What is deep learning? It’s a term you’ve probably heard of at this point – particularly if you’re familiar with the evolving world of artificial intelligence. After all, deep learning isn’t a “new” concept. But most people didn’t have an opportunity to really experiment with deep learning tools in their day-to-day lives until OpenAI released ChatGPT.
Now, deep learning is playing a more significant role in countless tools and resources we use for everything, from customer service to content creation and online search. Still, even as deep learning models continue to proliferate the world around us, many people don’t fully understand how they work – how they differ from machine learning models and why they’re beneficial.
Here, we’ll answer the question “What is deep learning?” in-depth, provide insights into its benefits (and potential challenges), and help you understand the use cases for deep learning models.
What is Deep Learning?
Deep learning is a form of machine learning solution that leverages multilayered neural networks (usually referred to as deep neural networks) to help AI systems complete tasks.
It essentially gives AI tools a framework that allows them to simulate the complicated decision-making processes of the human brain.
Deep learning models power some of the most impressive AI applications and tools in the modern world, from Google Gemini and IBM Watson, to ChatGPT. With deep learning, developers can create applications and services that enhance automation in every industry. Deep-learning models can perform tasks and make decisions at speed without human input.
What makes deep learning so difficult to understand, for most people, is its close connection to machine learning.
Machine Learning vs Deep Learning: What’s the Difference?
Deep learning is a “subset” or “type” of machine learning system. With machine learning, computers can learn from data and use algorithms to complete tasks they haven’t been explicitly programmed to perform. Similarly, deep learning solutions can learn from data, evolve, and complete tasks autonomously.
The main difference between the two concepts is the structure of the underlying “neural network architecture” in the model. Traditional machine learning models use “simple” neural networks with just one or two computational layers. Alternatively, deep learning models can process information by using dozens, hundreds, or thousands of layers.
Another key difference is in how the models are trained. Typical machine learning models require significant input and effort from human beings during training. They mainly rely on “supervised” training methods to ensure accurate outputs. For instance, if you were training a machine learning model for image recognition, you’d need to manually label hundreds of thousands of images, create algorithms for the system to use to process images, and test the performance of the model constantly.
Alternatively, deep models can use “unsupervised learning” strategies. This means they can automatically extract characteristics, features, and relationships they need from data, to complete tasks. They can also autonomously refine their outputs over time, learning from every activity.
Machine Learning vs Deep Learning: The Pros and Cons
So, is deep learning better than machine learning? The answer to that question really depends on your specific needs and use cases. There are pros and cons involved in using all types of AI models.
The Benefits of Deep Learning over Machine Learning
- Efficient unstructured data processing: Machine learning tools find unstructured data (like text documents) tough to process. The training dataset can have infinite variations, making it difficult to understand. Deep learning models, however, can comprehend unstructured data and the relationships between information more effectively.
- Improved pattern discovery: Deep learning models can “dive deeper” into the information they’re given. They can process huge volumes of data quickly, and reveal new insights – even when they’re not trained to do so. For instance, a model asked to analyze customer purchasing preferences could surface decision-making trends you weren’t aware of before.
- Unsupervised learning: Deep learning models learn and improve over time, based on user behavior and the information they’re exposed to. They don’t need huge, labeled datasets to improve their performance. This means it’s generally easier to create a model that becomes more effective and accurate over time.
The Challenges of Deep Learning over Machine Learning
- High data and computational requirements: Deep learning models still need access to huge volumes of data – including labeled data sets for training. They also rely on significant computational resources to work. This can make it extremely difficult for companies with limited budgets to create deep learning models.
- Errors and overfitting: Deep learning models can sometimes “overfit” to the training data, meaning they learn from noise in the data, rather than the relationships between information. This can lead to poor performance. In some cases, they can also show evidence of bias based on the data they’re trained on, leading to ethical issues.
- Interpretability: Speaking of ethical issues, advanced models can be challenging to interpret. It’s not always easy to understand directly why these models make certain decisions, which leads to issues with AI explainability.
How Deep Learning Models Work
One of the most common questions to follow “What is deep learning?”, and “How does it differ from machine learning?”, is “How do these models work?”
As mentioned above, deep learning models rely on neural networks, which mimic the function of the human brain. These networks use data inputs, weights, and bias to accurately recognize, describe, and classify objects within data.
Every deep neural network consists of various layers of interconnected nodes. Each layer builds on the previous layer to refine and optimize the model’s performance. The progression of computation tasks that occur through each layer is called “forward propagation.”
Each layer works in a specific way. The input and output layers of a deep neural network are called the “visible” layers. At the input layer, the model ingests the data required for processing. At the output layer, a final “result” is created.
Alongside moving data through the layers with forward propagation, deep learning models also use “backpropagation” techniques. Backpropagation uses algorithms (like gradient descent algorithms) to calculate potential errors in predictions. It can then adjust the function’s weights and bias by moving back through the layers to optimize and train the model.
Together, this backward and forward movement enables a neural network to not only complete tasks and make predictions, but also correct errors. For deep learning models to work, they rely on a huge amount of computing power. Usually, companies use high-performance GPUs (Graphic Processing Units), because they have a lot of memory to draw from, and can handle a large number of calculations across multiple cores.
However, companies can also use distributed cloud computing solutions to effectively train and optimize deep learning algorithms.
What is Deep Learning? Types of Deep Learning Models
One thing worth noting when it comes to understanding deep learning models and how they work is that various types of solutions use different forms of neural networks for specific use cases. Each option has its own strengths and weaknesses to consider.
Convolutional Neural Networks (CNNs)
CNNs are neural network solutions in deep learning models used mostly for computer vision and image classification tasks. These networks can detect patterns and features within videos and images, enabling object detection, face recognition, and image recognition tasks.
CNN neural networks are composed of various node layers, much like many neural networks, including an input layer, hidden layers, and output layers. All of the “nodes” in these layers connect with each other and are governed by specific thresholds and weights.
If a node’s output layer is above a specific threshold value, the node is activated and sends data to the next layer of the network. All CNNs have convolutional layers, pooling layers, and fully connected layers. However, in complex situations, CNNs can contain thousands of layers designed to enhance pattern recognition.
Compared to other neural networks, CNNs stand out for their superior performance with complex data, like audio, speech, and image-based inputs. However, these systems are also computationally demanding, and complex to implement.
Recurrent Neural Networks (RNNs)
You may be familiar with Recurrent Neural Networks in the conversational AI landscape. They’re used mostly for natural language and speech recognition applications. RNNs stand out for their unique feedback loops. They’re excellent for using data to make predictions about future outcomes.
For instance, some companies use RNNs within sales forecasting technology, or in contact centers to enhance the performance of AI tools with natural language processing and speech recognition capabilities. RNNs are even used in tools like Google Translate and Apple’s Siri assistant.
RNNs use their “memory” to take information from prior inputs to influence their responses to current inputs. They share parameters across every layer of the neural network and use backpropagation to facilitate ongoing “reinforcement learning.”
One major advantage of RNNs is their ability to use binary data processing and memory in real time, leading to more accurate and contextual responses. Plus, there are various ways to adjust how RNNs work, such as using long short-term memory to enable up-to-date responses. However, RNNs struggle with common problems, particularly linked to “exploding” gradients or “vanishing” gradients that can lead to unstable models.
Autoencoders and Variational Autoencoders
Deep learning transformed data analysis by allowing us to expand beyond simply looking at “numerical” data. With new models, we can now analyze images, speech, and various other data types. Among the first types of models that enabled this were variational autoencoders (or VAEs). These were the first models used widely for things like generating realistic images and speech in generative AI models.
Autoencoders work by compressing unlabeled data into a simplified “representation”, then decoding the data back into its original form. Initially, companies used these tools to reconstruct corrupted information and recreate blurry images. Adding “variational” abilities to autoencoders gave them the ability to not just reconstruct data, but create variations based on initial inputs.
The ability to generate “new” data and outputs, led to an evolution of new technologies in the AI space, such as generative adversarial networks (GANs) and diffusion models. In a way, variational autoencoders set the stage for the generative AI tools we use today. However, they also paved the way for a few new AI problems, such as the rise of deepfakes.
While VAEs are excellent at handling large volumes of data, and creating content, they struggle with similar problems to other types of deep learning models. Training these systems can be resource-intensive and fraught with errors. Sometimes, VAEs also overlook valuable relationships in structured data, which makes them less effective at certain tasks.
Generative Adversarial Networks
Generative Adversarial Networks (or GANS) are neural networks and deep learning solutions used inside and outside artificial intelligence. Their purpose is to create new data resembling the original training data they were given. Think of how generative AI tools like Midjourney can create “new” images based on existing image data.
The term “adversarial” in GAN models refers to the back-and-forth activity between the two key components of a GAN solution: the generator and discriminator. The generator is the tool that creates something, like an image, audio, video, or content. The discriminator is the adversary that compares the created content against the “real” content from the original data set.
Because they’re constantly comparing novel output to genuine data, GANs naturally train themselves, learning how to spot the difference between real input, and generated outputs. The prime benefit of these models for deep learning is that they can create highly realistic content and information. However, once again, training models to the point where they can create content that’s difficult to define as “fake” is extremely complicated and time-consuming.
Diffusion Models
Diffusion models are another key deep learning model used in generative AI solutions like GPT-4o. They’re trained using a forward and reverse process where developers increasing “add noise” to data, then the model attempts to “denoise” the data.
Like GANs, diffusion models can generate data (usually images) with similarities to the data they’re trained on. However, they gradually overwrite the data they’re trained with over time. By adding gaussian noise to training data, they eventually make it unrecognizable. Then they learn a denoising process that allows them to complete tasks (like creating images).
Beyond being able to create highly unique and valuable content, diffusion models have an extra benefit. They don’t rely on adversarial training, which can make it easier to create and optimize these models at speed. However, compared to GANs, diffusion models generally require more computing resources to “fine tune” their performance.
Plus, diffusion models can also be hijacked relatively easily with hidden backdoors, which can lead to serious security issues and ethical problems.
Transformer Models
Finally, we have transformer models in deep learning. These models combine encoder-decoder architectures with text processing mechanisms. On a broad scale, transformer models have completely revolutionized how AI solutions are created and trained. In the large language model landscape, transformer models are the standard for training.
Companies use encoders within transformer models to convert raw text into representations (embeddings). Then, the decoder in the model uses these embeddings, alongside previous outputs from the model, to predict words in a sentence or the next stage in a task.
Over time, the encoder “guesses” at accurate outputs to learn how words and sentences relate to each other, building a deep knowledge of human language. Transformers essentially build on the processes used to create countless other neural networks and deep learning solutions, like RNNs, and eliminate various challenges. For instance, with a transformer, you don’t need to define tasks upfront like you would with an RNN.
Today, transformer models appear not just in generative AI applications, but natural language processing solutions and conversational AI systems. They’re basically making it easier for people to interact naturally with machines at scale.
What is Deep Learning for? The Use Cases
So, what is deep learning used for? Like all forms of AI, the use cases for deep learning are constantly evolving. Just some of the key applications for deep learning models include:
Enhancing Application Development
With deep learning models built into generative AI solutions, developers can create and optimize applications at scale. Thanks to recent breakthroughs in natural language processing and large language models, generative AI solutions are becoming increasingly effective at coding.
With a deep learning system, programmers can simply send basic prompts to a bot telling it what they want the code for an application to do, and the system can do the rest. It can suggest code snippets, code improvements, or design full functions. Some solutions can even translate code from one language into another, like translating COBOL into Java.
Transforming Computer Vision
Computer vision is a type of artificial intelligence solution that many AI businesses focus on. It uses image classification, object detection, semantic segmentation and deep learning to surface meaningful information from images, videos, and other visual content.
Computer vision systems can analyze thousands of pieces of visual data per minute, enabling everything from the rapid identification of defects in products, to improved security mechanisms. Energy and utilities companies, manufacturing and automotive brands, and even law enforcement agencies use computer vision for various tasks.
It can enable predictive maintenance mechanisms, help companies authenticate individuals when they visit a location, and even assist with identifying risks in an environment from a video feed. Computer vision is even making strides in the healthcare industry, helping doctors and surgeons identify potential health issues, like tumors, at speed.
Upgrading Customer Care
On a broad scale, various types of artificial intelligence are helping businesses enhance their approach to customer care. Deep learning solutions empower companies to take their customer service strategies to the next level. Models can learn from customer activities and behaviors to help businesses understand how to personalize service delivery and improve products for customers.
Generative AI models with deep learning capabilities have even transformed the type of “chatbots” and virtual agents that support self-service tasks. These tools can learn from every interaction, provide contextual guidance based on conversation history, transcripts, and sentiment analysis, and even deliver more personalized shopping experiences.
For instance, a generative AI bot can draw on everything its learned about your industry, the market, and a specific customer to suggest relevant products to customers based on their needs, follow up automatically about issues, and deliver proactive support.
Enabling Advanced Language Processing
As mentioned above, one of the top benefits of deep learning is that it allows machines to process and understand natural language more effectively. Without this technology, we wouldn’t have the NLP solutions we rely on today for things like smart assistants.
Deep learning solutions empower AI models to recognize and understand human language in depth. With cutting-edge technology, AI models can classify and identify all kinds of language, from spoken words to text and even code or statistics.
Deep learning solutions power the speech recognition systems that enhance IVR processes in the contact center and allow virtual assistants to respond to customers when they ask questions naturally. Thanks to RNNs and other neural network solutions, these systems can learn as they work, discovering more valuable information about users over time.
Enhancing Automation
Enabling automation and enhancing “digital labor” is a common use case for many forms of artificial intelligence. Deep learning solutions allow companies to automate more tasks than ever before. They even allow for the creation of generative AI solutions and other systems that can collaborate with humans to increase productivity, like Microsoft Copilot.
Today’s AI technologies, powered by neural networks and deep learning, can complete all kinds of tasks, from creating content, to organizing data, and forecasting future events. In the financial services sector, companies use these models to drive algorithmic trading, assess business risks, streamline loan approvals, and detect fraud.
In the legal landscape, deep learning solutions can help firms automate processes like researching case law or identifying potential evidence of criminal activity. Elsewhere, in the manufacturing sector, these solutions can empower machines to assess their own performance, looking for potential errors that could lead to malfunctions. This paves the way for comprehensive predictive maintenance.
The Future of Deep Learning in AI
Deep learning is one of the most crucial components of AI in the modern world. It’s the key technology that has helped us enter a new age of artificial intelligence, where machines are more efficient, autonomous, and productive than ever before.
In fact, McKinsey conducted a study and found more than 400 potential use cases for deep learning applications across 19 industries and multiple business functions.
Like any AI solution, deep learning has challenges to overcome. Numerous hurdles remain regarding complex training processes and the demand for high computational resources. Plus, we still need to find ways of ensuring deep learning solutions adhere to the security, privacy, and ethical standards governments are implementing today.
However, by allowing machines to mimic the cognitive processes of human beings, deep learning algorithms have revolutionized the technology world. Going forward, this AI tech promises to reshape our future with an era of machines that can learn, adapt, and solve complex problems faster and more efficiently than ever before.
