What Is Deep Learning? Definition and Techniques [With Examples]

Deep learning
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You’ve heard of deep learning but unsure how it works or what this technology is used for?

In this article, we’ll explain all-things deep learning including what it is, the techniques used, as well as examples of its application. 

Let’s start!

What is Deep Learning?

Deep learning is a subset of machine learning and one of artificial intelligence’s advanced technologies. Its task is to mimic the human learning process – to learn by example. 

For data scientists, deep learning is indispensable because it facilitates and accelerates the collecting, analyzing, and processing of large amounts of data. 

Although this advanced technology sounds like it belongs to the future, it is present in everyday use; for example, it allows self-driving cars to recognize traffic signs and adjust their driving style or to distinguish pedestrians from trees on the side of the road.

To understand how deep learning acquires new knowledge, let’s take a simple example: imagine a toddler learning about the world and unfamiliar objects around him. 

The toddler points to things and names them, and parents confirm or deny his attempts. Let’s say a child names a mug – the parent will answer, “yes, it is” or “no, it’s not a mug”. 

Every time he tries to name an object, whether right or wrong, the toddler becomes more aware of its features, based on which he can distinguish them from other things. In this way, the child builds a complex abstract image of a concept, building hierarchy levels with each layer of acquired knowledge. 

Deep Learning vs. Machine Learning vs. Neural Networks: What is the Difference?

The terms machine learning, deep learning, and neural networks are sometimes used interchangeably, and while they are related   they are not the same. 

Video source: YouTube/AppliedAI

Let’s see the main differences between these three technologies:

  1. Machine learning is a subset of artificial intelligence that focuses on using data and algorithms to imitate the ways humans learn to build machines that “learn” and make predictions with minimal human interventions. It enables computers to operate autonomously and can independently learn and grow. 
  1. Deep learning is a subset of machine learning. It makes complex correlations between data and learns from examples and previous mistakes. Deep learning requires larger amounts of data compared to machine learning. 

While the training takes longer, the accuracy is higher than in machine learning. Deep learning can also learn on its own requiring less human interaction than machine learning.but datasets don’t have to be labeled – algorithms can learn from “raw” unstructured data.

  1. Neural networks is a method in artificial intelligence that teaches computers to process data in a way a human brain would. They consist of layers of neurons or nodes,which are interconnected and can receive data up to a particular value, after which they pass it on to the next node for processing.

The term “deep” in deep learning refers to the number of nodes in neural networks. If there are more than three of them, in addition to input and output, we are talking about a deep learning algorithm: a deep neural network. If a neural network consists of only three nodes, that is a basic neural network. .

How Does Deep Learning work?

Deep learning uses algorithms that attempt to make similar conclusions as humans would by analyzing data with a logical structure. To achieve this, deep learning uses neural networks, multi-layered structures of algorithms which can identify patterns and classify information, just as humans do.

When our brains receive new information, it tries to compare it with what we know. The same principle is applied in deep learning using neural networks. 

All deep learning programs require is access to large amounts of unstructured and unlabelled data and the programs will autonomously learn and create complex, accurate predictive models. 

For example,  a deep learning model is givena set of images containing mugs. Its algorithms will learn from the pixels contained in the images it received access to, classify groups of pixels, and create a set of features, i.e., a predictive model that recognizes all the pictures, including cups. With each iteration, the program acquires more knowledge and accuracy.

While in machine learning, an expert would have to spend significant time engineering a machine learning system that is capable of detecting images of mugs, in deep learning, all that is needed is to supply a large number of images depicting mugs and the system will learn on its own.

It is important to note that computer programs that use deep learning are high-speed: they create sets of features and sort through millions of data in just a few minutes.

What Are The Deep Learning Techniques/Algorithms?

Thanks to its advanced algorithms, deep learning can analyze and sort all datasets. These are the top ten deep learning algorithms:

1. Classic Neural Networks

Classic neural networks, also called Fully Connected Neural Networks, are used image processing and detecting objects Classic Neural Networks consist of  multilayer perceptronsn which neurons are connected to a continuous network. They include the adaptation of the model into primary binary data through three functions, namely:

  • Linear function: has a single line that multiplies its inputs by a constant multiplier.
  • Non-linear function: It is further divided into three subsets:
  1. Sigmoid curve: An S-shaped curve with a range of 0 to 1.
  2. Hyperbolic Tangent: An S-shaped curve that ranges from -1 to 1.
  3. Rectified Linear Unit (ReLU): A single point function returns 0 when the input value is less than the set value and returns a linear multiple if the given input is greater than the set value.

2. Convolutional Neural Networks

Convolutional Neural Network or CNN is a classical artificial neural network model with a high potential for solving complex tasks and analyzing image and non-image data. It is based on the same principle as the arranged neurons in the cortex of the animal brain.

CNNs are divided into four layers:

  1. One input layer contains a two-dimensional arrangement of neurons for image content analysis.
  2. One-dimensional neuron output layer, while images are processed at the input nodes via convolutional layers.
  3. The third layer consists of hidden nodes and is known as the sampling layer, which determines the number of neurons involved in identifying the data content.
  4. Thus, CNNs can have one or more hidden layers between the input and output, connecting the sampling to the output layers.

3. Recurrent Neural Networks

RNNs are used to predict sequences, and in that process, they use knowledge from the previous state as input for future predictions. RNs come in two forms:

  • LSTM: Predicts data in time sequences using acquired memory. It has three gates: entrance, exit, and oblivion.
  • Gated RNNs: This form also predicts time sequence data through memory but has two gates – update and reset.

4. Restricted Boltzmann Machines

This algorithm does not have a predefined direction, so its nodes are connected in a circular arrangement. Due to its unique form, it is used to produce model parameters.

The Boltzmann machine model is stochastic because it has a random probability distribution or pattern that can be analyzed statistically but cannot be predicted precisely.

5. Transfer Learning

Transfer learning is the process of refining a model that has already been learned to carry out new and more precise tasks. It can only be successful if the model’s features from the initial study are generic.

One of the most popular deep learning approaches is transfer learning, which has a lower data requirement than others and hence requires less time for data processing.

6. Generative Adversarial Networks

GAN consist of two components: a  generator and a discriminator. The generator learns how to generates false data, while the discriminator heps distinguish true from false data. 

For instance, the generator would generate simulated data for actual photos and develop a deconvolution neural network in response to a request to create an image library.

A network of Image Detectors would then monitor it to differentiate between authentic and false photos. The Detector must improve its categorization quality starting with a 50% chance of correctness so that the Generator can create the artificial image more effectively. 

7. Self-Organizing Maps

Self-Organizing Maps (SOMS) are used for data visualization and significantly reduce data dimensions with the help of self-organizing neural networks. Their function is beneficial, especially in cases where people cannot easily interpret high-dimensional information.

8. Deep Reinforcement Learning

Deep Reinforcement Learnig (DRL) combines multiple layers of artificial neural networks with reinforcement learning to train machines to replicate the way human brain works and solve problems by trial and error. 

 The DRL  model constantly tries to forecast the future benefits of every action in a particular situation.

9. Autoencoders

Autoencoders, one of the most popular deep learning methods, is a self-supervised learning model that operates autonomously Autoencoders reduce the size of input by compressing it and then reconstruct the output.  

Autoencoders consist of three components: 

  • Encoder: The encoder is fully connected neural network that works as a compression unit that creates a code.
  • Decoder: The decoder decompresses and reconstructs the input. 

There are several types of autoencoders, including:

  • Sparse: Employs sparsity penalty or a sarsity constraint to achieve an information bottleneck. A sparsity penalty is the action of controlling the number of hidden layers that become active.
  • Denoising: Creates a corrupted copy of the input by adding noise. This prevents auoencoders from copying the input to the output without learning features about the data. 
  • Contractive: Helps a neural network encode unlabeled data. This type of autoencoder learns how to contract a group of inputs into a smaller group of inputs. 
  • Stacking: Generates a reduced representation from initial output by reconstructing it and minimizing the loss function. 

10. Backpropagation

This technique, often known as back-prop, is the transmission of data in a specific direction through a dedicated channel that enables neural networks to detect errors in data prediction.

 The entire system can operate by the signal propagation in the forward movement at the time of the decision and convey all the information regarding network defects back in the opposite way.

The network evaluates the parameters and determines how to weigh the data using a loss function. Any wrong parameters are manually adjusted after the network identifies any errors.

Examples of Deep Learning

Having in mind that deep learning algorithms process information similarly to human brains, it isn’t surprising that they successfully replace humans in many tasks.

Deep learning is widely applied in image and speech recognition programs and natural language processing (NLP). In everyday applications, deep learning is the technology that powers self-driving cars, automatic translation apps, voice assistants, medical diagnostics, online security, and countless others.

Some of the specific fields of application of deep learning include:

  • Customer experience: Deep learning is one of the technologies that powers chatbots, significantly contributing to customer experience and satisfaction in many industries.
  • Voice virtual assistants: Deep learning, along with machiine learning and artificial intelligence,  enables the work of voice assistants like iPhone’s Siri and Google Assistant.
  • eCommerce: Deep learning helps eCommerce sites navigate faster thanks to image labeling, contributing to a better overall user experience.
  • Text generation: Deep learning algorithms recognize and learn the grammar and writing style of provided pieces of text and then use this experience to independently create new textual content that supports the grammar and style of the original version. 
  • Fraud detection: Deep learning can help detect anomalies in user transactions and is used by all major banking companies to detect fraudulent credit cards.
  • Computer vision: Computers now have extremely high levels of accuracy for object detection, image categorization, restoration, and segmentation because of deep learning’s significant advancements in computer vision.
  • Self-driving vehicles: Deep learning is applied in self-driving automobile technology enabling vehicles to detect the shortest road path that will take you to the desired site, understand traffic signs and respect traffic laws, recognize people and objects on the side of the road, and adapt their driving style accordingly.
  • Healthcare: Deep learning significantly simplifies the analysis of medical records, facilitating more accurate diagnoses and therapy prescriptions. Medical research has also shown that deep learning can help detect skin cancer in images, promising future applications.
  • Military purposes: Deep learning is used in satellite technology to create images from the air and identify them as safe or unsafe zones, which is significant for national security issues.
  • Industrial automation: Deep learning helps improve worker safety by automatically identifying dangerous postures and alerting the users.

Wrapping up on Deep Learning

As mentioned above, deep learning is a subfield of machine learning and an advanced AI technology. 

The most widely used deep learning techniques are:

  • Classic  Neural Networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Restricted Boltzmann Machines
  • Transfer Learning
  • Generative Adversarial Networks
  • Self-Organizing Maps
  • Deep Reinforcement Learning
  • Autoencoders
  • Backpropagation

Deep learning is successfully applied in a wide range of fields including customer experience, voice virtual assistants, eCommerce, text generation, fraud detection, computer vision, self-driving vehicles, healthcare, military, and industrial automation, to name a few. 

There is no doubt the technology will evolve even further in the future and its application will expand to even more fields. 

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Neil Sahota
Neil Sahota (萨冠军) is an IBM Master Inventor, United Nations (UN) Artificial Intelligence (AI) Advisor, author of the best-seller Own the AI Revolution and sought-after speaker. With 20+ years of business experience, Neil works to inspire clients and business partners to foster innovation and develop next generation products/solutions powered by AI.