Deep learning vs. machine learning: a comparison

What is machine learning?

Machine learning is a branch of AI that involves using data and algorithms to imitate how humans gain knowledge and learn through trial and error. ML algorithms require massive amounts of data to learn and improve at designated tasks. The process is slightly different from deep learning, which is a subset of ML.

Supervised learning

Supervised learning is a guided learning technique in which a user provides the algorithm with known datasets. Each dataset includes outputs and inputs.

As the algorithm makes predictions, the operator performs corrections so the machine can learn and evolve. The operator repeats this process until the system achieves an acceptable level of accuracy.

Semi-supervised learning

In semi-supervised learning, the operator provides the algorithm with both known datasets and unlabeled data. Labelled data includes tags that help the algorithm understand it. Unlabeled data doesn’t include any tags or identifiers.

By analysing tagged and untagged data, the ML algorithm can learn to process unstructured information.

Reinforcement learning

Reinforcement learning performs regimented processes to teach the algorithm how to use trial and error. The operator creates strict parameters and provides the ML system with a defined set of actions. The algorithm will explore the dataset within those confines and learn which strategies yield desired results.

Unsupervised learning

In the unsupervised learning process, the ML algorithm is provided with a large dataset. It isn’t given any guidance but instead is free to organise and arrange the data however it sees fit.

The difference between deep learning and machine learning

In ML processes, the algorithm has to continuously ingest more information to learn how to make accurate predictions. For instance, an ML algorithm may have to perform feature extraction to gain additional information about a particular dataset.

Conversely, deep learning solutions can use the multi-layered neural network infrastructure to make accurate predictions via its innate data processing capabilities.

Deep learning technology significantly reduces the amount of human intervention needed to produce an accurate output. Additionally, deep learning algorithms can process large datasets, even if they’re unstructured.

Let’s further examine the mechanisms of deep learning versus machine learning by looking at some key differences

Number of data points

Machine learning algorithms can make predictions using only small amounts of data. However, the more data these algorithms have access to, the more accurate the predictions.

Deep learning algorithms must be fed huge amounts of data to yield any output — they can’t function when provided with small datasets.

Featurisation process

In machine learning, users must accurately tag or otherwise identify data features. Generally, ML algorithms can’t conduct independent featurisation — the process of creating new features.

Conversely, deep learning algorithms can learn high-level features from unstructured data. These algorithms can also create new features independently.

Hardware dependencies

Machine learning solutions use three or fewer neural network layers, meaning they don’t need significant computing power to function. As such, ML algorithms can run on lower-end equipment.

During deep learning, the algorithm will perform a staggering number of matrix multiplication operations. Robust hardware is required to facilitate these operations.

Execution time

Most machine learning algorithms can be trained in a few hours, while especially simple algorithms can be trained in only a few minutes. That’s because the algorithm processes information using only a couple of neural network layers.

By contrast, deep learning algorithms process data using many hidden layers. Particularly sophisticated algorithms can take days or even weeks to train.

Output

Machine learning algorithmic outputs are almost always represented as a numerical value. This value may be a classification or score.

Deep learning outputs can take many forms, including sounds, scores or text. This versatility makes deep learning suitable for a broad range of applications, from communicating with consumers to powering a voice-activated TV remote.

Learning approach

ML algorithms break complex learning processes into small, manageable steps. The algorithm will then combine its results into a consolidated output.

Deep learning solves problems using a start-to-finish approach. The algorithm progresses through the problem using raw input data and doesn’t require manual feature extraction.

Benefits of deep learning and machine learning

There are many benefits associated with deep learning and machine learning. Here are some of the most notable ones.

• More sources of data input. According to several estimates, somewhere between 80% and 90% of the data companies collect is unstructured. This data can’t be analysed using standard tools, but combining ML and DL tools allows businesses to tap into it.
• Better, faster decision-making. Deep learning and ML algorithms provide business leaders with actionable insights they can use to guide decision-making processes.
• Increased operational efficiency. According to an US business survey, 33% of respondents cited “time savings” as the top benefit of machine learning tools.
• Improved customer experience. Machine learning tools can significantly improve the customer experience by providing actionable insights into the minds of your target audience. Consumers are on board, too, as 48% of survey participants said they would interact with AI “more frequently” if it improved their experience.
• Reduced costs. By providing a glimpse into customers’ minds, expediting decision-making and increasing overall efficiency, ML and deep learning tools can pave the way for significant cost savings.

As you can see, incorporating machine learning and deep learning algorithms into your workflow can positively affect every facet of your business.

How deep learning and machine learning work together

As deep learning is a subset of machine learning, the two technologies are already interconnected. However, you can intentionally use deep learning algorithms and ML algorithms with one another to parse complex sets of data.

How this works

Deep learning solutions will structure or layer multiple machine learning algorithms to form the aforementioned neural network. As the data progresses through each layer, the algorithm will assess the information and make decisions based on what it learns.

Let’s say you’ve amassed a huge amount of data about customer shopping habits, much of which is unstructured. You can use deep learning technology to sift through and categorise the raw data. Later, you can use ML algorithms to efficiently assess smaller, newly structured subsets of information to discern granular details.

Deep learning and machine learning are being used every day. AI-powered voice assistants are a prime example, as 97% of mobile users already rely on this tech.

Applying deep learning and machine learning

You don’t have to choose between machine learning or deep learning when incorporating AI technologies into your workflow. Instead, you can leverage the complementary capabilities of both. Let’s compare the different industries, business uses and societal applications of deep and machine learning.

Deep learning use cases

Some deep learning use cases include:

• Financial services. Deep learning algorithms can help financial institutions forecast market conditions, guide investments and better serve customers.
• Customer service. Deep learning can enable customer service teams to expedite the delivery of support and predict user needs.
• Law enforcement. Law enforcement agencies can use deep learning to predict crime trends and protect communities.
• Healthcare. Deep learning tools can assist health professionals with making diagnoses and improving patient outcomes.

As deep learning technology continues to evolve, it will undoubtedly make its way into many other industries.

Machine learning use cases

Some ML use cases include:

• Personalised websites. ML allows brands to serve up personalised experiences to consumers based on browsing history and other data.
• Search engines. Likewise, search engines use ML algorithms to predict future behaviour and offer users better results.
• Chatbots. Over time, chatbots can learn how to give users faster, more accurate responses to enquiries.

Find out how Adobe Real-Time Customer Data Platform (CDP) can help you to tap into use cases like these and put machine learning and deep learning to work for your business.

Powered by Adobe Sensei generative AI, Adobe Real-Time CDP makes smarter, faster work easier. Watch the overview video or take a product tour to learn more.