First Name: __________________________ Last Name: ___________________________ EECS 649 Introduction to Artificial Intelligence Exam Electronic Blackboard Submission Due: April 24, 2019 @ 9PM Paper Copy Due: April 25, 2019 @ 4PM 200 Points Directions: You must read and follow these directions carefully. This is a 3 hour open notes, open internet exam. You may not collaborate […]
Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market Rosdyana Mangir Irawan Kusuma1, Trang-Thi Ho2, Wei-Chun Kao3, Yu-Yen Ou1 and Kai-Lung Hua2 1Department of Computer Science and Engineering, Yuan Ze University, Taiwan Roc 2Department of Computer Science and Engineering, National Taiwan University of Science and Technology, Taiwan Roc 3Omniscient Cloud Technology
Faculty of Engineering and Information Technology School of Software 42028: Deep Learning and Convolutional Neural Networks Autumn 2019 ASSIGNMENT-1 SPECIFICATION Due date Friday 11:59pm, 19 April 2019 (Extended!) Demonstrations Marks Submission Optional, If required. 30% of the total marks for this subject 1. AreportinPDForMSWorddocument(5-pagesmax) 2. GoogleColab/iPythonnotebooks Submit to Note: This assignment is individual work. UTS
Call for Papers for the 2ND ANU ANNUAL BIO-INSPIRED COMPUTING STUDENT CONFERENCE also being used for COMP4660/8420 Assignment 1: Neural Networks Submission Due: Sunday 5th May (Week 8) at 11:55pm Context Neural networks research prior to the deep learning boom has a lot to teach us still. The neural networks part of the course focuses
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Call for Papers for the 2ND ANU ANNUAL BIO-INSPIRED COMPUTING STUDENT CONFERENCE also being used for COMP4660/8420 Assignment 1: Neural Networks Context Neural networks research prior to the deep learning boom has a lot to teach us still. The neural networks part of the course focuses on the research in this area from 1986 (back-
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Assignment #2 Fully-Connected Nets, Batch Normalization, Dropout, Convolutional Nets In this assignment you will practice writing backpropagation code, and training Neural Networks and Convolutional Neural Networks. The goals of this assignment are as follows: • understand Neural Networks and how they are arranged in layered architectures • understand and be able to implement (vectorized) backpropagation • implement various update
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Sentiment Analysis Your task is to develop a deep learning model that takes each sentence from movie reviews and classifies it into one of the 5 sentiments: 0 – very negative, 1 – negative, 2 – neutral, 3 – positive, 4 – very positive. Task 1 Copy and paste the hw2/ directory under the cs571
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The Final Project of “introduction to Statistical Learning and Machine Learning” Yanwei Fu January 19, 2019 Abstract (1) This is the final project of our course. The project is released on Dec 25th, 2018. The deadline is 5:00pm, Feb. 7th, 2018. Please send the report to sunqiang85@gmail.com. The late submission is also acceptable; however, you
Representational Dimensions CMPUT 366: Intelligent Systems P&M Chapter 1 Lecture Outline 1. Recap 2. Agents 3. Representations 4. Dimensions of representation Recap: Course Essentials Course webpage: jrwright.info/aicourse/ This is the main source for information about the class Slides, readings, assignments, deadlines Contacting us: Discussion board: piazza.com/ualberta.ca/winter2019/cmput366 for public questions about assignments, lecture material,
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Project 2 : Quality text prediction As seen during class, a lot of systems exists to produce automatic summarization. Lately, deep learning was involved to this task in order to improve results. This new systems generates abstract summaries, which means that new unseen sentences in source documents can be generated. In this case a problem
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