合肥生活安徽新聞合肥交通合肥房產生活服務合肥教育合肥招聘合肥旅游文化藝術合肥美食合肥地圖合肥社保合肥醫院企業服務合肥法律      
        
  
        



        



        
    

        

        



        
  
        
    
        
  
    
        
      
      
    
        
          

        
    
        
  
        
    
        

 
        
    
        
      
    
        
   
        



        
  
        
    
        
      
         
            		
            
        代寫5614. C++ PROGRAMMING
        
              
        
            
         時間：2024-02-29  來源：合肥網hfw.cc  作者：hfw.cc 我要糾錯
        
            
        
           
        
             Assignment 1: Linear classifiers

        Due date: Thursday, February 15, 11:59:59 PM

         

        In this assignment you will implement simple linear classifiers and run them on two different datasets:

        1. Rice dataset: a simple categorical binary classification dataset. Please note that the

        labels in the dataset are 0/1, as opposed to -1/1 as in the lectures, so you may have to change either the labels or the derivations of parameter update rules accordingly.

        2. Fashion-MNIST: a multi-class image classification dataset

        The goal of this assignment is to help you understand the fundamentals of a few classic methods and become familiar with scientific computing tools in Python. You will also get experience in hyperparameter tuning and using proper train/validation/test data splits.

        Download the starting code here.

        You will implement the following classifiers (in their respective files):

        1. Logistic regression (logistic.py)

        2. Perceptron (perceptr on.py)

        3. SVM (svm.py)

        4. Softmax (softmax.py)

        For the logistic regression classifier, multi-class prediction is difficult, as it requires a one-vs-one or one-vs-rest classifier for every class. Therefore, you only need to use logistic regression on the Rice dataset.

        The top-level notebook (CS 444 Assignment-1.ipynb) will guide you through all of the steps.

        Setup instructions are below. The format of this assignment is inspired by the Stanford

        CS231n assignments, and we have borrowed some of their data loading and instructions in our assignment IPython notebook.

        None of the parts of this assignment require the use of a machine with a GPU. You may complete the assignment using your local machine or you may use Google Colaboratory.

        Environment Setup (Local)

        If you will be completing the assignment on a local machine then you will need a Python environment set up with the appropriate packages.

        We suggest that you use Anaconda to manage Python package dependencies

        (https://www.anaconda.com/download). This guide provides useful information on how to use Conda: https://conda.io/docs/user-guide/getting-started.html.

        Data Setup (Local)

        Once you have downloaded and opened the zip file, navigate to the fashion-mnist directory in assignment1 and execute the get_datasets script provided:

        $ cd assignment1/fashion-mnist/

        $ sh get_data.sh or $bash get_data.sh

        The Rice dataset is small enough that we've included it in the zip file.

        Data Setup (For Colaboratory)

        If you are using Google Colaboratory for this assignment, all of the Python packages you need will already be installed. The only thing you need to do is download the datasets and make them available to your account.

        Download the assignment zip file and follow the steps above to download Fashion-MNIST to your local machine. Next, you should make a folder in your Google Drive to holdall of   your assignment files and upload the entire assignment folder (including the datasets you downloaded) into this Google drive file.

        You will now need to open the assignment 1 IPython notebook file from your Google Drive folder in Colaboratory and run a few setup commands. You can find a detailed tutorial on   these steps here (no need to worry about setting up GPU for now). However, we have

        condensed all the important commands you need to run into an IPython notebook.

        IPython

        The assignment is given to you in the CS 444 Assignment-1.ipynb file. As mentioned, if you are   using Colaboratory, you can open the IPython notebook directly in Colaboratory. If you are using a local machine, ensure that IPython is installed (https://ipython.org/install.html). You may then navigate to the assignment directory in the terminal and start a local IPython server using the jupyter notebook command.

        Submission Instructions

        Submission of this assignment will involve three steps:

        1. If you are working in a pair, only one designated student should make the submission to Canvas and Kaggle. You should indicate your Team Name on Kaggle Leaderboard   and team members in the report.

        2. You must submit your output Kaggle CSV files from each model on the Fashion- MNIST dataset to their corresponding Kaggle competition webpages:

          Perceptron

          SVM

          Softmax

        The baseline accuracies you should approximately reach are listed as benchmarks on each respective Kaggle leaderboard.

        3. You must upload three files on Canvas:

        1. All of your code (Python files and ipynb file) in a single ZIP file. The filename should benetid_mp1_code.zip. Do NOT include datasets in your zip file.

        2. Your IPython notebook with output cells converted to PDF format. The filename should benetid_mp1_output.pdf.

        3. A brief report in PDF format using this template. The filename should be netid_mp1_report.pdf.

        Don'tforget to hit "Submit" after uploadingyour files,otherwise we will not receive your submission!

        Please refer to course policies on academic honesty, collaboration, late submission, etc.
        代寫 5614. C++ Programming-留學生作業幫 (daixie7.com)

        請加QQ：99515681  郵箱：99515681@qq.com   WX：codehelp 			
        

        

            
        






        



        

        掃一掃在手機打開當前頁
        




            
            
      1. 上一篇:代寫CS444  Linear classifiers
        2. 
            
      2. 下一篇:莆田鞋官方正品入口，這十個官方入口必須收藏
        3.  

            無相關信息            
        

            
        
            
        
              
        合肥生活資訊
        
              
        
                
        
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
      
      
  
    
        
              
        
            
        
            
        
              
        合肥圖文信息
        
              
        
                
        
                  
      
        

        
  
        
    
        
  挖掘機濾芯提升發動機性能
  
        挖掘機濾芯提升發動機性能
        

        		
    
        
  戴納斯帝壁掛爐全國售后服務電話24小時官網400(全國服務熱線)
  
        戴納斯帝壁掛爐全國售后服務電話24小時官網
        

        		
  
        

        
      
        

      
        

        
  
        
    
        
  菲斯曼壁掛爐全國統一400售后維修服務電話24小時服務熱線
  
        菲斯曼壁掛爐全國統一400售后維修服務電話2
        

        		
    
        
  美的熱水器售后服務技術咨詢電話全國24小時客服熱線
  
        美的熱水器售后服務技術咨詢電話全國24小時
        

        		
  
        

        
      
        

      
        

        
  
        
    
        
  海信羅馬假日洗衣機亮相AWE  復古美學與現代科技完美結合
  
        海信羅馬假日洗衣機亮相AWE  復古美學與現代
        

        		
    
        
  合肥機場巴士4號線
  
        合肥機場巴士4號線
        

        		
  
        

        
      
        

      
        

        
  
        
    
        
  合肥機場巴士3號線
  
        合肥機場巴士3號線
        

        		
    
        
  合肥機場巴士2號線
  
        合肥機場巴士2號線
        

        		
  
        

        
      
        
                
        
              
        
            
        
          
      
      
        
      
    
        
  
        

        


        

        
    
        
      
      3.  

        
          
          



        

 
        

        
          
 
        
        
        
       
        4. 

        

        


        
    
        

        
  
        
    
    
  
        

        
    
        
    
        

        
  
        
    
    
  
        

        
	
        

        

        
 
        


        

        

        
 
        


        

        


        


        幣安app官網下載 

短信驗證碼 
丁香花影院 

        
 
        
  
        關于我們 | 打賞支持  | 廣告服務 | 聯系我們 | 網站地圖 | 免責聲明 | 幫助中心 | 友情鏈接 | 
    
    
        

        

        
  
        Copyright © 2024 hfw.cc Inc. All Rights Reserved. 合肥網 版權所有 
        
    ICP備06013414號-3 公安備 42010502001045
        

         


        
    

        

 
 
 







主站蜘蛛池模板：
亚洲国产欧美一区二区三区
|
一区二区三区在线视频播放|
无码精品人妻一区|
国产成人精品一区二区三区无码|
亚洲大尺度无码无码专线一区
|
中文字幕日韩欧美一区二区三区|
日韩精品一区二区三区大桥未久
|
国产微拍精品一区二区|
亚洲国产精品乱码一区二区|
国产一区二区精品久久岳|
日本一区二区三区精品视频|
无码人妻精品一区二区三区9厂
|
www.亚洲一区|
国精产品999一区二区三区有限|
中文字幕一区视频|
精品人妻一区二区三区浪潮在线
|
日本一区二区三区不卡在线视频|
日韩免费一区二区三区在线|
国产精品亚洲一区二区麻豆|
亚洲高清日韩精品第一区|
亚洲一区二区三区在线|
国产在线一区二区三区|
性色av一区二区三区夜夜嗨|
少妇无码一区二区三区|
无码成人一区二区|
亚洲愉拍一区二区三区|
国产日产久久高清欧美一区|
国产91精品一区|
果冻传媒一区二区天美传媒|
日韩人妻无码一区二区三区久久99|
熟女少妇丰满一区二区|
91精品国产一区二区三区左线
|
国产一区二区在线观看视频|
无码AV一区二区三区无码|
中文人妻无码一区二区三区|
精品国产一区二区三区不卡|
无码夜色一区二区三区|
精品一区二区三区在线播放
|
亚洲国产AV一区二区三区四区
|
国产乱人伦精品一区二区在线观看|
亚洲男女一区二区三区|