The Complete Guide to Artificial Neural Networks: Concepts and Models

website:The Complete Guide to Artificial Neural Networks: Concepts and Models

This article will provide a brief introduction to all the important concepts of Artificial Neural Network and explain how to use deep learning framework like TensorFlow and PyTorch to build deep learning architecture.

 

A shallow neural network has only three layers of neurons:

n   An input layer: accept the independent variables or inputs of the model

n   One hidden layer

n   An output layer: generate predictions

 

A deep neural network is more accurate and improve in accuracy as more neuron layers are added. Additional layers are useful up to limit of 9-10, after which their predictive power starts to decline. Nowadays most neural network models and implementations use a deep network of between 3-10 neuron layers.

Here is glossary of basic terms we should know

u  Inputs: source data fed into the neural network 

u  Training set: a set of inputs for which the correct outputs are known

u  Outputs: neural network generate the prediction in the form of a set of real values. Each output value is generated by one of the neurons in output layer 

u  Neuron: the basic unit of the neural network

u  Weight space: each neuron is given a numeric weight

u  Forward pass: take the inputs, passes them through the network and allow each neuron to react to a fraction of the input

u  Error function: define how far the actual output of the current model is from the correct output

u  Backpropagation: to discover the optimal weight for neurons, we use backward pass which means moving back from the network’s prediction to the neurons that generated prediction.

u  Bias and variance: bias measure how well the model fits the training set. Variance measures how well the model works with unknown inputs that were not available during train.

u  Hyperparameters: is a setting that affects the structure or operation of neural network

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