.

When you’ve organized your data, you’ll need to select a suitable method to evaluate it. There are several machine learning algorithms available, each with its own set of strengths and disadvantages. Linear regression, decision trees, and neural networks are some prominent examples.

The algorithm will next utilize the training data to discover patterns and correlations in the data. This procedure is known as “fitting the model” or “training the model.” After training, the model may be used to make predictions or judgments based on fresh data.

Of course, this is an oversimplification of a really complex process, and other factors might influence the success of a machine learning system. Nonetheless, maybe this provides a general sense of how machine learning works.

What is Deep Learning?

Deep learning is the sub-branch of machine learning that uses algorithms differently to make intelligent decisions. Deep learning models can learn from raw data, but typical machine learning models require considerable feature engineering. As a result, they are suited for tasks like image and speech recognition. Deep learning algorithms, on the other hand, require far more data and computer resources to train than typical machine learning models. Before diving deep into the working of deep learning algorithms, let’s first define ANN (Artificial Neural Network) which is the main offering of deep learning algorithms. 

Artificial Neural Networks

Artificial neural networks are computer systems inspired by the structure and operation of the human brain. They are made up of layers of linked nodes, or “neurons,” that collaborate to analyze input and learn from it.

Each neuron in an artificial neural network gets input from other neurons or external data sources and generates an output signal using an activation function. The outputs of one layer of neurons are then sent into the next layer, and so on until the network provides a final output.

Backpropagation is a technique for training artificial neural networks in which the network changes its weights and biases to minimize the difference between its anticipated outputs and the actual outputs in a training dataset. The deep learning chip market is growing quickly and is forecast to exceed 21 billion USD by 2027. 

Machine Learning Vs Deep Learning

To understand deep learning in more detail, check out our comprehensive guide on deep learning solutions and how they work for different operations.

How Does Deep Learning Work?

A deep learning model is designed to analyze data continuously in line with the logical working structure. And to fully mimic the human thinking and reasoning cycle, Deep learning models use ANNs. Artificial neural networks are much more efficient than standard machine learning models and mimic exactly the steps involved in human neuron processing. Because of the high number of parameters and sophisticated topologies involved, ANNs require substantially more computer resources to train than classical machine learning. Nonetheless, ANNs’ growing complexity enables them to attain cutting-edge performance on tasks such as image and speech recognition. A major example of a Deep learning solution is the AlphaGo program which plays the abstract board game Go.  

Key Differences

After exploring both deep learning and machine learning solutions in detail. Here are some key differences between deep learning compared to machine learning:

  • Difference in Architecture

    Deep learning uses neural networks with many layers, while machine learning uses simpler algorithms with fewer layers. On the other hand, Machine learning needs comprehensive feature engineering to determine and extract appropriate components from raw data, while deep learning can learn relevant features automatically.

  • Larger Amount of data

    Deep learning requires large amounts of data to train effectively, while machine learning can perform well with smaller datasets.

  • High Computational resources 

    Deep learning requires significant computational resources, such as specialized hardware and large amounts of memory, while machine learning can run on standard hardware.

  • Demanding Interpretability

    Deep learning models are comparatively more challenging to interpret, as the associations within the raw data learned by the neural network can be complex and difficult to understand. In contrast, machine learning models are often more interpretable, meaning that it is easier to understand how the model arrived at its predictions.

Final Thoughts

In practical terms, deep learning is a more advanced and refined form of machine learning. In fact, the working of both models is almost similar. This is the reason both terms are used interchangeably in the wild. We can say that the capabilities of deep learning are much enhanced compared to standard machine learning models. Another plus of using artificial neural networks or deep learning solutions is the reliability of prediction accuracy. For instance, machine learning models at almost some point require human intervention if the algorithm returns an inaccurate prediction. With deep learning models, the algorithm determines the accuracy of output and requires minimal human effort.