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What are Artificial Neural Networks

Backpropagation and Forwardpropagation

Structure of a Neural Network (Neuron)

Input feature Weight Vector, Sum Function, Activation Function, and Bias in the network

Defining an activation function and understanding different types of activation function

Back Propagation NN is the multilayered feedforward NN

Defining a function for Initializing the network

Calculating the neuron activation for an input

Defining the Transfer function for neuron activation

Defining function for forwarding propagate input to a network output

Testing the forward propagation

Calculating the derivative of a neuron output

Backpropagating the error and storing it in neurons

Updating the network weights with calculated error

Training the network with some iterations

Importing the final dataset for testing created algorithm

Preprocessing the dataset and scaling it for better results

Feeding the dataset into Artificial Neural Networks and calculating the results

Applying cross-validation to prevent overfitting

Making the final predictions and calculating the accuracy score

From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients. Because the real probability of default is unknown, so in this machine learning project we present the novel Sorting Smoothing Method to estimate the real probability of default.

With the real probability of default as the response variable (Y), and the predictive probability of default as the independent variable (X), the simple linear regression result (Y = A + BX) shows that the forecasting model produced by artificial neural network has the highest coefficient of determination; its regression intercept (A) is close to zero, and regression coefficient (B) to one. Therefore, among the six data mining techniques, artificial neural network is the only one that can accurately estimate the real probability of default.

In this data science project, we will predict internal failures of Bosch using thousands of measurements and tests made for each component along the assembly line.

In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.

In this deep learning project, you will build a classification system where to precisely identify human fitness activities.

4-Dec-2016

04h 44m