# A Beginners Guide to Artificial Neural Network using Tensor Flow & Keras

A Beginners Guide to Artificial Neural Network using Tensor Flow & Keras. Building a fraud detection model using Artificial Neural Network & fine-tuning Hyperparameters using RandomizedSearchCV

## Introduction

ANNs (Artificial Neural Network) is at the very core of Deep Learning an advanced version of Machine Learning techniques. Artificial Neural Networks involve the following concepts. The input & the output layer, the hidden layers, neurons under hidden layers, forward propagation, and backward propagation. In a nutshell, the input layer is the set of independent variables, the output layer represents the final output (the dependent variable), the hidden layers consist of neurons where equations are developed and activation functions are applied. The forward propagation talks about how equations are developed to achieve the final output, whereas the backward propagation calculates the gradient descent to updates the learning rates accordingly. More about the operational process can be found in the article below.

## Deep Neural Network

When an ANN contains a deep stack of hidden layers, it is called a deep neural network (DNN). A DNN works with multiple weights and bias terms, each of which needs to be trained. In just two passes through the network, the algorithm can compute the Gradient Descent automatically. In other words, it can identify how each weight and each bias term across all the neurons should be tweaked to reduce the error. The process repeats unless the network converges to a minimum error.

Let’s run through the algorithm step by step:

• Develop training and test data to train and validate the model output. Since it follows a parametric structure in which it optimizes the weight and bias parameter terms. All statistical assumptions involving correlation, outlier treatment remains valid and has to be treated
• The input layer consists of the independent variables and their respective values. One mini-batch of data (depending on the batch size) passes through the full training set, multiple times. Each pass is called an epoch. The higher the epoch, the higher is the training time
• Each mini-batch is passed to the input layer, which sends it to the first hidden layer. The output of all the neurons in this layer (for every mini-batch) is computed. The result is passed on to the next layer, and the process repeats until we get the output of the last layer, the output layer. This is the forward pass: it is like making predictions, except all intermediate results are preserved since they are needed for the backward pass
• The network’s output error is then measured using a loss function that compares the desired output to the actual output of the network
• The scientific contribution of every neutron to the error terms are calculated
• The algorithm performs a Gradient Descent to tweak weights and parameters based on the learning rate (the backward propagation) and the process repeats itself

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