Deep Learning vs Machine Learning
Let’s face it—AI is everywhere. It’s on the front lines of devastating global conflict, it’s writing poems, and it’s being used to save wildlife.
While it might be super prevalent, few people truly understand what artificial intelligence does, and the nuances between different subsets of AI can be confusing (to say the least). AI covers a broad spectrum of technologies that enable computers to mimic human behavior, and as it becomes more widely adopted, it’s essential for the average person to understand the nuances between sub-categories of AI, and how they impact their lives every day.
So, what do the terms “machine learning” and “deep learning” mean? How are they used? In this post, we’ll take a step back and explain two fundamental types of AI and how they’re being implemented to drive results at enterprises, today and in the future.
Defining Deep Learning vs Machine Learning—What’s the Difference?
Both machine learning and deep learning fall under the AI umbrella. At their core, they’re techniques that allow computers to mimic certain human behaviors. However, whereas machine learning is a subset of artificial intelligence, deep learning is its own, specific class of machine learning algorithms.
What is machine learning?
Machine learning (ML) is a specific type of AI concerned with extracting patterns from datasets. In ML, machines use the patterns they find to learn, predict, and improve tasks with experience.
There are two types of machine learning—supervised and unsupervised. In supervised learning, all the data being used is labeled, including defined inputs and outputs. It’s pre-categorized data, and the algorithm knows what it’s looking for.
In unsupervised learning, on the other hand, the datasets are unlabeled, and the algorithms are tasked with finding hidden, undefined patterns in the data.
Common machine learning algorithms
When tackling a problem with machine learning, there are a variety of algorithms to choose from. Here’s a look at a few of the most common:
- Decision Trees: Decision trees are supervised algorithms that work primarily on classification problems. While decision trees look fairly simple, they can be applied to a variety of use cases, such as loan approvals and determining a customer’s willingness to purchase a particular product.
- Linear Regression: Another popular supervised algorithm, linear regression estimates real values based on the relationship between two variables. It’s often used to forecast trends, price items, and evaluate the market effectiveness of a product.
- K-Means: K-means is one of simplest unsupervised learning algorithms used on clustering problems. In K-means, data points are grouped based on their proximity to other clusters, known as centroids. K-means is useful in customer segmentation, recommendation engines, and image classification.
What is deep learning?
Deep learning is its own subset of machine learning. It specifically refers to ML algorithms using complex, multi-layer neural networks. In deep learning, the machine trains itself to perform a task with no human interference needed.
When someone claims that deep learning and machine learning are radically different, they’re wrong. Think of it this way—while all deep learning is machine learning, not all machine learning is deep learning, like how all rectangles aren’t necessarily squares.
Common deep learning techniques
Deep learning algorithms make the computation of multi-layer neural networks possible. Here are a few common DL algorithms:
- Convolutional Neural Networks (CNNs): CNNs specialize in analyzing and classifying image, speech, and audio inputs using feature extraction. CNNs are used in facial recognition, document analysis, and medical image computing.
- Generative Adversarial Networks (GANs): Similar to CNNs, GANs work in advanced pattern recognition. However, GANs specifically work in generative modeling. Deep fakes, super image resolution, denoising X-rays, and face aging are all everyday examples of GANs at work.
- Autoencoders: Autoencoders are used to lessen the noise in data by reducing data dimensionality and focusing on true value areas. Some examples of autoencoders include anomaly detection and improving image quality.
Features of Machine Learning vs Deep Learning Algorithms
Now that we’ve covered the basics, let’s dive into some more tactical features that separate deep learning from other machine learning algorithms. The following will help you determine if your business use case is better suited to traditional ML, or if only deep learning capabilities will measure up:
Amount of data required
Typically, deep learning algorithms require much more training data than other types of machine learning. While deep learning doesn’t necessarily require big data, the more large, diverse data sets a deep learning model can learn from, the more accurate it will be. If you don’t have enough data points to extract complex patterns from, you’re better off using another machine learning technique.
In all machine learning algorithms, the output is typically numerical or some type of score or range. In a classification problem, the result will either be this or that. Only in deep learning models does the output vary to have a wider range of formats including text, sound, and images (think: photos generated by GANs).
If you don’t have a lot of time to run your model, deep learning is not the way to go. Most traditional machine learning models are faster than deep learning models, as deep learning algorithms have many layers and thus take longer to train.
Machine Learning Use Cases
Machine learning is an essential tool in a modern organization’s tool belt. Its various use cases include:
Predicting customer churn
Want to know which of your customers are likely to stay loyal, and which are most likely to leave? Machine learning can help you identify customers at risk of churn so you can optimize your marketing efforts and messaging.
Filtering spam is one of the biggest tasks email providers grapple with on a daily basis. Machine learning is the most accurate and efficient method for email providers to identify and filter junk mail out of your inbox.
Forecasting home prices
Using linear regression algorithms, ML can learn the relationships between your target parameters and existing parameters from statistical data. One example of this is predicting home prices based on the value of other homes in the area, the location, etc.
Deep Learning Use Cases
Deep learning use cases are increasing every day as models become more and more accurate, and their capabilities become increasingly human-like. Check out a few common examples of deep learning:
Deep learning can do more than generate images—it can generate appropriate captions that correspond with images, too. Algorithms can recognize an image’s features and context to come up with human-readable descriptions using natural language processing and computer vision.
Have you ever wondered who’s behind the chatbots you interact with when you go to a website? The answer: deep learning algorithms. DL-powered chatbots remove the need for a human customer service agent to be involved in chats and handle conversations on their own.
Self-driving cars use three sensors—their camera, LiDAR, and RADAR—that constantly stream data to be processed by deep learning algorithms. These algorithms can be used to detect obstacles, traffic lights, nearby cars, lane lines, and more objects that are essential to keep self-driving cars functioning safely
A Note on Model Interpretability
Going back to what we said at the beginning of the article, even the most straightforward machine learning models can be pretty confusing.
Enter: model interpretability. When looking at an interpretable model, it’s easy to understand the reasoning behind the decisions the model made—it’s transparent.
Interpretability is a key part of considering when deep learning models should be used. Most deep learning models are black box models, meaning they’re so complex they can’t be fully comprehended. While these models typically have more advanced capabilities, they lack inherent transparency, creating a tricky trade-off for data scientists.
Machine learning and deep learning are already changing how we interact with technology on an everyday basis—they power surge pricing on Uber and recommendation engines on Netflix—and they’re still in the early phases of adoption. The future of machine learning promises to change our world even further, from perfecting self-driving cars to improving the quality of agricultural harvests to predicting diseases before they’re diagnosed. The use cases and potential transformations are practically limitless.
Determining the best use cases for ML and deep learning at your organization can be confusing. Request a free AI assessment to let us help you start making an impact today.