Neural Network-from-scratch

A neural network is a network or circuit of neurons, or in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus a neural network is either a biological neural network, made up of real biological neurons, or an artificial neural network, for solving artificial intelligence (AI) problems. The connections of the biological neuron are modeled as weights. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred as a linear combination.

These artificial networks may be used for predictive modeling, adaptive control and applications where they can be trained via a dataset. Self-learning resulting from experience can occur within networks, which can derive conclusions from a complex and seemingly unrelated set of information

Types of Artificial Neural Network

1. Feedforward Neural Network – Artificial Neuron
2. Radial Basis Function Neural Network
3. Multilayer Perceptron
4. Convolutional Neural Network
5. Recurrent Neural Network(RNN) – Long Short Term Memory
6. Modular Neural Network
7. Sequence-To-Sequence Models