What is Deep Learning (DL)? Explain Like I’m Five

by Chris Von Wilpert, BBusMan • Last updated November 23, 2023

Expert Verified by Leandro Langeani, BBA

What is deep learning?

Imagine if your computer suddenly became a super-genius that could learn to recognize cats in photos, understand spoken words like a polyglot, and even drive cars better than any human — all by itself! That's deep learning for you. It's the magical potion of artificial intelligence that lets computers mimic our brain cells and become crazy smart at solving specific problems. With layers upon layers of interconnected nodes (think: tiny digital neurons), these machines dive into oceans of data, swim through complex calculations, and emerge with newfound knowledge even humans can’t think of. So next time you're chatting with Siri or unlocking your phone with a smile, remember it's all thanks to deep learning.

Deep learning fast facts

  • Deep learning uses layered neural networks for complex tasks like image recognition and language processing.

  • It mimics the human brain's structure with interconnected nodes, but is simpler and less versatile.

  • Some applications include healthcare diagnostics, financial fraud detection, and enhancing digital assistants like Siri.

  • Challenges for deep learning include needing large data sets, high computational power, and its "black box" nature making decision processes hard to interpret.

  • Deep learning careers are high-paying, demanding skills in mathematics, statistics, computer science, and programming.

What is deep learning basics?

Deep learning, a subset of machine learning, has gained significant traction in recent years due to its powerful algorithms and ability to process vast amounts of data. At the core of deep learning are neural networks, which consist of interconnected nodes or artificial neurons organized into layers: input layer, hidden layers, and output layer. These structures mimic biological neurons in human intelligence and can perform complex mathematical calculations with minimal human intervention.

One prominent application for deep learning is natural language processing (NLP), enabling digital assistants like Siri or Alexa to understand user commands through speech recognition. Additionally, image recognition plays a crucial role in technologies such as facial recognition systems and self-driving cars. Convolutional Neural Networks (CNNs) have been particularly successful at these tasks since their introduction by Yan LeCun back in 1998 during his "Gradient-based Learning Applied to Document Recognition" research paper.

A schematic of a Deep Neural Network showing multiple interconnected layers from input to output, illustrating the complexity of neural architectures in deep learning. Photograph: Ravindra Parmar via Towards Data Science.

Does ChatGPT use deep learning?

ChatGPT, a product of OpenAI, is indeed powered by deep learning techniques. At its core lies an advanced neural network model that processes and generates human-like responses in natural language. These models have evolved significantly over the years, thanks to pioneers like Yan LeCun who contributed immensely to the development of deep learning applications such as speech recognition and digital assistants.

Deep Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are some examples of popular deep learning architectures used today for tasks like image recognition or language translation. ChatGPT leverages similar technologies but focuses on understanding context from user inputs while generating coherent replies. It uses interconnected nodes arranged in layers — input layer, hidden layers, and output layer — which mimic biological neurons' functioning to process information efficiently.

The success behind ChatGPT's conversational capabilities can be attributed not only to advancements in artificial intelligence but also the availability of computational resources required for training these complex models. As more research continues within this domain at conferences like Neural Information Processing Systems (NIPS), we can expect even better performance from AI-driven chatbots across various industries including customer service, healthcare assistance, or simply engaging users with interactive conversations like ChatGPT.

What is the difference between deep learning and machine learning?

Deep learning and machine learning are interconnected fields within the broader domain of artificial intelligence, but they differ in their approaches to solving problems. Machine learning is a subset of AI that involves teaching computers to learn from data and make predictions or decisions without explicit programming. It encompasses various algorithms like decision trees, support vector machines, and clustering techniques that enable systems to identify patterns or relationships within datasets.

On the other hand, deep learning is a specialized branch of machine learning inspired by biological neurons' functioning in human brains. It employs artificial neural networks with multiple layers (input layer, hidden layers, output layer) called Deep Neural Networks (DNNs). These networks can automatically extract features from raw data through complex mathematical calculations during training processes. Examples include Convolutional Neural Networks (CNNs) for image recognition tasks or Recurrent Neural Networks (RNNs) for natural language processing applications.

The primary difference between these two lies in their complexity and problem-solving capabilities. While traditional machine-learning algorithms often require manual feature engineering and may struggle with large-scale unstructured data such as images or text documents, deep-learning models excel at handling massive amounts of high-dimensional information autonomously without much human intervention.

A comparative visual between machine learning and deep learning, highlighting the autonomy of deep learning in pattern recognition and decision-making processes with minimal human input. Photograph: Patrick Grieve via Zendesk.

Is deep learning the same as neural networks?

Deep learning and neural networks are closely related but not exactly the same. Neural networks form the foundation upon which deep learning is built, while deep learning represents a specific type of neural network architecture.

Neural networks are computational models inspired by biological neurons in human brains. They consist of interconnected nodes or artificial neurons organized into layers: input layer, hidden layers, and output layer. These nodes process information through mathematical calculations to learn patterns within data and make predictions or decisions accordingly.

Deep learning refers to a subset of machine-learning techniques that use large-scale artificial neural networks with multiple hidden layers — also known as Deep Neural Networks (DNNs). The term "deep" signifies the depth created by stacking numerous hidden layers in these architectures. By employing such complex structures, deep-learning algorithms can automatically extract high-level features from raw data without much manual feature engineering required for traditional machine-learning methods.

Although both terms are often used interchangeably due to their close association, it's important to note that neural networks serve as an underlying concept enabling various AI applications including deep learning systems.

What is an example of deep learning?

One striking example of deep learning in action is facial recognition technology. Think about unlocking your phone with just a glance. This feature is achieved through Convolutional Neural Networks (CNNs), a type of neural network model adept at processing visual data. These networks analyze facial features by breaking down images into pixels and identifying unique patterns. It's a complex process involving layers of nodes and mathematical calculations, but the result is a system that can match a face to an identity with astonishing accuracy. This technology isn't just for smartphones — it's also used in security systems and even in identifying suspects for law enforcement.

Another area where deep learning shines is in language translation services. Remember the last time you used an app to translate a sign or menu while traveling? That's deep learning at work, specifically through Recurrent Neural Networks (RNNs). These networks are exceptional at handling sequential data, like sentences in a language. They learn from vast amounts of text and can translate between languages with increasing fluency. In fact, improvements in this area have been so significant that the gap between human and machine translation quality is constantly narrowing... 

There's also the world of self-driving cars, a field where deep learning is making waves — especially within electric car systems like Tesla's. These cars use a combination of sensors and deep learning models to navigate roads, recognize traffic signals, and make decisions in real-time. Neural networks process data from cameras and sensors, enabling the vehicle to "see" its surroundings. This technology isn't just a futuristic dream — it's already being tested and used on roads today.

What problems can deep learning solve?

Deep learning solves problems through its unique technical architecture, which is inspired by the structure and function of the human brain. At its core are neural networks, consisting of interconnected nodes (analogous to biological neurons) organized into layers: an input layer, multiple hidden layers, and an output layer. Each layer's nodes transform incoming data, learning to recognize patterns and features. Deep learning models, especially deep neural networks, excel in handling vast amounts of data, learning from it without explicit programming.

The strength of deep learning lies in its ability to perform complex mathematical calculations and adjust the weights of connections between nodes during training. This process, known as gradient-based learning, optimizes the network to make accurate predictions or decisions. For instance, in image recognition, Convolutional Neural Networks (CNNs) analyze pixels and learn to identify shapes and objects. 

In natural language processing, Recurrent Neural Networks (RNNs) and language models handle sequential data, learning the nuances of human language. The deep learning model's ability to learn and improve over time with minimal human intervention is what makes it exceptionally powerful in solving a wide range of complex problems.

Over the years, deep learning has solved many unique and challenging problems. In astrophysics, deep learning algorithms have been used to sift through massive datasets, identifying new celestial objects and phenomena. Even in fields like agriculture, deep learning has been used to revolutionize farms and businesses by improving crop analysis and predictions for food production. The examples for its uses are endless, as more and more companies in various fields are developing applications and software to optimize their processes using deep learning algorithms.

What is deep learning used for today?

Deep learning today is used in a variety of groundbreaking and practical applications, profoundly impacting various industries and daily life. For example, deep learning is revolutionizing the healthcare industry in remarkable ways. For instance, it's significantly advancing medical diagnostics. By using deep learning algorithms, particularly Convolutional Neural Networks, healthcare professionals can analyze medical images with greater precision and speed. This technology is proving invaluable in detecting early signs of diseases like cancer, where early diagnosis can dramatically improve the odds of quick and successful recovery.

In finance, deep learning demonstrates its versatility and power. It's being used for complex tasks like fraud detection by analyzing transaction patterns to identify anomalies that could indicate fraudulent activities. Moreover, in algorithmic trading, deep learning models process vast amounts of financial data to predict stock market trends, helping traders make more informed decisions. This technology's ability to sift through and make sense of massive datasets is transforming how the financial sector and stock exchanges operate.

Deep learning's impact is also profoundly felt in the field of speech recognition. Today's voice-activated assistants, such as Siri, Google Assistant, and Alexa, are far more accurate and versatile thanks to deep learning. These systems can now understand different dialects, accents, and even context, making interactions with them more natural and effective. Beyond everyday use, deep learning is helping with many unique applications, like helping people with speech impairments communicate more effectively, guiding hikers in remote areas, and even in wildlife conservation — where it's used to analyze animal calls for monitoring and study.

A networked illustration of a human brain, representing the intricate connections and the concept of neural networks in deep learning. Photograph: Getty Images.

Does deep learning work like the brain?

Deep learning is inspired by the workings of the human brain, particularly in its structure and learning processes, but it is not an exact replica. The basic unit of deep learning is the artificial neural network, which resembles biological neurons and neural networks in the brain. These artificial networks consist of interconnected nodes (analogous to neurons) organized in layers, including an input layer, multiple hidden layers, and an output layer.

In both deep learning systems and the human brain, learning involves adjusting to new information. In deep learning, this is achieved through a process called backpropagation, where the network adjusts the weights of connections between nodes based on the error in its output. This is somewhat akin to how synaptic connections in the brain strengthen or weaken during learning. However, the simplicity of artificial neurons and their connections in deep learning models is a stark contrast to the complexity and diversity of biological neurons and synapses in the human brain.

Is deep learning overhyped?

The question of whether deep learning is overhyped depends on the perspective and context. On one hand, deep learning has made remarkable advancements and has a wide range of successful applications, as it's been shown in fields like image and speech recognition, natural language processing, and autonomous vehicles.

However, there are limitations and challenges. Deep learning models require large amounts of data and significant computational resources, which can be a barrier in some fields. There's also the issue of interpretability — deep learning models, especially complex ones, can act as "black boxes," making it difficult to understand how they reach specific conclusions. This lack of transparency can be a critical drawback, especially in areas like healthcare or criminal justice where understanding the decision-making process is crucial.

Deep learning isn't a one-size-fits-all solution and can be less effective for tasks where data is scarce or where the problems don't lend themselves well to the strengths of neural networks. Additionally, the enthusiasm surrounding deep learning sometimes leads to unrealistic expectations about its capabilities, especially regarding its current ability to replicate human-like understanding or general intelligence.

Is deep learning high-paying?

Deep learning careers are generally high-paying, reflecting the specialized skills and advanced knowledge required. This field demands proficiency in mathematics, statistics, computer science, and expertise in programming languages and frameworks like Python, TensorFlow, or PyTorch. The complex nature of building, training, and implementing deep learning models means that professionals with these skills are in high demand and short supply, which naturally drives up salaries.

The demand for deep learning expertise is growing as more industries embrace AI. From technology and finance to healthcare and automotive sectors, deep learning is becoming integral for various applications. This increasing demand, coupled with the significant impact these professionals can have on a company's efficiency and revenue generation, makes them highly valued in the job market.


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