Can single input of neural net consist of multiple data?

[fajar19]

Hi, I'am Fajar. And i'm new on R language.
Now, i'm on my final project of my study and i'm using machine learning for my final project.
My final project is about detecting movement of vehicle from the data of accelerometer and gyroscope sensor. I'm using neural network for movement classification. And i have some questions about neural net in R.

1. Can single input of neural net consist of multiple data? I mean 1 neuron for multiple data at the same time. Lets say i collect turn right data. And i have 20 values of accelerometer x-axis in 5 seconds. Can i enter that 20 values to 1 input of neural net? or i must make 20 inputs of neural net to process that data?

2. I get accel values like 1.2, 0.2, 0.75. Do i have to normalize the data before entering the neural network? or can i input it without normalize?

Thank you. And i'm sorry for my bad english

[technocrat]

Hi, and welcome!

First off, English is a world language, spoken and written many different ways, even within the countries where it is the dominant language. There is no single right way to express a thought in English and everybody who uses English experiences difficulties in communication and/or feels unsure about it. Don't here. If we don't understand a question, we'll ask for clarifications.

Second, if applicable, see the homework policy. This is such a general question, that most of that need not apply here.

Third, a reproducible example, called a reprex is always very helpful in attracting more and better answers. The data can be an extract of the data you are using or, ideally, a built-in R dataset with the same structure or code to transform it into the same structure. (I get a lot of mileage out of mtcars that way!)

Both your questions depend quite a bit on the specific neural network package that you are using. With a reprex, the community can look at the possibility and offer advice.

Often it comes down to a key insight: Using R is school algebra f(x) = y writ large. The key is understanding the arguments to f() and the results.