Neural network

A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature.

Neural Network or Artificial Neural Network is one set of algorithms used in machine learning for modeling the data using graphs of Neurons.

Compared to regular machine learning approach (e.g., decision tree or linear regressoin), neural network has:

advantages: neural networks have the ability to learn on their own and produce results that are not limited by the data supplied to them (e.g., data extrapolation).
disadvantages: neural networks generally require far more data than traditional machine learning methods; for example, thousands if not millions of labeled examples are needed.

Note

Neural Networks and Deep Learning (by Charu C. Aggarwal)

Densely connected neural network

Introduction dnn.pdf.

Covolutional neural network

Introduction cnn.pdf.

Recurrent neural network

Simple RNN rnn_simple_rnn.pdf.
LSTM rnn_lstm.pdf.

Generative Adversarial Network

Introduction gan.pdf.

Graph Neural Network

Introduction gnn.pdf.