{"id":6988,"date":"2024-01-18T12:56:46","date_gmt":"2024-01-18T11:56:46","guid":{"rendered":"https:\/\/www.artea.com\/what-is-deep-learning-definition-and-applications\/"},"modified":"2026-09-29T09:29:53","modified_gmt":"2026-09-29T07:29:53","slug":"what-is-deep-learning-definition-and-applications","status":"publish","type":"post","link":"https:\/\/www.artea.com\/en\/what-is-deep-learning-definition-and-applications\/","title":{"rendered":"WHAT IS DEEP LEARNING: DEFINITION AND APPLICATIONS"},"content":{"rendered":"\t\t
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Deep learning<\/strong> is a branch of machine learning that uses deep layers of artificial neural networks<\/strong> to analyze data and learn complex patterns from it.<\/p>

At the intersection of artificial intelligence and machine learning, deep learning represents a frontier in technological research: with it, neural networks<\/strong><\/a> not only learn, but do so at levels of complexity and depth that go far beyond what is traditional.<\/p>

In this article, we aim to define what deep learning is<\/strong>, highlighting how these technologies are redefining what machines can do and providing an overview of their numerous and surprising applications.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The Era of the Perceptron and the Evolution Toward Deep Learning<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The Perceptron<\/strong>, one of the first neural network models, introduced by Rosenblatt<\/strong> in the 1950s, broke new ground in the field of AI. However, a significant limitation soon became apparent: the \u201csingle-layer\u201d perceptron could not learn or recognize many classes of patterns. This realization significantly slowed down research until the power of a neural network known as a \u201cmultilayer\u201d perceptron was explored. <\/p>

A key component of the multilayer<\/strong> perceptron<\/strong> is the so-called “hidden layer.” While in a single-layer perceptron, the input is directly transformed into an output, in a multilayer network there are one or more internal layers of nodes, hidden between the input and the output. These intermediate layers can capture and model complexities and abstractions that are not immediately visible at the network\u2019s input or output. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The Power of Hidden Layers and the Deep Learning Revolution<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Deep learning takes this concept even further. It is based on neural networks with multiple hidden layers<\/strong>. Each layer tends to specialize in different aspects of the cognitive process, operating at increasingly higher levels of abstraction. In this way, the additional layers enable the network to construct and learn hierarchies of features\u2014from the simplest to the most complex\u2014mirroring the human process of incremental learning. <\/p>

Thanks to this architecture, deep learning is revolutionizing numerous fields\u2014from computer vision to natural language understanding\u2014and offers unimaginable opportunities.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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So what is the difference between machine learning and deep learning?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Machine Learning (ML) and Deep Learning (DL)<\/strong> are two closely related concepts in the field of artificial intelligence, but they have significant<\/strong> differences<\/strong>.<\/p>

Machine Learning (ML)<\/strong> is a broad field that encompasses methods and techniques for teaching machines how to learn from data. It uses algorithms that can learn and make predictions or decisions based on past data, without being explicitly programmed to do so. <\/p>

Deep Learning (DL)<\/strong> is the branch of Machine Learning that uses neural networks with multiple layers (hence the term “deep”). These layers allow the model to learn automatically and progressively from data through successive levels of abstraction. <\/p>

The difference between Machine Learning and Deep Learning<\/strong> lies in the fact that ML can use both simple and complex methods, while DL focuses specifically on complex neural networks and much larger datasets, allowing it to capture more subtle and abstract relationships in the data. In summary, DL is a specialization within the broader field of ML, leveraging more elaborate network architectures to tackle more complex challenges. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The breakthrough in deep learning lies in the numbers<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Deep learning is a revolution in that it is driven by an exponential increase in both the number of nodes and parameters in neural networks and the size of the datasets<\/strong> required to train them. To understand the magnitude of this leap, let\u2019s consider these two variables: <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Billions of Parameters for a Neural Network<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Modern deep learning (DL) neural networks, especially in large language models (LLMs), can have an impressive number of training parameters\u2014 up to 350 billion in open-source models<\/strong>.<\/p>

Version 3.5 of ChatGPT<\/strong> has approximately 175 billion parameters, while the upcoming version 4.0 reaches 100,000 billion. Olympus, the new model currently under development by Amazon, promises to multiply this figure by a thousand. These numbers demonstrate exponential growth in terms of complexity and processing power. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Massive datasets for training<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The dataset required to train these networks is equally impressive. A token<\/strong>\u2014which can be a syllable or a piece of text\u2014represents the basic unit of input for an LLM model. For example, Meta\u2019s Llama2 was trained on approximately 2 trillion tokens, while the amount of data in Wikipedia is on the order of 4 billion words. This vast amount of data requires unprecedented computational power and raises important questions regarding energy consumption and environmental impact, such as the large associated carbon footprint. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The World of Deep Learning: An Overview of Applications<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The DL has already been used in extraordinary ways across various sectors, demonstrating its versatility and power. Here are a few examples: <\/p>