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SIT744 Assignment 2: Deep Learning Project
创建日期：2024-06-22 14:58:08
标签： SIT744
Deep Learning Project

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SIT744 Assignment 2: Deep Learning Project


This is an individual assignment. It contributes 40% to your final mark. Read the assignment instruction
carefully.

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

Warning

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.

Marking criteria

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.

Assignment objective

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. 


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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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