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COMP222 Train Deep Learning Agents

创建日期：2024-08-01 16:17:39

所属分类：计算机computer science

标签： COMP222

Train Deep Learning Agents

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COMP222 Second CA Assignment

Individual coursework

Train Deep Learning Agents

Assessment Information

Assignment Number 2 (of 2)

Weighting 10%

Submission Mode Electronic

Learning outcome assessed 3. Ability to explain how deep neural networks are constructed

and trained, and apply deep neural networks

to work with large scale datasets

4. Understand reinforcement learning, and is able to

develop deep reinforcement learning algorithms for suitable

applications

Purpose of assessment To design and implement deep learning agents for either

image classification task or reinforcement learning task.

Marking criteria The marking scheme can be found in Section 3.2

Submission necessary in order No

to satisfy Module requirements?

Late Submission Penalty Standard UoL Policy.

1 Objective

This assignment requires you to implement one of the following tasks:

two image classifiers with convolutional neural networks,

a deep reinforcement learning model for a video game from OpenAI Universe.

Considering their di?erent diculties

and the fact that the second needs at least an implementation

of the first, we enable the possibility of getting 140% marks for the completion of

the second.

In the following, the Requirement and Description and the Marking Criteria for the two

tasks are explained separately.

2 CNN-based Image Classification

2.1 Requirement and Description

Language and Platform Python (version 3.5 or above) and Tensorflow (newest version).

You may use any libraries available on Python platform, such as numpy, scikit-learn, panda,

etc.

Dataset You can use any dataset which is convenient for you. Unless exceptional circumstance,

it is recommended that the dataset is not too small (e.g., no less than 10,000

items) and not too big (e.g., no more than 100,000 items). The images in the dataset are

not too large, as that will cost you too much time on training a good model.

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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 EMESTER1 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