3 min ler
The field of data science is gradually moving away from human hands and continues to be replaced by artificial intelligence and machines. Instead of people having to spend a lot of time on complex data, trained algorithms and data tools can accomplish a task very quickly. One of these powerful data analysis tools is neural networks. It is excellent in identifying patterns within complex data sets and gives accurate results.
First things first: What is a neural network?
A neural network is a computational model that works similarly to the human brain's network of neurons.
However, they differ in that they are artificial neural networks. Layers called neurons are connected to each other and constantly transfer information from one place to another. There is a balance of power between these connections, and in pattern recognition, each neuron reveals information about that pattern. But the correct ones gain weight, and the wrong neurons shrink. Thus, it can easily identify the most complex texts and images.
The process of the neural network
Neural network process
1. The neural network process starts with providing input data, which can be called supervised learning. In this step, the previously collected data is loaded into the system. This data then travels between hidden layers, and each neuron makes calculations based on these inputs.
2. These neurons are constantly adaptive and changing. Neurons try to uncover patterns by assigning weights to inputs. The results of some neurons give more accurate results than others, and the network highlights them according to the weight of these correct results.
3. This information transfer process continues through input layers and eventually reaches the final output layer. In this layer, a final prediction emerges as a result of the processed data. To give an example to make this concrete, facial recognition technology also uses neural networks.
First, images of what a face is are loaded into them, and things that do not resemble a face are given as input for negation. When you later ask neural networks to identify certain faces, they can now tell whether they are faces or not as a result of what they have learned.
How to use the neural network in data analysis
To use neural networks in data analysis, you must follow specific steps: First, a dataset for network analysis should be collected. This data collection process is the most critical step for the neural network to work correctly. Then, you need to prepare this model by choosing a model suitable for data classification. For example, you can use recurrent neural networks to analyze textual data.
In the next step, you need to train the neurons. Determine which information they should focus on with the help of weights. Later, neural networks will establish new hierarchies with connections among themselves. You should test this network model with small samples and save it for later use. From now on, the neural network you have prepared can now be used continuously in data analysis.
Frequently asked questions about the neural networks
In this section, you can learn more general answers and precise information about neural networks.
Você pode revelar relações complexas com mais facilidade com redes neurais, que podem ser ativamente usadas em muitos aspectos diferentes da análise de dados. Antes de realizar a análise de dados, é necessário alimentar essas redes neurais com dados ou treiná-las inicialmente.
Essas entradas podem ajudar posteriormente em muitos aspectos da análise de dados. Por exemplo, redes neurais podem ser usadas em classificação de padrões, examinando textos complexos, previsões, reconhecimento de imagens, interpretação de dados, visualização e muitas outras áreas.
Se você quiser escolher uma rede neural para classificação de dados, primeiro considere a natureza dos dados e os requisitos do seu propósito. Existem alguns tipos de redes neurais com funções de análise de dados que são frequentemente usados para diferentes tipos de classificação de dados.
-Analisando dados textuais: Rede Neural Recorrente
-Analisando dados tabulares: Perceptron Multicamadas
-Analisando dados de grafos: Rede Neural de Grafos
-Analisando dados de imagens: Rede Neural Convolucional
Sim, os cientistas de dados usam ativamente e frequentemente redes neurais para aprendizado profundo em suas pesquisas. Eles as usam para resolver um trabalho de forma eficaz e rápida, revelar relações complexas e facilitar a maioria das tarefas. Graças ao uso versátil de redes neurais, eles encontram muitas áreas de aplicação diferentes, como medicina, economia, marketing e produção.
Por exemplo, um cientista de dados na área de saúde pode desenvolver uma rede neural para analisar raios-X. Um cientista de dados na área de finanças pode criar um modelo que detecte fraudes.
Em primeiro lugar, é necessário usar um método usado na análise de dados textuais, como uma rede neural recorrente, deve ser usada. No entanto, isso por si só não é suficiente; é necessário apoiá-lo com redes de memória de longo e curto prazo. Dessa forma, você obtém uma rede que pode entender até mesmo frases complexas.
Conclusion
This article was an introductory and informative article about what neural networks are. We tried to show how you can get valuable insights from it as a unique tool in the data analysis world. Therefore, the article first started with the definition of a neural network and then explained its processes and how to use it in data analysis. From now on, you can use neural networks effectively and perform powerful data analysis.
Atakan is a content writer at forms.app. He likes to research various fields like history, sociology, and psychology. He knows English and Korean. His expertise lies in data analysis, data types, and methods.
