What are Neural Networks?
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This 365 data science tutorial is an introductory video to neural networks- computing systems that process and analyze data mimicking the functions of the human brain. They are an artificial intelligence method and a type of machine learning called deep learning which requires minimal human supervision.
In this line of thought, creating a machine learning model means building a model that outputs correct information, given that we are provided input data. For now, think of the model as a black box. We feed input and it delivers an output. For instance, we may want to create a model that predicts the weather tomorrow, given meteorological for the past few days. The input we will feed to the model could be metrics such as humidity, temperature, and precipitation. The output we will obtain would be the weather forecast for tomorrow.
Before we get confident about the model’s output, we must train the model. Training is a central concept in machine learning, as this is the process through which the model learn how to make sense of the input data . Once we have trained our model, we can simply feed it data and obtain an output.
The four ingredients involved in training an algorithm are:
Data
Model
Objective Function
Optimization Algorithm
Watch the rest of the video where we will go in detail on each one of the ingredients
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