Project 1: Implement Logistic Regression with Gradient Descent BackpropagationIntroductionIn Tutorial 1, we got start with Python and TensorFlow basics, and in LAB 1, we are going to have hands-on practices on Pyhton and TensorFlow. To consolidate the theoretical knowledge given in the lecture, in Project 1, we will implement the logistic regression with gradient descent backpropagation. Project 1 is an individ-ual student project that student should work independently. In this project, basic programming skills are required, otherwise you may have di culties in nishing it.Logistic Regression1. Key Points RecapLogistic regression is the linear classi cation algorithm for binary classi cation problems, which can be expressed very much like linear regression. Input values,x= x1; x2; , are combined linearly with parameters, = 1; 2; , to predict an output value, y. A key di erence from linear regression is that the output value being modeled is a binary value (0 or 1) rather than a numeric value.The logistic function is de ned as,We define the cost function as

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