What is deep learning? Everything you need to know

Because deep learning networks process information in a way that resembles the human brain, they can be used to help people with various tasks.

Deep learning technology is an essential component of many AI applications that organizations and individuals use. The deep learning method lies behind many everyday products and services that most people encounter every day. Here are a few examples:

• Speech recognition used on mobile phones and smart speakers
• Voice-activated virtual assistants (like Siri, Google and Alexa)
• Facial recognition on your devices (e.g., Face ID or in your photos)
• Fraud detection on your bank uses

Deep learning applications also include exciting inventions like virtual reality, self-driving cars, and robotics. Deep learning systems allow autonomous vehicles to navigate their surroundings and train robots to adapt to various environments.

With more data scientists running increasingly complex artificial intelligence projects and utilizing deep learning frameworks, it’s expected to become an even bigger part of our everyday lives.

Most deep learning methods use a neural network architecture. That’s why deep learning models are called deep neural networks.

They are called “deep neural networks” because they have many layers (up to 150). That’s significantly more than traditional networks, which typically only have two or three hidden layers.

Deep learning is a process of building and training artificial neural networks — or algorithms modeled to work like the human brain.

To train deep learning models, you need a large amount of data. Only by working with vast amounts of training data can the artificial neural network achieve insightful answers and learn from its mistakes.

A deep learning model consists of several layers of artificial neurons, such as:

• Input layer. The first layer of a deep learning model, the input layer is where the raw data feeds into the neural network. This layer represents the features of the data that the model will process. For example, in speech recognition, the input layer receives audio data as input.
• Hidden layers. Hidden layers in deep learning neural networks play an important role in transforming the input data into meaningful information. Each hidden layer consists of multiple artificial neurons that process the data and perform computations before passing it on to the next layer. Deep learning models typically have hundreds of hidden layers, allowing them to analyze the data from many different angles based on what the deep learning algorithm is trying to achieve (e.g., classify an image or recognize a pattern).
• Output layer. The final layer of a deep learning model, the output layer produces the desired output based on the processed information. The number of neurons in the output layer will depend on the task.

These interconnected layers process the input data using deep learning algorithms to gradually learn and extract relevant features (or meaningful patterns). Examples of deep learning algorithms include:

• Convolutional neural networks (CNNs)
• Recurrent neural networks (RNNs)
• Deep reinforcement learning

Each of these algorithms train deep learning models to learn from examples, and some algorithms are better suited for specific tasks.

Feature extraction is the process of automatically finding the most important and relevant aspects of the data. It could be likened to how our brains focus on certain details when recognizing objects or understanding information. Feature extraction helps the model focus on the most critical elements of the data.

Once trained, you get a deep learning model that is able to process and analyze new information without human intervention. At this stage, deep learning models are optimized to minimize errors and rigorously tested.

When the model is evaluated, it can be used for predictive analytics based on new, unseen information. The model takes in new input data, and the neural networks use their deep learning algorithms to accurately process and produce insights from new, unseen data.

Deep learning offers many benefits, not only in general but also over machine learning. Let’s look at some of the main advantages of deep learning.

Exceptionally high accuracy

Deep learning models can achieve exceptional accuracy in tasks like image recognition and classification, natural language processing, and speech recognition. Because deep neural networks can learn complex patterns and features directly from raw data, they can automatically extract meaningful, accurate predictions and insights.

Works with unstructured data

The neural networks in deep learning can process and understand unstructured data (e.g., text documents, images). Traditional machine learning methods have found working with unstructured data challenging because this type of data can have countless variations.

Unsupervised learning

Deep learning models can adapt to user behavior over time, improving without extensive labeled data or supervised learning. For example, a neural network analyzing typing patterns autocorrects English words, but if a user frequently types non-English words (like “gracias”), the model will learn that it’s not a mistake that needs to be corrected.

Many possible applications

Deep learning is highly versatile and can be applied in many different ways. Many industries — from aerospace to medical research — can make use of the technology to advance their systems and improve services.

Deep learning is a relatively new technology, but it is already used by businesses, non-profit organizations, and educational institutions in many ways. Here are some of the real-life uses of deep learning.

Object detection and recognition

Deep learning is used as a means of quickly and accurately predicting the location of an object in an image. To detect objects, computers use deep learning methods to understand and interpret visual information (also known as computer vision). Here are some examples of how deep learning is used for object detection:

• Self-driving cars. Autonomous cars rely on computer vision to identify and track vehicles, traffic signs, pedestrians, and other objects in real-time. Using deep learning, the vehicle can make informed decisions and navigate safely with minimal human intervention.
• Facial recognition. You may have come across instances where your phone classifies photos of the same people, or a social media platform knows whom you should tag in a photo. These are examples of deep learning used for facial recognition. The deep network recognizes people based on previously tagged photos and can make relatively accurate predictions.
• Surveillance systems. Deep learning models are also used by law enforcement agencies (e.g., surveillance systems). Using the models, the agents can track objects of interest in real time, such as people or vehicles. The technology allows them to automatically monitor activities and identify potential threats or unusual behavior.

Customer service and experience

Businesses use deep learning to improve their customer service by making it more streamlined, efficient, and automated. Here are some examples of how businesses can integrate deep learning into customer services:

• Chatbots. When shopping online, you’ll often see the option to start a conversation with a chatbot. Deep learning is used to train and develop intelligent bots that can engage in natural language conversations with customers. Most chatbots still have a long way to go — they’re limited in their ability to help customers. That said, speaking to a chatbot could potentially solve your problem now rather than you having to wait to speak to a real person.
• Personalizations and recommendations. Deep learning algorithms are used to create personalized customer experiences by analyzing user behavior, preferences, and historical data. They can then build recommendation systems that suggest relevant services, products or content.

Medical research and healthcare

Deep learning has several important applications in healthcare. Some of which include:

• Medical diagnosis. Deep learning models can be trained on large datasets of medical images (e.g., X-rays or MRIs) to accurately detect and classify diseases. Using medical image analysis, they can even learn to automatically detect cancer cells.
• Drug discovery and development. The models can analyze vast amounts of biological and chemical data to help discover new effective drugs for various diseases. They can predict molecular interactions, identify potential drugs, and speed up the process of drug development.
• Health monitoring and wearables. Deep learning algorithms can process data from wearable devices (e.g., fitness trackers or biosensors) to monitor vital signs, detect anomalies, and provide real-time feedback. This deep learning application allows for continuous monitoring of patient health and early detection of potential health issues.

Industrial and machine automation

Deep learning techniques play a significant role in industrial and machine automation. Using the models, these industries can make their systems more efficient and intelligent. Let’s look at some examples:

• Quality control. Deep learning models are trained to analyze images to detect defects or quality issues in manufacturing processes. The use of deep learning algorithms allows for real-time monitoring and automated quality control, reducing the need for manual inspection and improving the overall quality of goods.
• Safety features and systems. Deep learning is also used to ensure the tools and machines remain safe for employees. By analyzing sensor data from safety equipment (e.g., fire alarms, gas detectors, or security cameras), they can monitor patterns and detect anomalies. This process allows them to predict maintenance needs and ensure the systems remain safe and function properly.

Natural language processing and text and image generation

Potentially one of the most recent uses of deep learning is natural language processing (NLP) and image generation. In recent years, deep learning has significantly advanced NLP tasks by generating more accurate and robust models for language understanding and generation. Some real-life examples of this include:

• ChatGPT. In the last year, ChatGPT, a language generation tool owned and developed by 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. You can use it to improve and edit your writing.
• 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 app is a subscription-based photo-editing app powered by AI. You can use it to change the style of your selfie completely or make specific adjustments, such as removing unwanted objects. It also offers advanced AI-powered text-to-image generator.

The main issue with deep learning is that deep learning algorithms require vast amounts of training data. Accessing large amounts of data can be challenging, and using limited data won’t allow these models to learn in a way that could be representative.

Deep learning also has several other challenges, such as:

• Biases. If the training data is biased, deep learning models will reproduce those biases in their predictions. This has been a challenging issue for deep learning programmers as models learn from subtle data differences. For example, if a facial recognition model is trained predominantly using images of a particular race, it won’t be able to recognize faces from other races.
• Lack of transparency. Neural networks process and learn from millions of data points to find patterns, so it can be challenging to understand how a neural network generates the solution. This lack of transparency is an issue with deep learning and machine learning in general. Without complete transparency, it can be difficult to trust the predictions or decisions the model makes.
• Lack of flexibility. Deep learning is still a new field of study, so it can only solve specific types of problems. With time, deep learning will evolve, but right now it’s still relatively rigid and incapable of multitasking.
• Lack of trust by people. Many customers still don’t trust many deep learning applications. They’re often not as helpful as real people, and talking to chatbots can be frustrating. Many customers still prefer to speak to a real person when in need of a hand rather than an automated chatbot or virtual assistant. With time, these attitudes may change — especially if the technology improves the quality of help customers receive.

While both machine learning and deep learning are types of AI, deep learning differs from traditional machine learning in several ways. Here are some of their main differences:

• Training data required. Machine learning algorithms can train using small data sets, while deep learning requires vast amounts of training data.
• Accuracy. While machine learning techniques allow for high accuracy, deep learning outperforms ML and can produce more accurate results.
• Training duration. Machine learning models can be trained relatively quickly. However, deep learning model training takes time due to the large amount of data.
• Human intervention. Machine learning requires more human intervention (closer to augmented intelligence than true AI), while deep learning models learn on their own from past mistakes.