Build a deep learning model for image analytics

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Reference no: EM133764041

Artificial Intelligence and Machine Learning

Learning Outcome 1: Investigate deep learning.

Learning Outcome 2: Build a Deep Learning model for Image Analytics using the Orange Data Mining App.

Learning Outcome 3 Analyse case studies in deep learning.

Activity - Evaluating Neural Network Models

Your Task
Evaluate the predictive modelling capability of neural networks and other prediction models in the Orange Data Mining Application. The assessment will involve implementing predictive models using software and solving various machine learning problems encountered in business. The assessment is worth 30 marks (see rubric for allocation of these marks).

Assessment Description
There has been a recent advent of Neural Networks including applications in deep learning. Analytics professionals can run basic Deep Learning applications via the browser and no-code platforms as the algorithms use hardware accessed via the cloud to provide the required performance.
In this assessment, you will implement an image classification model using Orange Data Mining's image analytics toolbox. You will be provided with a dataset containing images.

Assessment Instructions
In class: Implementation. Use the Orange data mining software to perform both deep learning- based image classification and classical predictive analytics tasks.

Image Classification -Construct a predictive model using the Image Analytics widgets in Orange.
Analyse tabular data using Neural Networks (NN)- Construct a predictive model using NN and other widgets in Orange.
In class: Problem Solving (Puzzle). Based on the Orange output, solve the accompanying puzzles in the assessment sheet designed to test your understanding of various prediction models and general knowledge on machine learning.

Deliverables: By the end of the workshop, you must submit your:
Orange workflow file (.ows), via the file submission dropbox
Your answer sheet containing answers to the puzzle.

Reference no: EM133764041

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