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

        DTS101TC代做、代寫Python語言程序
        DTS101TC代做、代寫Python語言程序

        時間:2025-04-09  來源:合肥網hfw.cc  作者:hfw.cc 我要糾錯



        DTS101TC Coursework
        This coursework is designed to assess your understanding of neural networks and machine learning concepts, as well as your ability to implement, analyze, and evaluate models effectively. It consists of two main components: five assignments and an image object detection project. Detailed instructions, marking criteria, and submission requirements are outlined below. AIGC tools are not allowed.

        Part 1: Assignments (50 Marks)
        This section includes five individual assignments, each focusing on different neural network techniques and datasets. The breakdown for each task includes marks for code execution, analysis, evaluation, and reporting quality.
        Submission Requirements
        Please submit your notebooks to Gradescope. Each assignment must be completed according to the instructions provided in the Python Jupyter Notebook, with all output cells saved alongside the code. You don’t need to write a report for this part. Please put all the analysis and results in your notebook.
        Weekly TA checks during lab sessions and office hours are mandatory. Assignments will not be graded without TA verification.
        Question 1: Digit Recognition with Neural Networks
        Task: Implement a basic neural network using TensorFlow/PyTorch to train a digit recognition model on the MNIST dataset.
        Mark Breakdown:
        oCode execution by Gradescope: 5 marks
        oData and model analysis: 2 marks
        oTest cases: 2 marks
        oReport quality (comments and formatting): 1 mark
        Question 2: Logistic Regression for Flower Classification
        Task: Build and implement a Logistic Regression model to classify three types of iris flowers using the dataset in sklearn.
        Mark Breakdown:
        oCode execution by Gradescope: 5 marks
        oData and model analysis: 2 marks
        oTest cases: 2 marks
        oReport quality (comments and formatting): 1 mark

        Question 3: House Price Prediction with ANN/MLP
        Task: Design and implement an ANN/MLP model to predict house prices in California using the dataset in sklearn.
        Mark Breakdown:
        oCode execution by Gradescope: 5 marks
        oData and model analysis: 2 marks
        oTest cases: 2 marks
        oReport quality (comments and formatting): 1 mark
        Question 4: Stock Price Prediction with RNN
        Task: Create an RNN model to predict stock prices for companies like Apple and Amazon from the Nasdaq market using the provided dataset.
        Mark Breakdown:
        oCode execution by Gradescope: 5 marks
        oData and model analysis: 2 marks
        oModel evaluation: 2 marks
        oReport quality (comments and formatting): 1 mark
        Question 5: Image Classification with CNN
        Task: Develop a CNN model to classify images into 10 classes using the CIFAR-10 dataset.
        Mark Breakdown:
        oCode execution by Gradescope: 5 marks
        oData and model analysis: 2 marks
        oModel evaluation: 2 marks
        oReport quality (comments and formatting): 1 mark

        Part 2: Project (50 Marks)
        The project involves building a custom image dataset and implementing an object detection neural network. This is a comprehensive task that evaluates multiple skills, from data preparation to model evaluation. 
        Submission Requirements
        All of your dataset, code (Python files and ipynb files) should be a package in a single ZIP file, with a PDF of your report (notebook with output cells, analysis, and answers). INCLUDE your dataset in the zip file.
        Step 1: Dataset Creation (10 Marks)
        Task: Collect images and use tools like Label Studio or LabelMe to create labeled datasets for object detection. You can add one more class into the provided dataset. The dataset should have up to 10 classes. Each contains at least 200 images.
        Deliverable: Include the dataset in the ZIP file submission.
        Mark Breakdown:
        oCorrect images and labels: 6 marks
        oData collection and labeling process explanation: 2 marks
        oDataset information summary: 2 marks
        Step 2: Data Loading and Exploration (10 Marks)
        Task: Organize data into train, validation, and test sets. Display dataset statistics, such as class distributions, image shapes, and random samples with labels. Randomly plot 5 images in the training set with their corresponding labels.
        Mark Breakdown:
        oCorrect dataset splitting: 6 marks
        oDataset statistics: 2 marks
        oSample images and labels visualization: 2 marks
        Step 3: Model Implementation (10 Marks)
        Task: Implement an object detection model, such as YOLOv8. Include a calculation of the total number of parameters in your model. You must include calculation details.
        Mark Breakdown:
        oCode and comments: 6 marks
        oParameter calculation details and result: 4 marks
        Step 4: Model Training (10 Marks)
        Task: Train the model using appropriate hyperparameters (e.g., epoch number, optimizer, learning rate). Visualize training and validation performance through graphs of loss and accuracy.
        Mark Breakdown:
        oCode and comments: 6 marks
        oHyperparameters analysis: 2 marks
        oPerformance analysis: 2 marks
        Step 5: Model Evaluation and Testing (10 Marks)
        Task: Evaluate the model on the test set, displaying predictions (visual result) and calculating metrics like mean Average Precision (mAP) and a confusion matrix.
        Mark Breakdown:
        oCode and comments: 6 marks
        oPrediction results: 2 marks
        oEvaluation metrics: 2 marks
        Submission Guidelines
        1.Assignments: Submit your Jupyter Notebooks via Gradescope. Ensure all output cells are saved and visible.
        2.Project: Submit your ZIP file containing the dataset, Python files, Jupyter Notebooks, and a PDF report via Learning Mall Core.
        General Notes and Policies
        1.Plagiarism: Submissions must be your own work. Avoid copying from external sources without proper attribution. Sharing code is prohibited.
        2.Late Submissions: Follow the university's policy on late submissions; penalties may apply.
        3.Support: Utilize lab sessions and TA office hours for guidance.

        Marking Criteria
        Assignments
        Code execution by Gradescope: 5 marks
        Data and model analysis: 2 marks
        Test cases or model evaluation: 2 marks
        Report quality (comments and formatting): 1 mark
        Project
        Code (60%):
        oFully functional code with clear layout and comments: 6 marks
        oPartially functional code with some outputs: 4 marks
        oCode that partially implements the solution but does not produce outcomes: 2 marks
        oIncomplete or non-functional code: 0 marks
        Analysis (40%):
        oComplete and accurate answers with clear understanding: 4 marks
        oPartial answers showing some understanding: 2 marks
        oLimited understanding or incorrect answers:: 0 marks

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

        掃一掃在手機打開當前頁
      1. 上一篇:代寫AI3013編程、代做Python設計程序
      2. 下一篇:代寫MEC 302、代做python編程設計
      3. 無相關信息
        合肥生活資訊

        合肥圖文信息
        急尋熱仿真分析?代做熱仿真服務+熱設計優化
        急尋熱仿真分析?代做熱仿真服務+熱設計優化
        出評 開團工具
        出評 開團工具
        挖掘機濾芯提升發動機性能
        挖掘機濾芯提升發動機性能
        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
        合肥機場巴士1號線
        合肥機場巴士1號線
      4. 短信驗證碼 酒店vi設計 NBA直播 幣安下載

        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 © 2025 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: 久久精品人妻一区二区三区| 无码少妇一区二区浪潮免费| 国产精品高清一区二区人妖| 人妻体内射精一区二区三区| 国产一区二区三区精品视频| 国产在线精品一区二区| 亚洲AV综合色区无码一区爱AV| 亚洲AV午夜福利精品一区二区| 一区二区视频在线免费观看| 丰满岳乱妇一区二区三区| 亚洲视频在线一区二区| 久久se精品一区二区| 国内精品视频一区二区三区| 一区二区三区福利视频| 精品乱子伦一区二区三区高清免费播放| 3d动漫精品成人一区二区三| 91一区二区三区| 熟女少妇丰满一区二区| 精品无码一区二区三区在线| 一区二区三区视频免费| 亚洲AV无码一区二区乱子仑| 日本精品高清一区二区| 国产一区中文字幕在线观看| 久久精品国产第一区二区| 精品亚洲av无码一区二区柚蜜| 国产人妖在线观看一区二区| 波多野结衣精品一区二区三区 | 久久久久久人妻一区精品| 午夜福利一区二区三区在线观看 | 中文字幕一区二区三| 老熟女五十路乱子交尾中出一区| 国产成人综合一区精品| 一区精品麻豆入口| 久久精品国产AV一区二区三区| 精品一区二区三区无码视频| 精品性影院一区二区三区内射| 高清一区二区三区视频| 精品少妇ay一区二区三区| 中文字幕无线码一区| 亚洲AV一区二区三区四区| 国产一区二区福利|