Diagram 3 - Relationship between Artificial Intelligence Terms

Author's work

This simple Venn diagram helps explain the relationship and difference between Deep learning, Machine learning and AI and explains in which of them Data science is used. In summary, artificial intelligence is the collection of tasks that machines are able to execute. Machines learn these tasks with the help of machine learning and deep learning for specific cases. Together, these three processes are utilized in data science so that data can be converted into insights.

Comments

Neural Network (Initial Idea 2)

Neural Networks Neural Networks, colloquially known as artificial neural networks (ANNs) and also simulated neural networks (SNNs), are a sub-fiend of machine learning which in it self is a sub-field of Artificial Intelligence, they are the brain of AI and are components of deep learning. The name and structure of NNs are similar to the human brain and were inspired by it, they imitate the biological neurons by signaling to each other (IBM, 2020). ANNs consist of an input layer of neurons, one or two hidden layers of neurons and a final layer of output neurons (Wang, 2003). The diagram illustrated exemplifies a simple architecture of a neural network, the lines which connect the neurons are called weights and are associated with a number which represent the strength of connection between neurons. There are many different types of neural networks and each are used for specific motives. Below are some of the most common ones used in the field: The perceptron “t...

Computer methodology - Artificial intelligence

Diagram of a simple feed-forward artificial neural network, with one “hidden layer,” also known as a “perceptron.” Image: Wikipedia Deep learning simply means “stacked neural networks” which are networks composed of several layers (Pathmind, 2021). Based on Rosenblatt’s perceptron, the simplest version of an artificial neural network has three layers of neurons. The first is the input layer which takes input values ,which in other words is data. This first layer of neurons is connected to the “hidden” layer. The outputs of these “hidden” neurons are then connected to the final output layer. This final layer is what gives you the answer to what the network has been trained to do (Medium, 2021). The simplest neural networks consist of input layer, hidden layer and output layer, which are attached to each other with predictors, these predictors come attached with coefficients called "weights" , this is a non-linear network known as multilayer feed-foward network (Hyndman, R.J....

Computer skills and Research Sofiane Benzid

Search This Blog