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Deep Learning: LEARN WHAT IT IS, HOW IT WORKS, AND ITS USE CASES

The literal translation is deep learning, but deep learning, a subcategory of machine learning and the broader world of artificial intelligence, underlies something much broader than simple multi-layered machine learning. So let’s try to understand what Deep Learning is, how it works, and what kind of applications it can have.

Deep Learning is a type of Machine Learning that focuses on using more complex neural networks to recognize relationships between different inputs by observing hidden data structures. A neural network is a network of interconnected artificial neurons, which are used to solve problems and perform necessary operations.

Neural networks have been designed to work like the human brain: input is processed by layers of neurons, which produce a more complex output. The power of the neural network comes from the fact that the number of layers increases as the information provided to the network increases.

Deep learning algorithms can be used to recognize patterns in large volumes of data, as well as to implement predictions that may not be immediately apparent. This technology has been widely used in the fields of artificial intelligence, computer vision, and speech recognition.

Neural networks can also be applied to decision-making processes where the data is complex or where the logic is not immediately apparent. Using them can help analyze data, recognize trends, and predict outcomes. Neural networks can also be used to create control systems that automatically react to certain situations, such as autonomous vehicle driving or air traffic control.

In addition, neural networks can be used to improve productivity in an organization although processes and procedures are already automated.

What is Deep Learning?

Deep Learning is a subcategory of Machine Learning and refers to the branch of artificial intelligence that refers to algorithms inspired by the structure and function of the brain, called artificial neural networks.

From a scientific point of view, it could be said that Deep learning represents the learning of machines through the processing of learned data using mainly statistical calculation algorithms.

Deep learning (also known as deep structured learning or hierarchical learning) is part of a larger family of machine learning methods based on the assimilation of data representations, as opposed to algorithms for performing specific tasks.

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Deep learning architectures (with which today the concept of the artificial neural network is brought to the attention of the general public) have been applied for example in computer vision, Automatic Speech recognition, Natural Language Processing, audio recognition, and Bioinformatics. i.e. the use of computer tools to describe from a numerical and statistical point of view certain biological phenomena such as gene sequences, the composition and structure of proteins, and biochemical processes in cells.

We have collected the different interpretations of some of the most well-known researchers and scientists in the field of deep learning:

  • Andrew Yan-Tak Ng, associate professor at Stanford University and former founder of Google Brain and Chief Scientist of Baidu;
  • Ian J. Goodfellow, a researcher at DeepMind and inventor of GAN networks;
  • Yoshua Bengio, authority in the field of Deep Learning;
  • Ilya Sutskever, Co-founder and Chief Scientist of OpenAI;
  • Geoffrey Everest Hinton, one of the key figures in Deep Learning and Artificial Intelligence, is the first researcher to demonstrate the use of a generalized backpropagation algorithm for the training of multilayer neural networks.

We can therefore define Deep Learning as a system that leverages a class of machine learning algorithms that:

  1. They use various layers of cascading nonlinear units to perform feature extraction and transformation tasks. Each subsequent layer uses the output of the previous layer as input. Algorithms can be both supervised and unsupervised, and applications include pattern analysis (unsupervised learning) and classification (supervised learning);
  2. They are based on the unsupervised learning of multiple hierarchical levels of data features (and representations). The higher-level characteristics are derived from the lower-level ones to create a hierarchical representation;
  3. are part of the broader class of algorithms learning data representation within machine learning (Machine Learning);
  4. they learn multiple levels of representation that correspond to different levels of abstraction; These levels form a hierarchy of concepts.
The different interpretations of some of the most well-known researchers and scientists in the field of deep learning

By applying Deep Learning, we will therefore have a machine that can autonomously classify data and structure them hierarchically, finding the most relevant and useful ones for solving a problem (exactly as the human mind does), improving its performance with continuous learning.

Artificial neural networks, the basis of deep learning

As mentioned in the previous paragraph, deep learning is based on classifying and selecting the most relevant data to conclude.

This process echoes the workings of our biological brain, in which neurons and neural connections are activated to formulate responses, deduce logical hypotheses, and solve problems.

Interconnected biological neurons form our brain neural networks, which allow each individual to reason, make calculations in parallel, recognize sounds, images, and faces, learn, and act.

Deep Learning behaves in the same way and exploits artificial neural networks, mathematical-computer computing models based on the functioning of biological neural networks, i.e. models consisting of interconnections of information.

A neural network is an adaptive system capable of modifying its structure (nodes and interconnections) based on both external data and internal information that connects and passes through the neural network during the learning and reasoning phase.

FOR FURTHER INFORMATION ON NEURAL NETWORKS, PLEASE REFER TO: “Neural networks: what they are and what they are for”

How Deep Learning Works

With Deep Learning, the learning processes of the biological brain are simulated through artificial systems (artificial neural networks, in fact) to teach machines not only to learn autonomously but to do so in a more “deep” way as the human brain can do, where deep means on several levels (i.e. on the number of layers hidden in the neural network – called hidden layers: Traditional neural networks contain 2-3 layers, while deep neural networks can contain over 150).

The image below (taken from the free online eBook Neural Networks and Deep Learning) can help you better understand the structure of deep neural networks.

The Structure of Deep Neural Networks

The Structure of Deep Neural Networks

Deep neural networks take advantage of a greater number of intermediate layers (hidden layers) to build more layers of abstraction, just as is done in Boolean circuits, the mathematical models of computation used in the study of computational complexity theory which, in computer science, studies the minimum resources needed – mainly computational time and memory – to solve a problem.

Let’s try to give a concrete example of how a deep neural network works with visual pattern recognition: the neurons of the first layer could learn to recognize edges. The neurons in the second layer might learn to recognize more complex shapes, such as triangles or rectangles, created by the edges. The third layer would recognize even more complex shapes, the fourth would recognize further details, and so on.

The multiple layers of abstraction can give deep neural networks a huge advantage in learning how to solve complex pattern recognition problems, precisely because at each intermediate level they add useful information and analysis to provide reliable output.

It’s pretty easy to guess that the more intermediate layers there are in a deep neural network (so the larger the neural network itself) the more effective the result. In contrast, neural network scalability is closely related to datasets, mathematical models, and computational resources.

Scalability of Deep Learning

Although the demand for immense computational capabilities is an obstacle, the scalability of deep learning, thanks to the increase in available data and algorithms, is what sets it apart from machine learning.

Deep learning systems improve their performance as the data increases, while machine learning applications, also known as surface learning systems, once they reach a certain level of performance, are no longer scalable even by adding examples and training data to the neural network.

This is because, in Machine Learning systems, the characteristics of a given object (in the case of visual recognition systems) are extracted and selected manually and are used to create a model capable of categorizing objects (based on the classification and recognition of those characteristics).

In Deep Learning systems, on the other hand, the extraction of features takes place automatically: the neural network autonomously learns how to analyze raw data and how to perform a task (for example, classifying an object by autonomously recognizing its characteristics).

If from the point of view of potential, Deep Learning may seem more fascinating and useful than Machine Learning, it should be noted that the Computational calculation required for their operation is impactful, even from an economic point of view: the most advanced CPUs and top-of-the-range GPUs useful for supporting the workloads of a Deep Learning system still cost thousands of dollars.

The use of computational capabilities via the Cloud only partially mitigates the problem because the formation of a deep neural network often requires the processing of large amounts of data using high-end GPU clusters for many, many hours (it is therefore not certain that buying the necessary computing capacity as a service is cheap).

How to train a Deep Learning system

A very simple but effective example to understand the real functioning of a Machine Learning system (and the difference with a Deep Learning system) is provided by Tech Target:

«While traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction. To understand deep learning, let’s imagine a child whose first word is “dog.” The child learns what a dog is (and what it is not) by pointing to objects and saying the word dog. The parent says, “Yes, that’s a dog” or “No, it’s not a dog.” As the child continues to point at objects, he becomes more aware of the characteristics that all dogs possess. What the child does, without knowing it, is to clarify a complex abstraction (the concept of the dog) by constructing a hierarchy in which each level of abstraction is created with the knowledge that has been acquired from the previous layer of the hierarchy.

Unlike the child, who will take weeks or even months to understand the concept of a dog and will do so with the help of the parent (what is called supervised learning), an application that uses Deep Learning algorithms can show and sort millions of images, identifying precisely which images contain the datasets, in a few minutes while not having had any kind of direction on the correctness or otherwise of the identification of certain images during the training.

In Deep Learning, Data Is Labeled

Usually, in Deep Learning systems, the only precaution of scientists is to label the data (with meta tags), for example by inserting the meta tag “dog” inside the images that contain a dog but without explaining to the system how to recognize it: it is the system itself, through multiple hierarchical levels, that intuits what characterizes a dog (the paws, the thing, the hair, etc.) and therefore how to recognize it.

These systems are based, in essence, on a trial-and-error learning process, but for the final output to be reliable, huge amounts of data are required.

However, it would be a mistake to immediately think of Big Data and the ease with which data of any form and from any source is produced and distributed as an easy solution: the accuracy of the output requires, at least in the first phase of training, the use of labeled data (containing meta tags) which means that the use of unstructured data could be a problem. Unstructured data can be analyzed by a deep learning model once it has been trained and reached an acceptable level of accuracy, but not for the system training phase.

Not only that, deep learning-based systems are difficult to train due to the very number of layers in the neural network. The number of layers and links between neurons in the network is such that it can become difficult to calculate the “adjustments” that need to be made at each stage of the training process (a problem referred to as the gradient disappearance problem).

This is because so-called Back Propagation algorithms are commonly used for training, through which the weights of the neural network (the connections between neurons) are revised in case of errors (the network propagates the error backward so that the weights of the connections are updated more appropriately). A process that continues iteratively until the gradient (the element that gives the direction in which the algorithm should move) is zero.

Deep Learning Framework: From TensorFlow to PyTorch

One of the most widely used specific frameworks for Deep Learning among researchers, developers, and data scientists is TensorFlow, a well-known Open-source software library (project supported by Google) for machine learning that provides tested and optimized modules for the creation of algorithms to be used in different types of software and with different types of programming languages. from Python, C/C++, Java, Go, RUST, R, … (in particular for perceptual tasks and natural language comprehension).

Since 2019, PyTorch, an open-source project developed by Facebook and now part of the Linux galaxy, has made its way.

Initially (and for several years) Meta’s developers used a framework known as Caffe2, which has also been adopted by many universities and researchers. In 2018, however, Facebook announced that it was working on another type of framework accessible to the open-source community, which would combine the best of Caffe2 and ONNX in a new framework (PyTorch).

ONNX stands for Open Neural Network Exchange and is an interoperable framework to which Microsoft and AWS also actively contribute by providing support for Microsoft CNTK and Apache MXNet. PyTorch 1.0 combines the best of Caffe2 and ONNX (it is one of the first frameworks with native support for ONNX models).

What the developers of Meta (but not only) focused on was the creation of a much simpler and more accessible framework than TensorFlow. PyTorch, for example, uses a technique known as dynamic computing that makes it easy to train neural networks.

Not only that, PyTorch’s execution model mimics the conventional programming model known to an average Python developer. It also offers distributed training, deep integration into Python, and a vibrant ecosystem of tools and libraries (such as Keras).

Since September 2022, Meta has announced the birth of the PyTorch Foundation, an independent organization within the Linux Foundation.

The Use Cases and Types of Applications of Deep Learning

Despite the problems we have illustrated, Deep learning systems have made enormous evolutionary strides and have improved a lot in recent years, especially due to the huge amount of data available but above all due to the availability of ultra-performing infrastructures (CPU and GPU in particular).

In the field of artificial intelligence research, machine learning has enjoyed considerable success in recent years, allowing computers to surpass or approach corresponding human performance in areas ranging from facial recognition to speech and language recognition. Deep learning, on the other hand, allows computers to take a step forward, specifically to solve several complex problems.

As a simple non-tech-savvy citizen, it is possible to notice several use cases and areas of application:

The tasks that a machine can perform thanks to Deep Learning

  1. automatic colorization of black and white images: for the neural network it means recognizing edges, backgrounds, and details and knowing the typical colors of a butterfly, for example, knowing exactly where to place the correct color;
  2. automatic addition of sounds to silent movies: for the Deep Learning system it means synthesizing sounds and placing them correctly within a particular situation by recognizing images and actions, for example by inserting the sound of the jackhammer, the breaking of the asphalt, and the subfloors of a busy city street in a video in which workers are seen breaking the asphalt with the pneumatic hammer;
  3. simultaneous translation:p for the Deep Learning system means listening to and recognizing natural language, recognizing spoken language, and translating meaning into another language;
  4. classification of objects within a photograph: the system in this case can recognize and classify everything it sees in an image, even a very complex one where, for example, there is a background landscape, mountains, people walking along a path, grazing animals, etc.;
  5. automatic handwriting generation: there are already Deep Learning systems capable of using human handwriting to write, even learning the estimates of human handwriting and imitating it;
  6. Automatic text generation: This is a process in which systems learn to write correctly in the chosen language, respecting spelling, punctuation, and grammar. In addition to that, these systems also learn to use different styles of writing, depending on the goals, such as producing news articles or short stories.
  7. automatic generation of captions: in this case, the recognition of the images, the analysis of the context, and the ability to write allow a system to automatically write the captions of an image, perfectly describing the scene;
  8. Autoplay: We discovered the potential of a system capable of autonomously learning to play a certain game thanks to DeepMind, now part of Google. Through his Deep Learning system called AlphaGo, he not only learned how to play the very complex Go game but also defeated the human world champion. This extraordinary demonstration of artificial intelligence has opened up new perspectives in the field of machine learning and has generated great fascination and interest around the world.

Some of the fields of application of deep learning

Driverless car

Traffic Sign Detection (TDS) is a feature on many new cars that allows you to recognize traffic signs. It is a machine learning application that uses convolutional neural networks and frameworks such as TensorFlow.

Cinema

At the beginning of 2021, a new AI methodology applicable to film production was presented. The new approach leverages a parallel and sequential combination of various deep learning tools such as VGG16, MLP, and transfer learning.

By using diversified datasets and highlighting different characteristics of the images, the aim is to obtain an accurate classification of cinematic shots.

This allows a professional and operational use in the process of film creation and indexing of streaming content. Let’s talk about AI applied to image processing.

Quantum Intelligence

There are high expectations for quantum computing. An innovative computing paradigm requires not only new hardware technologies, and advanced algorithms, but also state-of-the-art solutions. Its main feature lies in its ability to greatly simplify the solution of complex problems, reducing their exponential complexity.

Take, for example, determining the factors of a number, a fundamental problem in cryptography and with numerous applications in the field of computer security. This paradigm opens up new perspectives and opens the door to a world of unprecedented opportunities.

Holograms

A study conducted on holograms in 2021 by researchers at the Massachusetts Institute of Technology (MIT) showed that thanks to the innovative deep technique called tensor holography, it is possible to instantly generate holographic videos using the computing power of a common computer.

The unique aspect of this technology lies in the use of trainable tensors, capable of learning how to process visual and depth information in a similar way to the human brain. This allows you to achieve extraordinary results, with unprecedented realism and detail.

Author

  • Geekay Dutta

    Welcome to my world! I'm Goutam Kumar Dutta, the brains behind this platform. As an author and the proud owner of this site, I'm on a mission to bring you the latest and most intriguing sports news from various genres. But it's not just about sports - entertainment in all its forms also captivates my interest. Whether it's analyzing the latest match or delving into the world of entertainment, I strive to provide comprehensive coverage and valuable insights.

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