Skip to main content

Article 11 min read

Deep learning vs. machine learning

Uncover the inner workings of machine learning and deep learning to understand how they impact the tools and software you use every day.

By Patrick Grieve, Contributing Writer

Last updated November 14, 2023

Examples of machine learning (ML) and deep learning (DL) are everywhere.

For instance, these algorithms enabled Tesla to manufacture self-driving cars, Netflix to recommend shows that subscribers want to watch, and Facebook to curate user Feeds and share accurate tagging suggestions.

Together, ML and DL can power AI-driven tools that push the boundaries of innovation. If you intend to use only one, it’s essential to understand the differences in how they work. Read on to discover why these two concepts are dominating conversations about AI and how businesses can leverage them for success.

Table of contents

What is the difference between machine learning (ML) and deep learning (DL)?

Deep learning is an evolution of machine learning. Both are algorithms that use data to learn, but the key difference is how they process and learn from it.

While basic machine learning models do become progressively better at performing their specific functions as they take in new data, they still need some human intervention. If an AI algorithm returns an inaccurate prediction, then an engineer has to step in and make adjustments.

With a deep learning model, an algorithm can determine whether or not a prediction is accurate through its own neural network—minimal to no human help is required. A deep learning model is able to learn through its own method of computing—a technique that makes it seem like it has its own brain.

Other key differences include:

  • Machine learning consists of thousands of data points while deep learning uses millions of data points. Machine learning algorithms usually perform well with relatively small datasets. Deep Learning requires large amounts of data to understand and perform better than traditional machine learning algorithms.
  • Machine learning algorithms solve problems by using explicit programming. Deep learning algorithms solve problems based on the layers of neural networks.
  • Machine learning algorithms take relatively less time to train, ranging from a few seconds to a few hours. Deep learning algorithms, on the other hand, take a lot of time to train, ranging from a few hours to many weeks.
machine_vs_deep_learning_graphic

What is AI and how does it relate to deep learning and machine learning?

In practice, artificial intelligence (AI) means programming software to simulate human intelligence. AI can do this by learning from data and algorithms such as machine learning and deep learning.

What is the difference between AI vs. machine learning vs. deep learning?

Now that you’re familiar with machine learning and deep learning, let’s throw AI into the mix.

machine_vs_deep_learning_blocks_graphic

Here’s an overview of how all three of these technologies work together to power AI-driven tools and programs:

  • Artificial intelligence: AI is a broad field that encompasses deep learning and machine learning. Its goal is to develop smart tools that can carry out cognitive functions such as problem-solving, sentiment analysis (analysis of text to understand the sender’s tone and emotions), and decision-making.
  • Machine learning: ML delivers accurate predictions and decisions by identifying patterns in its training data.
  • Deep learning: DL is a subset of machine learning. With this model, an algorithm can determine whether or not a prediction is accurate through a neural network without human intervention. Deep learning models can build extensive knowledge over time, acting as a brain, of sorts.

What is machine learning?

Machine learning definition: A branch of AI trained on statistical models and algorithms, which enable it to make predictions and decisions. Using training and historical data, machine learning algorithms can improve and adapt over time, enriching its capabilities.

Machine learning relies on human engineers to feed it relevant, pre-processed data to continue improving its outputs. It is adept at solving complex problems and generating important insights by identifying patterns in data.

How does machine learning work?

Machine learning is an algorithm that enables computers and software to learn patterns and relationships using training data. A ML model will continue to improve over time by learning from the historical data it obtains by interacting with users.

machine_learning_process_graphic

Once the engineer finishes training the AI and optimizing its performance, it will follow a standard process. The algorithm will:

  1. Receive new information via a user query.

  2. Analyze the data.

  3. Find a pattern.

  4. Make a prediction.

  5. Send an answer back to the user.

Machine learning example

Here’s an example of how machine learning and reinforcement learning (an area of ML where the algorithm makes decisions by interacting with its environment) powers on-demand music and video streaming services like Spotify, Apple Music, and YouTube.

For a music service to recommend new songs or artists to a listener, machine learning algorithms associate the listener’s preferences (e.g., saved songs, playlists, followed artists, and skipped songs) with other listeners who have similar musical tastes.

What are the different types of machine learning?

The primary difference between various machine learning models is how you train them. Although, you can get similar results and improve customer experiences using models like supervised learning, unsupervised learning, and reinforcement learning.

Leverage AI to transform customer service

AI plays an important role in modern support organizations, from enabling customer self-service to automating workflows. Learn how to leverage artificial intelligence within your business to enhance productivity and streamline resolutions.

What is deep learning?

Deep learning definition: a subfield of machine learning that structures algorithms in layers to create an “artificial neural network” that can autonomously learn and make intelligent decisions.

Deep learning models can analyze data continuously. They draw conclusions similar to humans—by taking in information, consulting data reserves full of information, and determining an answer.

This technique enables it to recognize speech and images, and DL has made a lasting impact on fields such as healthcare, finance, retail, logistics, and robotics.

How does deep learning work?

Deep learning applications work using artificial neural networks—a layered structure of algorithms. To use a deep learning model, a user must enter an input (unlabeled data). It is then sent through the hidden layers of the neural network where it uses mathematical operations to identify patterns and develop a final output (response).

The algorithm’s design pulls inspiration from the human brain and its network of neurons, which transmit information via messages. Because of this, deep learning tends to be more advanced than standard machine learning models.

machine_learning_process_graphic

What is a neural network?

Neural networks—also called artificial neural networks (ANNs)—are a way of training AI to process data similar to how a human brain would. Neural networks function by receiving information via an input, allowing that information to flow between nodes (intersection points) to deep, hidden layers (similar to neurons firing in your brain), where the algorithm learns before sending back a final answer through the output layer.

Without neural networks, there would be no such thing as deep learning. The depth of the algorithm’s learning is entirely dependent on the depth of the neural network.

What are some examples of deep learning?

Google DeepMind created a computer program, AlphaGo, with its own neural network that excels at the strategy game, Go.

  • About the game: The game is easy to learn, but gamers must have a quick wit and good intuition to play well.
  • AlphaGo’s achievement: With deep learning, the program learned to play the abstract board game, Go. Before long, AlphaGo was defeating world-renowned Go masters, proving that with deep learning, machines could grasp abstract concepts and complex techniques.
  • How AlphaGo did it: By playing against professional Go players, AlphaGo’s deep learning model quickly succeeded by identifying patterns and maneuvers that were never seen before in AI. And the program did so without instructions on what move to make and when, as is traditionally seen among machine learning models.

What are the different types of deep learning algorithms?

Machine learning empowers computers to carry out impressive tasks, but the model falls short when mimicking human thought processes. Fortunately, that’s exactly where deep learning excels.

Below are some of the most common types of deep learning algorithms.

Convolutional neural networks

Convolutional neural networks (CNNs) are algorithms that work like the brain’s visual processing system. They can process images and detect objects by filtering a visual prompt and assessing components such as patterns, texture, shapes, and colors.

CNNs often power computer vision and image recognition, fields of AI that teach machines how to process the visual world.

Recurrent neural networks

Recurrent neural networks (RNNs) are AI algorithms that use built-in feedback loops to “remember” past data points. RNNs can use this memory of past events to inform their understanding of current events or even predict the future.

They are particularly useful for data sequencing and processing one data point at a time.

A deep neural network can “think” better when it has this level of context. For example, a maps app powered by an RNN can “remember” when traffic tends to get worse. It can then use this knowledge to predict future drive times and streamline route planning.

Multilayer perceptron

Multilayer perceptrons (MLPs) are a type of algorithm used primarily in deep learning. MLPs are classified as a feedforward neural network meaning the information the user inputs only flows in one direction without using feedback loops which makes it better at processing unpredictable data and patterns than other algorithms.

MLPs can be used to classify images, recognize speech, solve regression problems, and more.

How do machine learning and deep learning impact customer service?

Many of today’s AI applications utilize deep learning and machine learning algorithms in customer service. A few key examples of this include:

  • Agent assistance: Machine learning and deep learning enable natural language processing (NLP), sentiment analysis, and continuous learning, making it possible for bots to streamline support experiences and empower agents with insights, such as what a customer’s request is about, the language it’s in, and whether it’s positive or negative.
  • Customer service chatbots: Conversational bots can use ML and DL to understand customer intent (reason for messaging), personalize responses, and answer customer questions without a human agent ever getting involved. They also help agents by collecting customer data via in-chat forms.
  • Workflow automation: ML and DL can optimize workflows through intelligently routing customer requests to the right agent and automatically suggesting pre-written responses to customer questions.
  • Predictive analytics: As the name implies, predictive analytics anticipate what will happen in the future using historical data and machine learning capabilities to help support teams get ahead of customer issues.
  • Fraud detection: Deep learning and machine learning can help support teams by proactively flagging security issues such as an unsafe password or a suspicious login.

All of these tools are beneficial to customer service teams and can improve agent capacity.

Deliver seamless customer experiences with intelligent AI

According to the Zendesk Customer Experience Trends Report 2023, 71 percent of customers believe AI improves the quality of service they receive, and they expect to see more of it in daily support interactions. Combined with the time and costs AI saves businesses, every service organization should be incorporating AI into customer service operations.

Fortunately, Zendesk offers a powerhouse AI solution with a low barrier to entry. Zendesk AI was built with the customer experience in mind and was trained on billions of customer service data points to ensure it can handle nearly any support situation.

Using our software, you can efficiently categorize support requests by urgency, automate workflows, fill in knowledge gaps, and help agents reach new productivity levels.

Related stories

Nucleus Research report: Zendesk vs. Freshworks

Nucleus Research found that users prefer Zendesk vs. Freshworks due to our ease of use, adaptability and scalability, stronger analytics, and support and partnership.

White Paper

2023 Gartner® Magic Quadrant™

Every year, Gartner evaluates vendors in the customer service and support space. The Gartner Magic Quadrant…

Article
4 min read

CX Trends 2024: Unlock the power of intelligent CX

Ten trends every CX leader needs to know in the era of intelligent CX, a seismic shift that will be powered by AI, automation, and data analytics.

Infographic

5 facts to help you stay cybersecurity savvy

Learn about the current state of cybersecurity and our recommended best practices for a secure Zendesk Suite experience.