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Introduction to Artificial Neural Network (PRE40)

  • 12-16 June 20235 daysVienna, AustriaCourse Fee: 3550 EUR + VAT
    Computer Fee: 250 EUR + VAT


This course introduces the basic concepts of Artificial Neural Network, its applications and techniques for developing models to predict outputs for any specified input dataset. The basic principles shall be demonstrated in a very lucid manner on an excel worksheet. Moreover, it is not essential for participants to have any prior knowledge regarding the subject.

Course Level: Foundation
Instructor: Mohit Narain

Designed for you, if you are...

  • A professional handling data in any field such as engineering, sciences, management, finance etc.

How we build your confidence

  • Highly interactive course
  • Practical oil & gas case examples will be presented and adequate exposure will be provided to the participants through exercises
  • Participants will be able to hone their skills on several input datasets

The benefits from attending

By the end of the course you will feel confident in your understanding of:

  • The fundamental structure of the artificial neural network model
  • The concept of backpropagation technique
  • The significance of activation functions
  • Using Excel worksheets for minimizing cost functions
  • Developing basic feedforward neural network models on Excel worksheets
  • Managing raw data more effectively


  • Applications of the neural model
  • Similarities to biological neurons
  • Concept and structure of Artificial Neural Network
  • Managing the data for a neural network
  • Linear and non-linear separability
  • Commonly used activation functions
  • Effect of parameters on the activation functions
  • Utilising activation functions for mapping outputs - forward pass for a small data set
  • Determining the cost function on a small data set based on activation functions
  • Procedure for backpropagation to develop a new set of weights
  • The local & global Minima-Avoiding the local minima using the momentum term
  • Minimizing the cost function using the Excel worksheet and finalizing the weights for the model
  • Concept of training, validation and test sets
  • Comparing the cost functions for the validation and test datasets based on the weights obtained for the training dataset
  • Effect of increasing the nodes in the hidden layer on minimizing the cost function
  • Adding a hidden layer to the model and its impact on minimizing the cost function
  • Estimating the cost function for the validation and test datasets based on the model obtained from training dataset
  • The concept of Multiple Input Multiple Output (MIMO) and Multiple Input Single Output (MISO) models
  • Analysis of weights for MIMO & MISO models using different activation functions
  • Comparing minimized cost functions for validation and test datasets for the MIMO model & both MISO models
  • Developing the model for compressor liquid and gas data example with two different activation functions using two inputs - a single hidden layer and two outputs
  • Compare the effect of adding a hidden layer on the training, validation, test data in above exercise
  • Review the graphical correlation between the true and model predicted values on the test data



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