Objective: The goal of this assignment is to implement a 2-layer NN for image classification using the CIFAR-10 dataset. You will: 1. Utilize a built-in packages or libraries such as Pytorch and Tensorflow to perform forward and backward computations (Optional, not graded) 2. Implement the algorithm, such as forward and backward computations independently (required). Tasks and Requirements: 1. Algorithm Implementation: . Employ the 2- layer NN classifier for image classification on the CIFAR-10 dataset using a self-coded version. 2. Performance Improvement Strategies: · Analyze how to improve the performance of your implementations, including and not limited to: tuning hyperparameters such as number of nodes in hidden layer, regularization terms, strength of regularization, etc. Explore by yourself! 3. Comprehensive Report: · Prepare a detailed report encompassing the following sections: . Background and Method Introduction: Provide an overview of the 2-layer NN and its application in image classification. - Dataset and Tasks Description: Describe the CIFAR-10 dataset and outline the specific classification tasks undertaken. . Algorithms Used: Elaborate on the implementation details of the algorithm. Attach screenshot of the codes whenever necessary. . Results: Present and discuss the classification results obtained. . Methods of Improvements: Discuss the strategies employed to enhance the performance of your algorithm, focusing on hyper-parameter tuning 4. Submission Format: . Submit your work in the form of Jupyter Notebook (.ipynb) and HTML files, along with the final report. Submit each file separately. Grading Criteria: · Implementation of the Algorithm with regularization (40%): · Algorithm Improvement (40%): Thoughtful considerations and implementations for validating and improving your algorithm, including techniques like hyper-parameter tuning, and efficient coding practices. . Report Quality (20%): Overall quality, clarity, organization, and thoroughness of the submitted report.

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