Deep learning¶ Our toy example will be to classify species as endangered or not based genomic data. The rationale is that species with a small (effective population size) will have higher chances to be threatened. The amount of genomic variability (e.g. polymorphic sites and haplotype diversity) is taken as a proxy for the (effective) population […]
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Deep learning¶ Our toy example will be to classify species as endangered or not based genomic data. The rationale is that species with a small (effective population size) will have higher chances to be threatened. The amount of genomic variability (e.g. polymorphic sites and haplotype diversity) is taken as a proxy for the (effective) population
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Assignment 3 In this assignment we will visually analyze the outputs of a deep neural network, namely a model for generating images. A generative adversarial network has been trained to learn the distribution of a dataset of images, and you will visualize the outputs of the generator to help understand different aspects of the network.
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Explore Demo Fork Sign in 👋 Welcome. This is live code! Click the left margin to view or edit.  ava-yy Published Mar 10 7 Files Assignment 3 In this assignment we will visually analyze the outputs of a deep neural network, namely a model for generating images. A generative adversarial network has been trained
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COURSE PROJECT, EL2320 APPLIED ESTIMATION 1 Kalman Filter and Particle Filter for Real time Object Tracking Lucas Rode ́s Guirao (partner Diego Mor ́ın) lucasrg@kth.se Abstract—The objective of this project is to compare the performance of the Kalman Filter and the Particle Filter for real time object tracking. In addition, we explore possible combinations of
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ESE545-Project2 February 25, 2020 Due: March 22, 2020 at 11:59pm • You are required to solve the problems using Python. You are allowed to use only python standard libraries, numpy, pandas, scipy and Pytorch. • You are allowed to work in groups up to 3 members. You should submit your code with a brief report
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COMP5623 Coursework on Image Classification with Convolutional Neural Networks – ImageNet10 Deadline 10am 2nd March 2020. Submission Submit work electronically via Minerva. This coursework is worth 20% of the credit on the module. Time allocation An indicative duration for this coursework is a total of around 17 hours. Motivation Through this coursework, you will: •
Fundamentals of Computer Vision, Spring 2020 Project Assignment 1 Image Filtering and Hough Transform Due Date: Fri February 28, 2020 11:59PM In this assignment you will be implementing some basic image processing algorithms and putting them together to build a Hough Transform based line detector. Your code will be able to find the start and
pp SS4850G/9850B Assignment #1 (Due Feb 14 2020 before 13:30) • Show all your works in details and provide your code for computations. • In Questions 4 to 6, use the “Biweight” kernel. • Write down your name, student id, and you are undergraduate/graduate stu- dents. • Please submit your hard copy in class. 1.
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Article Gene Selection in Cancer Classification Using Sparse Logistic Regression with L1/2 Regularization Shengbing Wu *, Hongkun Jiang, Haiwei Shen and Ziyi Yang Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China; jiang.hk.xm@gmail.com (H.J.); yykjthaiwei@buu.edu.cn (H.S.); yangziyi091100@163.com (Z.Y.) * Correspondence: shengbing.wu@163.com Received: 8 August 2018; Accepted: 4 September 2018; Published: 6
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