What is Deep Learning? Use Cases, Examples, Benefits in 2024

Deep learning is a state-of-the-art field in machine learning domain. Deep learning models can learn from examples and they need to be trained with sufficient data. The predictions of deep learning algorithms can boost the performance of businesses. However, they have challenges such as being data hungry, hard to interpret and can be expensive due to the cost of collecting and labelling data.

What is deep learning?

Deep learning, also called deep structured learning or hierarchical learning, is a set of machine learning methods which is part of the broader family of artificial neural network based machine learning methods. Like other machine learning methods, deep learning allows businesses to predict outcomes. A simple example is to predict which customers are likely to buy if they receive discounted offers. Improved models allow businesses to save costs and increase sales.

Deep learning is one of the most popular machine learning methods in commercial applications and interest in deep learning has exploded since 2013 as you can see below.

Why is deep learning relevant now?

While  Geoffrey Hinton and other researchers started to demonstrate deep learning’s potential in 1980s effectiveness, several elements were missing:

• Cheap computing power is required by deep learning. Enough amounts of economical computing power for deep learning applications only became available around 2010s.
• Training data: Researchers used to rely on hand-labeled data for machine learning. However, data generation has increased significantly with new data per year doubling every 2 years since 2010s. Currently, new data generation and storage is expected to grow with a CAGR of 23% until 2025.
• Better algorithms: Years of research also led to more optimized algorithms, further enabling deep learning.

Modern companies armed with an abundance of data, cheap computing power and modern deep learning algorithms are set to take advantage of deep learning models.

How does deep learning work?

Based on training dataset, an Artificial Neural Network (ANN) based model is built and tested against a test dataset to make predictions on your business’ data. Let’s explain each term:

Training data: As its name implies, machine learning is all about learning from previous examples. Training data includes both data that is and will be known, as well as the outcome that needs to be predicted. For example, let’s assume that we are trying to predict which customers are likely to buy if they receive discounted offers. In this case,

• Known data (or input data) is all relevant data about the customer which can include demographic data, previous purchases, online behavior, etc.
• The outcome to be predicted is whether the customer will make a purchase after receiving the offer.

Artificial Neural Network (ANN) is a mathematical model with a structure inspired by brain’s neural circuitry. Though its structure may be complex, it is essentially a function that makes predictions given input variables. We use the word “inspired” because brain’s structure is quite complex compared to even the most complex neural networks, is analog and highly optimized closely coupling processing, computation and software.

Test dataset is not used as part of the training. It has the same format as training data and it is used to test the model’s results and decide whether model’s predictions are accurate enough for the busines goals.

Predictions are outputs of the model. When trying to predict which customers are likely to buy if they receive discounted offers, the model predicts an outcome (will buy, will not buy) for each customer in the dataset. The company can use these predictions to decide which customers to reach out. Furthermore, model can assign a confidence score to each prediction, helping the company further refine the actions it will take. For example, if an incorrect prediction is costlier than a correct prediction, the company may not act on a prediction if the confidence level of the model for that data point is low.

What are its benefits?

Deep learning models can lead to better, faster and cheaper predictions which lead to better business, higher revenues and reduced costs.

• Better predictions: Which business wouldn’t want to be able to call just the customers who are ready to buy or keep just the right amount of stock? All of these decisions can be improved with better predictions.
• Faster predictions: Deep learning, and machine learning in general, automates a company’s decision making increasing its execution speed. Consider customers that leave their contact info to get more details about a tech solution for their company. Maybe it is obvious from the contact info that this is a very high potential and needs to be contacted. Thanks to the model in place, no one needs to manually check that data, the potential customer will be immediately prioritized. Speed is especially important in this example because customers contacted sooner are more likely to convert.

• Cheaper predictions: Companies that do not implement operational decision making models, rely on analysts to make decisions which are orders of magnitude costlier than running deep-learning models. However, deep learning models also have setup time and costs. Therefore, the business case for models need to be investigated before rolling out models.

What are its important use cases?

Deep learning is a machine learning technique so its areas of applications are almost limitless. However, business benefit of a model need to be compared with the cost of setting up such a model.

Any business application would benefit from better predictions. After all, life is the decisions we make and our decisions are as good as our predictions. Examples of applications include:

• Image classification: From recognizing customers who enter the store to automatically identifying defects, image classification applications exist in almost all industries
• Other predictions: Predicting churn in marketing, likelihood to buy in sales, customer’s emotional state from her voice in customer service contact centres are all some of the applications of deep learning

Models have widespread applications areas but also have setup time and costs. Therefore, the business case for models need to be investigated before rolling out models. In short, areas where models provide the best value are:

• Valuable predictions where machines outperform humans. Soon, medical image analysis could be within this domain as for example a cancer diagnosis is quite valuable and machines could be doing better than humans in the near future.
• Lower value predictions that need to be repeated often. Most machine learning models tend to fall into this category. Going through millions of customers to identify the right customers for a campaign is too costly without having a model to pick the right customers.

Industries with the most data are likely to benefit the most from deep learning models. If you want to read more about deep learning use cases in different industries:

• Top 41 Deep Learning Use Cases
• Deep Learning Use Cases in Finance
• Deep Learning Use Cases in Healthcare
• Especially companies leveraging Industrial Internet of Things (IIoT) have access to copious amounts of data which can power deep learning models. Feel free to explore Deep Learning Use Cases in Manufacturing

Which business functions benefit the most from deep learning?

Business functions with more data are likely to benefit more from deep learning. Some data-rich business functions are:

• Commercial functions such as sales, marketing and customer service
• Cost centers such as technology that create detailed log files including granular data

How is deep learning expected to evolve in the future?

Deep learning domain is expected to gain new capabilities and overcome its challenges with new research and studies such as capsule networks and adversarial learning. Feel free to read our article about future of deep learning.

What are the challenges in deep learning?

Deep learning models have challenges such as

• Data privacy/consumer data protection: Deep learning algorithms rely on training data which may include personal or sensitive data. Personal data in training datasets may be demographic information, income, health, interests, etc. This raises concerns about privacy in deep learning applications. However, by encrypting models, companies are able to protect personal data stored in models
• Data hungry
• Hard to explain or interpret
• Biased
• High energy costs

Researchers and industry pioneers have some ideas to overcome these barriers including:

• Minimize use of personal data in models by learning from fewer examples as in the case of few shot learning.
• Provide explanations about the predictions of deep learning models by developing new models following XAI approaches
• Prevent bias in deep learning with multiple approaches like introducing more diversity in the field
• Improving efficiency of deep learning models to accelerate them and reduce deployment and hardware costs. For example, there is significant effort to build better AI chips
• Taking steps to reduce the skill shortage in deep learning domain. Currently deep learning models are hard to build and data science professionals are needed to build advanced models. However, that is changing as companies adopt no code AI solutions.

You can also check our article on deep learning challenges and ways to overcome them.

If you are ready to use deep learning in your firm, we prepared a data driven list of companies offering deep learning platforms.

If you need help in choosing among deep learning vendors who can help you get started, let us know:

This article was drafted by former AIMultiple industry analyst Ayşegül Takımoğlu.