9 examples of deep learning algorithms

The world of artificial intelligence has been revolutionised by deep learning, a fascinating field of study that has transformed the way machines learn and understand the world around us. In this blog post, we will explore in detail what deep learning is, what makes it so special and what are the main algorithms used in deep learning techniques.

What is deep learning?

The deep learning, deep learning, also known as deep learning, is a branch of machine learning that relies on artificial neural networks. Unlike traditional machine learning, where machines learn through explicit rules programmed by humans, deep learning allows machines to learn automatically from large amounts of data.

In the world of technology and artificial intelligence, deep learning is considered a breakthrough innovation because of its ability to learn complex, hierarchical representations of data automatically. In other words, human intervention in deep learning algorithms is almost non-existent once they are set up.

Deep learning is thus predicted to become pervasive in a large number of disciplines, bringing unprecedented improvements in areas such as healthcare, industrial robotics, art, translation, image recognition, data analytics, finance, and much more.

How does deep learning differ from machine learning?

Although the two terms may seem synonymous and we find them in similar contexts, they actually refer to different techniques for training an AI. If you want to talk accurately about artificial intelligence, it is essential to understand the disparity between the two.

Firstly, it is important to note that deep learning is within the field of machine learning.. In other words, deep learning represents a specialisation of machine learning, not a separate entity. We could say that deep learning goes one step further from the traditional machine learning approach, as it employs more complex and sophisticated neural networks.

In deep learning, the algorithm acquires and evaluates a vast amount of data on its own, similar to how a human being evaluates itself. This means that it does not require as much human intervention as conventional machine learning.

How do deep learning algorithms work?

Although the deep learning algorithms possess self-learning qualities, they are directly dependent on rArtificial neural neurons (ANNs) that emulate the way the human brain processes and relates information. Throughout training, algorithms make use of unknown elements within the input to group data and trace logical patterns in it.

Several algorithms are used in deep learning models. Although none are considered definitive or flawless, there are a few specific algorithms that are more efficient for specific tasks. In order to distinguish between them and choose the best one for each task, it is necessary to have a good understanding of how they work and what their role is in machine learning.

Examples of deep learning algorithms

Convolutional Neural Networks (CNN)

These are neural networks specialised in image processing. They use convolutional layers to detect relevant features and reduce the dimensionality of images, enabling more efficient learning in computer vision tasks.

Neural networks of short-long term memory (LSTM)

They are a type of recurrent neural network designed to work with sequences of data, such as text or audio. Their structure allows them to remember long-term patterns, making them very effective for natural language processing and other sequential tasks.

Recurrent Neural Networks (RNN)

They are neural networks that have recurrent connections, which are perfect for maintaining a kind of internal memory about previous data. They are used in tasks involving sequences, such as machine translation and text generation.

Generative adversarial neural networks (GAN)

They are a special approach to unsupervised learning that consists of two complementary networks: a generator and a discriminator, which compete with each other. The generator creates synthetic samples, while the discriminator tries to distinguish between real and synthetic samples. Thus, both improve their performance without human intervention, continuously competing with each other.

Radial Basis Neural Networks (RBFN)

These are neural networks with layers of radial neurons using radial basis functions as the activation function. They are frequently used in pattern approximation and classification tasks.

Multilayer perceptrons (MLPs)

They are a classical form of neural networks, composed of multiple hidden layers between the input layer and the output layer. They are versatile and are used in a wide range of supervised and unsupervised learning tasks.

Self-Organising Maps (SOM)

They are a class of competitive neural networks used for unsupervised learning and dimensionality reduction. They are especially useful for visualising complex data in two-dimensional maps.

Deep belief networks (DBN)

They are probabilistic neural networks composed of multiple generative and discriminative layers. They are used in unsupervised learning tasks and are often pre-trained layer by layer before being tuned to specific data.

Boltzmann Machine (RBM)

They are a specialised form of probabilistic neural network used in unsupervised learning to find patterns in high-dimensional data.

Want to get the most out of deep learning algorithms?

In order to immerse yourself in the fascinating world of deep learning, it is essential to have a solid foundation in mathematics and programming. Although there are resources designed for beginners, deep learning is a complex discipline involving mathematical concepts such as linear algebra, calculus, statistics and probability.

The path to mastering deep learning may take time, dedication and constant practice, but don't worry, it's an exciting and rewarding journey! Start with the basics of mathematics and programming, and gradually work your way into the more advanced concepts of deep learning algorithms and neural networks.

At Euroinnova we offer you state-of-the-art training, 100% online and specially adapted to your level. Our deep learning training catalogue will allow you to develop cutting-edge skills in this revolutionary discipline. 

Are you ready to embark on an unprecedented educational experience? Discover our deep learning training options and choose the one that best suits your needs and goals!