Python广泛应用于机器学习, 人工智能和统计数据分析等课程. 它也被很多大学作为入门语言来教授. 目前是我们代写最多的编程语言.
Operating Systems Homework 5 CS 4410 Due Tuesday, August 15 (before lecture) 1. IP Consider the following network: (a) For each of the routers (R1 through R4), write an appropriate routing table. (b) Suppose the MTU of the wireless link between hosts and R1 is 7981 bytes, and the MTU of all other links is […]
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Natural Language Engineering: Assessed Coursework Submission format: You should submit one file that should either be a Jupyter notebook or a zip file containing a Jupyter notebook and the image files that you want to include in the notebook. Due date: You work should be submitted on the module’s Study Direct site before 4pm on
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Assignment #4 CS 3357a Fall 2016 Due Dec 2nd, 2016 11:55pm Background In Assignment #3 we implemented the rdt 2.2 protocol from the course textbook which allowed us to use UDP for relaible data transfers. Unfortunately, rdt 2.2 only accounted for corrupt packets so we will need to build on our code from Assignment #3 and add a timer to account for lost
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CS440/ECE448 Fall 2016 Assignment 3: Naive Bayes Classification Due date: Monday, November 14, 11:59:59 PM The goal of this assignment is to implement Naive Bayes classifiers as described in this lecture and to apply it to the task of classifying visual patterns and text documents. As before, you can work in teams of up to three people
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Predict the outcome of EURO 2016 soccer matches for fun and profit Your task is to design machine learning models to predict the outcome of EURO 2016 soccer matches. Briefly explain how you choose the features and the models and how accurate is the model. The data is included in “data.csv” (we’ve also provided
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Introduction COSC1519 Introduction to Programming Semester 2 2016 – Assignment 1 This is a group assignment and worth 15% of your final grade. The grading is based on both demonstration and final code submission. Each group may consist of up to two students. You are recommended to form the assignment group with your study buddy.
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MLP Coursework 2 Due: 24 November 2016 Machine Learning Practical: Coursework 2 Release date: Wednesday 2nd November 2016 Due date: 16:00 Thursday 24th November 2016 Introduction The aim of this coursework is to use a selection of the techniques covered in the course so far to train accurate multi-layer networks for MNIST classification. It is
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University of Toronto, Department of Computer Science CSC 485/2501F—Computational Linguistics, Fall 2016 Assignment 3 Due date: 23:59, Wednesday 7 December 2016, at the course drop box in BA 2220. Late assignments will not be accepted without a valid medical certificate or other documen- tation of an emergency. This assignment is worth either 25% (CSC 2501)
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CS3335 Homework Assignment #3 Final Projects Overview For your assignment 3 you should select and implement one of the projects below. You will not be given any starter notebooks, but you are strongly encouraged to submit your results as a notebook containing your code and detailed explanation of your methodology and results. In case you
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CS3335 Homework Assignment #2 Geometric techniques: morphing, panoramas, model estimation Overview This homework has two (or three) parts covering basic geometric techniques in visual computing: warping, morphing, blending, and geometric model estimation. Generalization of these methods are routinely used in more advanced applications of computer vision. Thus, hands-on experience that you obtain while working on
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