This is an individual assignment. It contributes 40% to your final mark. Read the assignment instruction
What to submit
This assignment is to be completed individually and submitted to CloudDeakin. By the due date, you are
required to submit the following files to the corresponding Assignment (Dropbox) in CloudDeakin:
1. [YourlD]_ _assignment2_ solution.ipynb: This is your Python notebook solution source file.
2. [YourlD]_ assingment2_ _output.html or [YourlD]_ _assingment2_ output.pdf: This is the output of your
Python notebook solution exported in HTML or PDF format.
3. Extra files needed to complete your assignment, if any (e.g.. images used in your answers).
For example, if your student ID is: 123456, you will then need to submit the following files:
●123456_ assignment2_ _solution.ipynb
●123456_ _assignment2_ _output.html
Some components of this assignment may involve heavy computation that runs for a long duration. Please start
early to avoid missing the assignment due date.
Your submission will be assessed based on the overall impact of your effort. A useful model (or application)
should be your focus. But as in Assignment 1, we will also consider the following criteria at the same time.
●Showing good effort through completed tasks.
●Applying deep learning theory to design suitable deep learning solutions for the tasks.
●Critically evaluating and reflecting on the pros and cons of various design decisions.
●Demonstrating creativity and resourcefulness in providing unique individual solutions.
●Showing attention to detail through a good quality assignment report.
This assignment is to feedback on your learning in deep learning theory and its application to data analytics or
artificial intelligence problems.
It builds on Assignment 1 but requires a higher level of mastery of deep learning theory and
programming/engineering skills. In particular, you will practice making design decisions yourself. You are also
likely to encounter practical issues that will help you consolidate textbook learning.
Task 1 (P Task) Smart Recycling using Deep Learning
In Assignment 1, you tackled the image classification problem in Fashion-MNIST. There, you used a Densely
Connected Neural Network. In Assignment 2, you will apply the best practices of deep-learning computer vision
to make something useful for our planet- -waste classification.
Background Every day, we put things into our recycle bin, to reduce landfill waste. However, we may
unintentionally contribute to recycling. contamination (https://www.cleanaway.com.au/sustainable-
future/contamination-main) by “wish recycling” the wrong items. As every city council has slightly different rules
for recycling, you will build a technological solution to ensure you only recycle things that are permitted by your
local council. More discussions about recycling contamination can be found here