Objective: The goal of this assignment is to gain hands-on experience in designing and implementing a deep neural network using the Keras library in Python. You will select a dataset of your choice from kaggle and apply a deep learning model to it. The focus is on experimenting with different activation functions and optimization techniques to understand their impact on the model’s performance.
Requirements:
Dataset Selection:Choose a dataset that interests you. It can be related to image classification, text analysis, or any other domain.
Perform necessary data preprocessing steps, such as normalization, encoding categorical variables, splitting into training and testing sets, etc.
Model Design:Design a deep neural network using Keras.
Your network should have at least 3 hidden layers.
Activation Functions:Experiment with three different activation functions: Sigmoid, Tanh, and ReLU.
Implement these activation functions in different models (or layers) to observe their effects on your network’s performance.
Optimization Technique:Use either Gradient Descent or Stochastic Gradient Descent (SGD) as your optimization technique.
Discuss how your chosen optimizer influences the training process and outcomes.
Evaluation:Evaluate the performance of your models using appropriate metrics (such as accuracy.).
Compare the results obtained with different activation functions and discuss your findings.
Report:Write a report documenting your data preprocessing steps, model architecture, training process, results, and analysis.
Include visualizations such as loss and accuracy curves, confusion matrices, etc., where applicable.
Provide a link to the dataset.
Submission:
Submit your Jupyter Notebook containing the code, along with the report. Submit a ipynb file.
Category: Artificial Intelligence
Objective: The goal of this assignment is to gain hands-on experience in designi
Objective: The goal of this assignment is to gain hands-on experience in designing and implementing a deep neural network using the Keras library in Python. You will select a dataset of your choice from kaggle and apply a deep learning model to it. The focus is on experimenting with different activation functions and optimization techniques to understand their impact on the model’s performance.
Requirements:
Dataset Selection:Choose a dataset that interests you. It can be related to image classification, text analysis, or any other domain.
Perform necessary data preprocessing steps, such as normalization, encoding categorical variables, splitting into training and testing sets, etc.
Model Design:Design a deep neural network using Keras.
Your network should have at least 3 hidden layers.
Activation Functions:Experiment with three different activation functions: Sigmoid, Tanh, and ReLU.
Implement these activation functions in different models (or layers) to observe their effects on your network’s performance.
Optimization Technique:Use either Gradient Descent or Stochastic Gradient Descent (SGD) as your optimization technique.
Discuss how your chosen optimizer influences the training process and outcomes.
Evaluation:Evaluate the performance of your models using appropriate metrics (such as accuracy.).
Compare the results obtained with different activation functions and discuss your findings.
Report:Write a report documenting your data preprocessing steps, model architecture, training process, results, and analysis.
Include visualizations such as loss and accuracy curves, confusion matrices, etc., where applicable.
Provide a link to the dataset.
Submission:
Submit your Jupyter Notebook containing the code, along with the report. Submit a ipynb file.
I need to have a complete project, but it will be developed in three parts (1, 2
I need to have a complete project, but it will be developed in three parts (1, 2 and the 3- final complete report). Please save each part in a separate file.There should be three files i.e.part 1, part 2 and part 3- Completion of Final Project Report.
Each part has instructions for it already, but I am looking for a part of the complete project written and not an outline.
Each part should be 3-4 pages in length not including the title page and reference list.
So, when later combining each part, the complete project should be 6-8 pages again not including the title page and reference list.
below I will attach my paper and feedback from my instructor please review the f
below I will attach my paper and feedback from my instructor please review the feedback and make the paper sufficient according to the feedback provided please
Assignment Requirements APA Format Please note that the word limit for the assig
Assignment Requirements
APA Format
Please note that the word limit for the assignment is 600 words.
Assignment topic: Ethical Implications of AI Technology
Provide a description and justification of the research design used. The research design can be described at two levels:
a. Descriptive/Predictive/Prescriptive
b. Experimental/Quasi-experimental/Non-experimental
Elaborate on the choice/design of the measurement scale. (Explain all the points with appropriate justifications.)
Create or replicate a questionnaire using an online survey tool (Qualtrics/Google Forms/Survey Monkey). Download the questionnaire in word format and attach it with the submission.
Generate the data for the pilot study. Download the Qualtrics data as an Excel file. Share the PDF version.
Components (Marks)Meets ExpectationsDoes Not Meet ExpectationsCreating or Replicating a Questionnaire for a Pilot Study (15)
Contains all elements:
Description of the research design (1)
Components of the questionnaire and their alignment with the constructs of the study are described with proper justification (2)
Measurement scale is explained with proper justification (2)
Questionnaire is created or replicated in accordance with the pilot study (5)
Adheres to academic writing style (Grammar rules, clarity of sentences and American English) (3)
Questionnaire is submitted in a properly-formatted MS Word file (2)
Generating Data from the Questionnaire (5)
The data is generated for at least 50 – 100 respondents (2)
Qualtrics data is generated and downloaded in Microsoft Excel format. (1)
The context of the dataset is clearly mentioned. Note on the different constructs and variables of the dataset is added. The column headers in the generated data, or any codes used to describe data are clearly explained (2)
Assignment Requirements APA Format Please note that the word limit for the assig
Assignment Requirements
APA Format
Please note that the word limit for the assignment is 600 words.
Assignment topic: Ethical Implications of AI Technology
Provide a description and justification of the research design used. The research design can be described at two levels:
a. Descriptive/Predictive/Prescriptive
b. Experimental/Quasi-experimental/Non-experimental
Elaborate on the choice/design of the measurement scale. (Explain all the points with appropriate justifications.)
Create or replicate a questionnaire using an online survey tool (Qualtrics/Google Forms/Survey Monkey). Download the questionnaire in word format and attach it with the submission.
Generate the data for the pilot study. Download the Qualtrics data as an Excel file. Share the PDF version.
Components (Marks)Meets ExpectationsDoes Not Meet ExpectationsCreating or Replicating a Questionnaire for a Pilot Study (15)
Contains all elements:
Description of the research design (1)
Components of the questionnaire and their alignment with the constructs of the study are described with proper justification (2)
Measurement scale is explained with proper justification (2)
Questionnaire is created or replicated in accordance with the pilot study (5)
Adheres to academic writing style (Grammar rules, clarity of sentences and American English) (3)
Questionnaire is submitted in a properly-formatted MS Word file (2)
Generating Data from the Questionnaire (5)
The data is generated for at least 50 – 100 respondents (2)
Qualtrics data is generated and downloaded in Microsoft Excel format. (1)
The context of the dataset is clearly mentioned. Note on the different constructs and variables of the dataset is added. The column headers in the generated data, or any codes used to describe data are clearly explained (2)
The work should be your own, copying from AI or other resources will result in Z
The work should be your own, copying from AI or other resources will result in ZERO mark. 😎
Why is it important to match the data to the
needs of a specific predictive algorithm or software tool?
Suppose a teacher asks you to design a dashboard to
monitor the students’ grades for the following courses IT-351: Computer
Networks,IT-353: System Analysis and Design , IT-475: Decision Support Systems,
And IT-479: Senior Project I . Design a dashboard by identifying the basic
elements (at least 7 KPIs ) that should be displayed on the dashboard using any
drawing programming to give an idea of how the dashboard should look like. You
should use some graphs and analysis on the dashboard..
What are the components of Decision Support Mathematical
Models? Choose any area and give examples.
AI technologies affect our lives in different
domains such as education, healthcare, business, etc. Choose any one domain and
discuss one AI application that has been used in that domain. You should be
specific about how the technology has been implemented and which AI method has
been used?
The work should be your own, copying from students or other resources will resul
The work should be your own, copying from students or other resources will result in ZERO mark. 😎
Why is it important to match the data to the
needs of a specific predictive algorithm or software tool?
Suppose a teacher asks you to design a dashboard to
monitor the students’ grades for the following courses IT-351: Computer
Networks,IT-353: System Analysis and Design , IT-475: Decision Support Systems,
And IT-479: Senior Project I . Design a dashboard by identifying the basic
elements (at least 7 KPIs ) that should be displayed on the dashboard using any
drawing programming to give an idea of how the dashboard should look like. You
should use some graphs and analysis on the dashboard..
What are the components of Decision Support Mathematical
Models? Choose any area and give examples.
AI technologies affect our lives in different
domains such as education, healthcare, business, etc. Choose any one domain and
discuss one AI application that has been used in that domain. You should be
specific about how the technology has been implemented and which AI method has
been used?
10 slides, with examples and screenshots of service. You need to choose 5 top se
10 slides, with examples and screenshots of service.
You need to choose 5 top services and describe their advantages
Your research paper should be structured as follows: Introduction:Introduction t
Your research paper should be structured as follows:
Introduction:Introduction to the problem domain.
Significance of the problem.
Brief description of the dataset.
Literature Review:A review of existing methods that have been used in the problem domain.
Any existing challenges or gaps in the current methodologies.
Methods:Detailed description of the AI/data science techniques you applied.
Reasons for selecting these methods.
Any preprocessing steps undertaken.
Description of the dataset (features, number of samples, any specific characteristics).
Results:Present the results obtained using your methods.
Use appropriate visualizations like graphs, charts, etc.
Provide screenshots of relevant sections of code and output.
Discussion:Interpretation of the results.
How do your results compare with existing methods (if any comparisons were made)?
Any challenges faced and how they were overcome.
Limitations of your study.
Conclusion:Summary of your findings.
Potential implications of your work.
Suggestions for future work in this domain.
References:Ensure all sources, including datasets and code libraries, are appropriately cited.
Use peer-reviewed sources wherever possible.
Submission Format:
The paper should be a minimum of 5 pages and a maximum of 15 pages, including references.
Use 12-point Times New Roman font, double-spaced.
Ensure all figures, tables, and code snippets are appropriately labeled and referenced in the text.