Description

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
• Every participant will be able to hone his skills on several input datasets.

The benefits from attending

• Manage raw data more effectively.
• Develop basic feedforward neural network models on excel worksheets.
• Participate in discussions related to applications of neural modelling.
• Expose other participants to new technology for further development.
• Attend advanced courses on the subject.

• Fundamental Structure of the Artificial neural network model.
• The concept of backpropagation technique.
• The significance of Activation functions.
• Use of the excel worksheet for minimizing cost functions.

Topics

• 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