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Introduction
👩🏫 Concepts of neural network with theoric details
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.
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 |
- 🚧 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
Last modified 2yr ago