Unraveling Deep Learning: An Introduction to Neural Networks
Artificial intelligence (AI) has been transforming the world in extraordinary ways, and at the core of this transformation, there is a fascinating subarea known as deep learning. But what is deep learning and how does it enable machines to “learn” autonomously? In this article, we dive into the depths of artificial neural networks, unraveling the secrets of this revolutionary technology that drives countless advances in the field of AI.
Table of Contents
What is Deep Learning?
Deep learning is an advanced technique of machine learning, an approach in which machines use a series of algorithms to interpret data, perceive patterns and make decisions with little or no human intervention. A distinctive feature of deep learning is its ability to process and model effectively large volumes of complex information.
Using artificial neural networks, deep learning is able to perform complex tasks, such as image recognition, natural language processing, content generation, among others.
What are artificial neural networks?
Artificial neural networks are computational models inspired by the functioning of the human brain. They are composed of processing units called neurons, which connect to each other through synaptic weights. Each neuron receives a set of inputs, performs a mathematical operation on them and produces an output. The output of a neuron can be the input of another, forming a network of neurons.
Artificial neural networks can be trained to learn from data, adjusting their synaptic weights according to an optimization algorithm. The goal is for the neural network to be able to produce the desired outputs for the inputs provided, minimizing an error or a cost function.
What are the layers, neurons and activation functions of a neural network?
Artificial neural networks can be divided into layers, which are groups of neurons that perform the same operation. Each layer has a unique function:
- Input Layer: Here, the raw data are received. Each node represents a different feature of the input data.
- Hidden Layers: These are the heart of the network, where most of the processing occurs through a series of transformations.
- Output Layer: The results of the processing are presented here, offering the conclusions or predictions of the network.
The neurons of a neural network can have different types, depending on their function and their layer. The simplest neurons are the perceptrons , which perform a weighted sum of the inputs and apply a binary activation function, which returns 0 or 1.
The most common neurons are the sigmoids , which perform a weighted sum of the inputs and apply a sigmoid activation function, which returns a value between 0 and 1.
Other types of neurons are the tanh , which apply a hyperbolic tangent activation function, which returns a value between -1 and 1, and the ReLU , which apply a rectified linear activation function, which returns the maximum between 0 and the input.
The neurons in each layer are connected by weighted connections, whose weights are adjusted during the training of the network. The activation functions, in turn, are mathematical formulas that decide whether a neuron should be activated or not, introducing non-linearity to the process and allowing the network to learn from complex data. They also help to regularize the neural network, avoiding problems such as overfitting, which occurs when the neural network memorizes the training data and does not generalize to new data.
What are the most used types of neural networks?
There are several types of neural networks, which differ by their architecture, their domain of application and their method of training. Some of the most used types are:
- Perceptron: the precursor of modern neural networks, it is the simplest neural network, which consists of a single neuron. Despite its simplicity, it laid the foundations for the development of more complex networks. It is used to perform binary classification tasks, such as identifying whether an email is spam or not.
- MLP (Multilayer Perceptron): it is a neural network that consists of several layers of neurons, connected in a fully connected way. It is used to perform classification and regression tasks, such as recognizing handwritten digits or predicting the price of a house. They are characterized by having one or more hidden layers between the input and the output, allowing them to learn more complex patterns.
- CNN (Convolutional Neural Network): it is a neural network that consists of layers of neurons, connected in a locally connected way. It is used to perform image processing tasks, such as recognizing objects, faces or scenes.
- RNN (Recurrent Neural Network): it is a neural network that consists of layers of neurons, connected in a recurrent way. It is used to perform sequence processing tasks, such as translating texts, generating captions or composing music.
- GAN (Generative Adversarial Network): it is a neural network that consists of two neural networks, a generator and a discriminator, that compete with each other. It is used to perform content generation tasks, such as creating images, texts or videos.
What are the applications of deep learning?
Deep learning has revolutionized various areas of human knowledge, thanks to its ability to learn from large volumes of data, automatically and efficiently. Some of the applications of deep learning are:
- Image recognition: deep learning allows computers to recognize objects, faces, scenes, gestures, emotions and other visual features in images, with high accuracy and speed. This can be used to improve security, medicine, education, entertainment and other areas. Deep learning systems are the technological basis behind the functionalities of facial recognition in smartphones and advanced medical diagnostics that identify diseases in X-ray images with super-human accuracy.
- Natural language processing: deep learning allows computers to understand, generate and translate texts, with a high degree of naturalness and coherence. This can be used to improve communication, information, education, entertainment and other areas. Thanks to RNN and variants such as LSTM (Long Short-Term Memory) networks, deep learning has transformed the way we interact with machines, enabling natural conversations with virtual assistants and almost real-time automatic language translation.
- Content generation: deep learning allows computers to create original content, such as images, texts, videos, music, among others, with a high degree of creativity and diversity. This can be used to improve art, culture, entertainment and other areas.
Challenges and Future of Deep Learning
While the achievements are many, deep learning is not free of challenges. The need for large volumes of data for training, the difficulty of explaining how decisions are made by neural networks (called “black box”), and the high demand for computational resources, are barriers that scientists and engineers are working to overcome.
The future of deep learning is promising, with research aimed at making the models more efficient, understandable and less dependent on large data sets. As these techniques become more accessible and comprehensible, their application can expand even more, positively impacting virtually all sectors of society.
Conclusion
Deep learning is a subarea of machine learning, which uses artificial neural networks to perform complex tasks, such as image recognition, natural language processing, content generation, among others.
Artificial neural networks are computational models inspired by the functioning of the human brain, which are composed of layers, neurons and activation functions. There are several types of neural networks, which differ by their architecture, their domain of application and their method of training.
Deep learning has revolutionized various areas of human knowledge, thanks to its ability to learn from large volumes of data, automatically and efficiently.
