{"id":18632518,"url":"https://github.com/denrydu/cs231n_assignment1","last_synced_at":"2025-11-04T07:30:26.987Z","repository":{"id":37222368,"uuid":"273237667","full_name":"DenryDu/cs231n_Assignment1","owner":"DenryDu","description":"斯坦福大学cs231n课程的第一项作业之我的解答。Solution for Assignment1 (Images classification, kNN, SVM, SoftMax, FullyConnected Neural Network)","archived":false,"fork":false,"pushed_at":"2022-12-08T05:29:43.000Z","size":676,"stargazers_count":2,"open_issues_count":14,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2024-12-27T07:44:12.140Z","etag":null,"topics":["assignment1","knn","softmax","softmax-classifier","svm"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/DenryDu.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-06-18T12:54:13.000Z","updated_at":"2023-11-10T09:01:37.000Z","dependencies_parsed_at":"2023-01-24T13:01:21.190Z","dependency_job_id":null,"html_url":"https://github.com/DenryDu/cs231n_Assignment1","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DenryDu%2Fcs231n_Assignment1","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DenryDu%2Fcs231n_Assignment1/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DenryDu%2Fcs231n_Assignment1/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DenryDu%2Fcs231n_Assignment1/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/DenryDu","download_url":"https://codeload.github.com/DenryDu/cs231n_Assignment1/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239427616,"owners_count":19636776,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["assignment1","knn","softmax","softmax-classifier","svm"],"created_at":"2024-11-07T05:12:27.208Z","updated_at":"2025-11-04T07:30:26.956Z","avatar_url":"https://github.com/DenryDu.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Solution and tutorial\n#### Assignment One for class cs231n in Standford\n### 斯坦福cs231n计算机视觉课程作业#1 \n## Table (目录)\n- [1.Instruction of this assignment (课业导引)](#Instruction)\n- [2.Video record of this class (课程视频)](#Video)\n- [3.Tutorial to start (如何安装和开始)](#Tutorial)\n- [4.Solution in Detail (方案详解)](#DetailedSolution)\n\u003ca name = \"Instruction\"\u003e\u003c/a\u003e\n## 1.Instruction of this assignment (课业导引) \n### Goals\nIn this assignment you will practice putting together a simple image classification pipeline based on the k-Nearest Neighbor or the SVM/Softmax classifier. The goals of this assignment are as follows:\n- Understand the basic Image Classification pipeline and the data-driven approach (train/predict stages) (了解 __图像分类__ 和 __数据驱动算法__ 的大致过程)\n- Understand the train/val/test splits and the use of validation data for hyperparameter tuning. (理解 __数据集分割__ 和利用验证集进行 __超参数调优__ )\n- Develop proficiency in writing efficient vectorized code with numpy (利用numpy矩阵化运算 __提升性能__ )\n- Implement and apply a k-Nearest Neighbor (kNN) classifier (实现 __kNN分类器__ )\n- Implement and apply a Multiclass Support Vector Machine (SVM) classifier (实现 __SVM分类器__ )\n- Implement and apply a Softmax classifier (实现 __SoftMax分类器__ ) \n- Implement and apply a Two layer neural network classifier (实现 __两层神经网络分类器__ )\n- Understand the differences and tradeoffs between these classifiers (理解 __各个分类器__ 的 __不同和优劣之处__ )\n- Get a basic understanding of performance improvements from using higher-level representations as opposed to raw pixels, e.g. color histograms, Histogram of Gradient (HOG) features, etc. (对于诸如 __HOG__ 等不同于单位像素的 __高级表达形式__ 对于表现的提升做一个初步的了解)\n\u003ca name = \"Video\"\u003e\u003c/a\u003e\n## 2.Video record of this class (课程视频)\n- [Lecture2: Data-driven approach, kNN, Linear Classification 1](https://www.youtube.com/watch?v=8inugqHkfvE\u0026list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC\u0026index=2)\n- [Lecture3: Linear Classification2, Optimization](https://www.youtube.com/watch?v=qlLChbHhbg4\u0026list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC\u0026index=3)\n- [Lecture4: Backpropagation, Neural Networks 1](https://www.youtube.com/watch?v=i94OvYb6noo\u0026list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC\u0026index=4)\n\u003ca name = \"Tutorial\"\u003e\u003c/a\u003e\n## 3.Tutorial to start (如何安装和开始) \n### How to set up Local Development\n___Download___. Starter code containing jupyter notebooks can be downloaded [here](https://cs231n.github.io/assignments/2020/assignment1_jupyter.zip).       \n___Install Packages___. Once you have the starter code, activate your environment (the one you installed in the Software Setup page) and run (运行脚本安装运行环境,强烈推荐[此教程方法](https://denrydu.github.io/content.html?id=3))\n```\npip install -r requirements.txt.\n```\n如果需要在conda虚拟环境中安装，则运行以下命令(具体见上一段链接):    \n```\npython -m pip install -r requirements.txt.\n```     \n___Download CIFAR-10___. Next, you will need to download the CIFAR-10 dataset. Run the following from the assignment1 directory: (运行脚本下载cifar-10数据集)\n```\ncd cs231n/datasets\n./get_datasets.sh\n```\n___Start Jupyter Server___. After you have the CIFAR-10 data, you should start the Jupyter server from the assignment1 directory by executing jupyter notebook in your terminal.\n```\n```\n\u003ca name = \"DetailedSolution\"\u003e\u003c/a\u003e\n## 4.Solution in Detail (方案详解) \n[Unfinished, please wait...](#)\n***\nDetails about this assignment can be found [on the course webpage](http://cs231n.github.io/), under Assignment #1 of Spring 2019.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdenrydu%2Fcs231n_assignment1","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdenrydu%2Fcs231n_assignment1","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdenrydu%2Fcs231n_assignment1/lists"}