🌱Introduction

👩‍🏫 Concepts of neural network with theoric details

🔎 Definition

A neural network is a type of machine learning which models itself after the human brain. This creates an artificial neural network that via an algorithm allows the computer to learn by incorporating new data.

Neural networks are able to perform what has been termed deep learning. While the basic unit of the brain is the neuron, the essential building block of an artificial neural network is a perceptron which accomplishes simple signal processing, and these are then connected into a large mesh network.

📑 Types of NNs

There are many types of neural networks, choosing a type is due to the problem that we are trying to solve, for example

Type	Description	Application
👼 Standard NN	We input some features and estimate the output	Online Advertising, Real Estate
🎨 CNN	We add convolutions for feature extraction	Photo Tagging
🔃 RNN	Suitable for sequence data	Machine Translation, Speech Recognition
🤨 Custom NN / Hybrid	For complex problems	Autonomous Driving

🎨 Types of Data in Supervised Learning

• 🚧 Structured Data

  • Such as tables

  • We have input fields and an output field

• 🤹‍♂️ Unstructured Data

  • Such as images, audio and texts

  • We need to use feature extraction algorithms to build our model

🧐 References

• Introduction to Artificial Neural Networks (ANN)