Objective: The goal of this assignment is to implement the linear classifier algorithm for image classification using the CIFAR$-10$ dataset. You will:

Utilize a built$-$in packages or libraries such as Pytorch and Tensorflow to perform forward and backward computations $($Optional$,$ not graded$)$

Implement the algorithm, such as forward and backward computations independently $($required$).$

Tasks and Requirements:

Algorithm Implementation:

Employ the linear classifier for image classification on the CIFAR$-10$ dataset using a self$-$coded version.

Performance Improvement Strategies:

Analyze how to improve the performance of your implementations, including and not limited to: tuning hyperparameters such as training epoch, step size $($learning rate$),$ regularization terms, strength of regularization, etc. Explore by yourslef!

Comprehensive Report:

Prepare a detailed report encompassing the following sections:

Background and Method Introduction: Provide an overview of the linear classifier 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 accuracy and speed.