What is deep learning? Everything you need to know

What is deep learning? 

Deep learning is a form of machine learning that uses artificial neural networks for more advanced processing capability. These neural networks are inspired by the structure of the human brain, which gives deep learning models advanced processing and decision-making capabilities. 

Deep learning models can evaluate and process data sets with minimal human intervention. Many deep learning models are also able to improve their own outputs over time. 

The terms “machine learning” and “deep learning” are often referenced when discussing new AI technology, and deep learning is an advanced form of machine learning. The key difference between machine learning and deep learning is that deep learning uses more layers of computing power. Unlike traditional machine learning models, which often rely on manually crafted features and simpler algorithms, deep learning models use many layers of artificial neurons to automatically learn complex patterns in data.

How does deep learning work? 

In a deep learning model, data passes through several layers of the artificial neural network before producing an output. Some advanced deep learning AI tools have hundreds of layers through which data needs to pass. 

These layers evaluate the data by extracting features from it so that the deep learning model can categorize information, recognize patterns, and make predictions. After the data moves through each layer, the deep learning model will generate an output based on the initial query. 

Types of deep learning

Some of the most widely used deep learning approaches include convolutional neural networks, recurrent neural networks, and deep reinforcement learning. These algorithms are used for different computational tasks, although they all rely on artificial neural networks for advanced processing. 

Convolutional neural networks (CNNs)

Convolutional neural networks (CNNs) are used for processing visual data, such as images and videos. They are often used for image classification and facial recognition because they can process visual data even when it’s unclear or distorted. 

CNNs have a wide range of potential applications, notably in healthcare and transportation. Using CNNs in medical imaging could help doctors make accurate diagnoses, while CNNs in vehicles can provide collision warnings to prevent accidents. 

Deep reinforcement learning

Deep reinforcement learning combines neural networks with reinforcement learning principles, enabling AI systems to learn by interacting with an environment and receiving feedback in the form of rewards. These algorithms are often used when building robotics or developing video games with AI elements. 

Recurrent neural networks (RNNs)

Recurrent neural networks (RNNs) are used for evaluating and generating language. RNNs are used for natural language processing models in generative AI tools, which can generate and translate text. RNN deep learning frameworks are also very effective at speech processing. 

Deep learning use case examples

Deep learning powers a wide range of exciting applications in modern technology. Let’s take a look at some of the ways that companies are already using deep learning to make operations more efficient. 

Object detection and recognition

• Self-driving cars: Autonomous vehicles use CNN deep learning models to process visual indicators like lane lines, traffic signs, and other cars in real time. These models use this information to avoid collisions and make safe driving decisions. 
• Facial recognition: Deep learning is used to identify people in photos and videos. Many social media platforms use this technology to automatically tag users in posts. 
• Surveillance systems: Law enforcement officers and security teams use deep learning to track people or objects in surveillance footage. This step can help police respond faster to crimes and identify suspects. 

Customer service

• Chatbots: AI-powered chatbots use deep learning to assess and respond to customer concerns. While their capabilities are still fairly limited, they can answer frequently asked questions and direct inquiries to the right team members. 
• Personalized recommendations: Deep learning tools can analyze a user’s online behavior to provide personalized product or content recommendations.

Healthcare and medical research

• Medical diagnosis: Deep learning models can be trained to process medical images and support doctors when making medical diagnoses. 
• Drug discovery and development: Pharmaceutical companies now use deep learning models to assess biological and chemical data. With this information, AI tools can identify patterns that indicate opportunities for drug development. 
• Health monitoring and wearables: Deep learning models can process biometric data from fitness trackers and other wearable devices. This technology is particularly helpful for the early detection of serious medical conditions. For example, the AI might identify abnormal cardiovascular activity, which encourages the patient to get a screening and diagnosis. 

Industrial operations and machine automation

• Quality control: Deep learning algorithms analyze visual input from industrial machines to identify product defects in real time. This step helps minimize losses by preventing defective products from making it to market. 
• Safety features and systems: Deep learning algorithms can analyze data from safety equipment and detect anomalies. This technology helps teams respond to emergencies faster to prevent injuries and damage to your facilities. 

Natural language processing and text and image generation

• ChatGPT: ChatGPT, a language generation tool owned and developed by the research laboratory OpenAI, became a global phenomenon. This tool uses language models like GPT (generative pre-trained transformer) to create text, maintain a dialogue, and create stories. The tool can be used in many different ways, with companies already integrating it into their everyday systems.
• Bing image creator: Bing is an online platform that uses neural networks to apply artistic styles to images and create AI-generated artwork. Bing, like several other AI image generators, produces images from natural language descriptions — you type a prompt, and Bing creates an image for you.
• Quillbot: QuillBot is a safe tool for paraphrasing text while retaining its original meaning. Although it benefits from artificial intelligence, its core technology does not rely as heavily on deep learning as generative tools like ChatGPT. Instead, it focuses on more targeted natural language processing techniques to assist with rewriting and improving text.
• Midjourney: Midjourney is a generative AI tool that uses artificial intelligence to create images based on text prompts you type. Find out if Midjourney is safe here.
• Lensa AI: Lensa AI is a safe app for editing photos. You can use it to change the style of your selfie completely or make specific adjustments, such as removing unwanted objects. The app uses AI tools, including some that rely on deep learning methods, to enhance images and generate new visuals, such as through its advanced AI-powered text-to-image generator.
• DeepSeek AI: A direct competitor of ChatGPT, DeepSeek AI entered the industry in 2025 and immediately started making waves. The Chinese AI chatbot is a strong (and free) alternative to other generative AI models, known for its efficiency and robust processing capabilities. Generally, DeepSeek is a safe tool, designed with user security in mind. The tool has already overtaken its competitors, jumping to the top of the US Apple App Store's downloads, causing a shift (and potential turning point) in the AI market.

What are the benefits of deep learning? 

Deep learning offers powerful benefits for businesses and their teams. These algorithms have advanced capabilities and can take on repetitive tasks that were previously handled by humans, which helps teams operate more efficiently. 

• Unstructured data processing: Deep learning algorithms are powerful enough to process unstructured data like images and text documents. This feature makes it more convenient to use AI technology. 
• High accuracy: Compared to traditional machine learning, deep learning often delivers higher accuracy results for natural language processing, speech recognition, and image recognition, leading to fewer output errors than traditional machine learning. 
• Versatile technology: Deep learning has potential applications in many industries, from healthcare to manufacturing to defense. 
• Scalability: Since deep learning AI models can process large volumes of data at once, they scale seamlessly as business operations grow. 
• Repetitive tasks automation: Advanced deep learning algorithms can complete a variety of repetitive tasks, such as data processing or simple text generation. This feature gives employees added time to focus on more complex projects. 

What are the challenges of deep learning? 

While deep learning has many benefits, especially in terms of efficiency and productivity, it also has some challenges that organizations should be aware of. 

• Requires large data sets: Deep learning models must be trained on extremely large volumes of data in order to learn, which not all organizations have access to. 
• Inherent biases: AI tools that use deep learning algorithms can reflect or even amplify real-world biases present in the training data.
• Lack of transparency: Because deep learning models use such a large volume of data, it is difficult to understand exactly how the neural networks process this data and develop their outputs. 
• Lack of consumer trust: Many consumers are still hesitant to trust deep learning models, especially when it's unclear how their data is used or how decisions are made. This lack of transparency fuels concerns about privacy and fairness.
• Requires extensive computational resources: To run deep learning models, organizations need access to extremely powerful hardware. 

Deep learning vs. machine learning: The main differences

The terms “deep learning” and “machine learning” are sometimes used interchangeably, but they have different meanings. Technically, deep learning is a subset of machine learning. Here’s a breakdown of the differences between the two terms. 

While both deep learning and machine learning have their place in the AI landscape, deep learning is particularly well-suited for solving complex problems that require high accuracy and adaptability. As data availability and computational power continue to grow, deep learning will play an even greater role in shaping the future of artificial intelligence.