π±Introduction
π©βπ« Concepts of neural network with theoric details
π 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.
π 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 |
πΌ Standard NN | We input some features and estimate the output | Online Advertising, Real Estate |
π¨ CNN | We add | Photo Tagging |
π RNN | Suitable for sequence data | Machine Translation, Speech Recognition |
π€¨ Custom NN / Hybrid | For complex problems | Autonomous Driving |
π¨ Types of Data in Supervised Learning
π§ Structured Data
Such as tables
We have input fields and an output field
π€ΉββοΈ Unstructured Data
Such as images, audio and texts
We need to use feature extraction algorithms to build our model
π§ References
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