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CS4001/4042: Neural Networks and Deep Learning
创建日期：2024-07-06 16:37:11
标签： CS4001
Neural Networks and Deep Learning

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Programming Assignment CS4001/4042:

Neural Networks and Deep Learning

Deadline: 18th March 2024

●    This assignment is to be done individually. You can discuss the questions with others but your submission must be your own unique work.

●    Data  files  and  other supporting  code for  both  parts are found  in the folder ‘Programming Assignment’ under ‘Assignments’ on NTULearn. You should use the  helper  codes  to  begin  and  provide  answers  to  the  assignment.  Please follow the formats given in helper codes to fill in your solutions. Those who do not   follow  the   specified  format  for  the   answers   risk   losing   marks   for presentation.

●    The assessment will be based on the correctness of the codes and solutions. Each part carries 45 marks, and 10 marks are assigned to the presentation and clarity of the solutions. The total number of marks is 100.

●    You  do   not  need  GPUs  for  this   assignment.   Local  PCs  will   be  sufficient. Attempt your assignments using Jupyter Lab / Notebook with version 3 and greater.

●    TAs  Mr.  Feng   Ruicheng  ([email protected]  ),  Ms.  Liang  Zhenxin ([email protected] ), and Mr. Liao Chang ([email protected]tu.edu.sg ) are in charge of this assignment.

●    For   any  inquiries,   please  either  contact  TAs   listed  above  or   post  in  the Discussion Board of NTULearn.

Submission procedure

●    Complete both parts A and B of the assignment and submit your solutions online via NTULearn before the deadline.

●    All submissions should be within the notebooks provided. Do not include data  nor model checkpoints in your submission. Submit 8 notebooks in this format:

o  For part A

.     <lastname>_<firstname>_Part_A_1.ipynb .     <lastname>_<firstname>_Part_A_2.ipynb .     <lastname>_<firstname>_Part_A_3.ipynb .     <lastname>_<firstname>_Part_A_4.ipynb .     common_utils.py

o  For Part B

.     <lastname>_<firstname>_Part  B  1.ipynb .     <lastname>_<firstname>_Part  B  2.ipynb .     <lastname>_<firstname>_Part  B  3.ipynb .     <lastname>_<firstname>_Part  B  4.ipynb

●    Late submissions will be penalized: 5% for each day up to three days.

Part A: Classification Problem

Part A of this assignment aims at  building  neural  networks to  perform.  polarity detection from voice recordings, based on data in the National Speech Corpus, which is obtained from https://www.imda.gov.sg/how-we-can-help/national-speech-corpus

The   National  Speech  Corpus  is  an  initiative   by  the   Info-Communications  and   Media Development Authority, and it is the first large scale Singapore English corpus. Within the dataset, there  are  6  parts.  In  the  fifth  segment,  speakers  are  made  to  communicate  in several  different  styles,  including  Positive  Emotions  and  Negative  Emotions.  The  original recordings are approximately 20 minutes long. Using the librosa library, the recordings are split  into  shorter  segments  and  preprocessed  to   features  such  as  chromagrams,  Mel spectrograms, MFCCs and various other features.

The preprocessed CSV file is provided in this assignment. We will be using the CSV file named simplified.csv, which is both provided to you. The features from the dataset are engineered. The aim is to determine the speech polarity of the engineered feature dataset. The csv file is called simplified.csv with a row of 77 features that you can use, together with the filename. The “filename” column has the labels associated with them.

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MOEC0021 STA 106 ENGG 1300A ENGG 1300 OLET1139 STAT 3503 STAT 206 FEM11149 ELEN 4720 COMP20003 SOLA1070 CS574 C11CS LINB29H3 MAST90083 ECON7200 STAT 610 LINB29 DSM-5 COMP3161 EEEN40010 MATH 4431 K353 STA 3386 IERG4300 COMS W4167 ENG5298 AMME 2302 CS3910 COMPSCI 320 STATS 369 GR5242 COSC 328 CSCI435 MGTS1301 INFS2200 COMP3900 CIS 375 Module AE03 COMP3121 CS260 HAM503 MGMT5050 INT3075 COSC 285 GEOL0029 GEGE2X01 STAT1201 MATH1061 MMF1941H STAT 5060 CS 520 COMP 4320 MSIN0104 000T FINS3648 ECMT2160 ECON4003 Phys 129L CSE 142 CS 3130 MGEB01 EECS 340 Finance (20443/20444) ECMM149 COMP5310 EEEN60180 CSC 111 AT2 CSCI 4430 STAB57 MA/CSC 427 MATH322 D100 CSCI544 CITS3004 CI6227 CSE 565 APCO 1P01 852L1 ELEC S411F COMP3207 STATS330 ECON7530 FIT5149 CSI4104 CMPT 371 COMP201 SEHS4696 FI4003 MATH6182 CG1 WS MATH401 MGT 205 QTM 110 COMP 0137 MACE12201 FIT5210 ECE 544 BISM7206 HUDM 6055 ECON3203 STATS 210 MSCI 570 MATH6173 COMPSCI4039 Accounting COM3503 AOE2024 ECON213 PSYC10004 Control Data 100/200A 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